Advances in Child Development and Behavior Volume 17

ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR Volume 17 Contributors to This Volume Jeffrey Bisanz Pamela Blewitt Rober...

Author: Lewis Paeff Lipsitt | Charles C. Spiker

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ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR

Volume 17

Contributors to This Volume Jeffrey Bisanz Pamela Blewitt Robert Kail Stan A. Kuczaj I1 Deanna Kuhn Howard V . Meredith Erin Phelps Alexander W. Siege1 Sheldon H. White


edited by Hayne W. Reese Department of Psychology West Virginia University Morgantown, West Virginia

Volume 17

@

1982

ACADEMIC PRESS A Subsidiary of Harcourt Brace Jovanovich, Publishers

New York London Paris San Diego San Francisco Sao Paulo Sydney Tokyo Toronto

COPYRIGHT @ 1982, BY ACADEMIC PRESS,INC. ALL RIGHTS RESERVED. NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM on BY ANY MEANS, ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT PERMISSION IN WRITING FROM THE PUBLISHER.

ACADEMIC PRESS,INC. 111 Fifth Avenue, New

York. New York 10003

United Kingdom Edition published by ACADEMIC PRESS, INC. (LONDON) LTD. 24/28 Oval Road, London N W I

7DX

LIBRARY OF CONGRESS CATALOG CARD NUMBER:63-23237 ISBN 0-12-009717-6 PRINTED IN THE UNITED STATES OF AMERICA 82 83 84 85

9 8 7 6 5 4 3 2 1

Contents

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..........

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..........

vii

ix

The Development of Problem-Solving Strategies DEANNA KUHN AND ERIN PHELPS I . Introduction and Rationale Underlying the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . I1. Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111. Strategy Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...................

..............................

V . Replication and Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI . Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 4 9

18 32 36 42

Information Processing and Cognitive Development ROBERT KAIL AND JEFFREY BISANZ I . Introduction ................................................... I1. A Generic 1 n-Processing System: Defining the Metaphor . . . . . . . . . . . . . . 111. An Information-Processing Look at Research on Cognitive Development . . . . . . . . . IV . The Issue of Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ V . Additional Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V1. Concluding Remarks . . . . . . . . . . . . . .................................. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 48 52 62 68 75 76

Research between 1950 and 1980 on Urban-Rural Differences in Body Size and Growth Rate of Children and Youths HOWARD V . MEREDITH Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Retrospect: 1870-1915 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Differences in Standing Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Differences in Body Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V . Differences in Chest Girth . . . . . . . . . . . . . . . . . . . . . . V1. Differences in Other Somatic Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII . Summary . . . . . . . . . . .................... References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. I1 . 111. IV .

... .....

83

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85 86

.....

..... ..... ........

105 117

123

.....

130

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134

vi

Contents

Word Meaning Acquisition in Young Children: A Review of Theory and Research PAMELA BLEWI'M Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .............. Approaches to the Study of Early Word Meanings . . . . . . . . . . . . . . . . . . . . Nominal Words ................................... . . . . . . . . . . . . . . . . . ... Relational and Dimensional Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . Discussion: Theoretical Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion: Research Directions ..................... . . . . . . . . . . . . . .. . . . . . References ....................................... ...... ... . . .

i40 141 143 153 175 184 187

Language Play and Language Acquisition STAN A . KUCZAJ I1 I . Introduction ...................................... . . . . . . . . . . . . . .. . . . . . 11. Language Play and Language Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... I11 . Typesof Play ................................................... IV . What Determines the Content of Children's Language Play? . . . . . . . . . . . . . V . Developmental Trends in Language Play ................................... VI . Is Language Play Developmentally Progressive? ............................. Conclusions ........................................................... References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

197 198 199 202 209 210 225 228

The Child Study Movement: Early Growth and Development of the Symbolized Child ALEXANDER W . SIEGEL AND SHELDON H . WHITE I . Introduction: The Child in Texts and Symbols............................... I1 . The Larger Social Context of the Child Study Movement ...................... 111. The Enterprises of the Child Study Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV . Motives and Needs for Child Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References ............................................................

234 238 248 276 280

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

287

Subject Index ...............................................................

297

Contents of Previous Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

30 1

I. I1 . 111 . IV . V. VI .

Contributors Numbers in parentheses indicate the pages on which the authors' contributions begin.

JEFFREY BISANZ Psychology Department, University of Alberta, Edmonton, Alberta T6G 2E9 Canada (45) PAMELA BLEWITT Department of Psychology, Villanova University, Villanova, Pennsylvania 19085 (139) ROBERT KAIL Department of Psychology Sciences, Purdur University, West Lafayette, Indiana 47907 (45) STAN A. KUCZAJ I1 Department of Psychology, Southern Methodist University, Dallas, Texas 75275 (197) DEANNA KUHN Laboratory of Human Development, Graduate School of Education, Harvard University, Cambridge, Massachusetts 02 138' ( I ) HOWARD V. MEREDITH Blatt Physical Education Center, University of South Carolina, Columbia, South Carolina 29208 (83) ERIN PHELPS Laborutory of Human Development, Harvard University, Cambridge, Massachusetts 021382 ( I ) ALEXANDER W. SIEGEL Department of Psychology, University of Houston, Houston, Texas 77004 (233) SHELDON H. WHITE Department of Psychology and Social Relations, Harvard University, Cambridge, Massachusetts 02138 (233)

'Present address: Program in Developmental Psychology, Teachers College, Columbia University, New York, New York 10027 2Present address: Murray Research Center, Radcliffe College. Cambridge, Massachusetts 021 38 vii

This Page Intentionally Left Blank

Preface The amount of research and theoretical discussion in the field of child development and behavior is so vast that researchers, instructors, and students are confronted with a formidable task: They must not only keep abreast of new developments within their areas of specialization through the use of primary sources, but they must also be knowledgeable in areas peripheral to their primary focus of interest. Moreover, journal space is often simply too limited to permit publication of more speculative kinds of analyses that may spark expanded interest in a problem area or stimulate new modes of attack on a problem. The serial publication, Advances in Child Development and Behavior, is intended to ease the burden by providing scholarly technical articles that serve as reference material and by being a forum for scholarly speculation. In these documented critical reviews, recent advances in the field are summarized and integrated; complexities are exposed; and fresh viewpoints are offered. They should be useful not only to the expert in the area but also to the general reader. No attempt is made to organize each volume around a particular theme or topic, nor is the series intended to reflect the development of new fads. Manuscripts are solicited from investigators conducting programmatic work on problems of current and significant interest. The editor often encourages the preparation of critical syntheses dealing intensively with topics of relatively narrow scope but of considerable potential interest to the scientific community. Contributors are encouraged to criticize, to integrate, and to stimulate, but always within a framework of high scholarship. Although publication in the volumes is ordinarily by invitation, unsolicited manuscripts will be accepted for review if submitted first in outline form to the editor. All papers-whether invited or submitted-receive careful editorial scrutiny. Invited papers are automatically accepted for publication in principle, but they may require revision before final acceptance. Submitted papers receive the same treatment except that they are not automatically accepted for publication even in principle and may be rejected. The use of sexist language, such as “he” or “she” as the general singular pronoun, in contributions to the Advances series is strongly discouraged. The use of “he or she” (or the like) is acceptable; it is widespread and no longer seems cumbersome or self-conscious. The Advances series is usually not suitable for reports of a single study or a short series of studies, even if the report is necessarily long because of the nature of the research. However, an exception has been made in the present volume by inclusion of the paper by Kuhn and Phelps. Although a single study (with ix

X

Preface

replication and variation) is reported, the method used is sufficiently novel and promising to qualify as a real advance. I wish to acknowledge with gratitude the aid of my home institution, West Virginia University, which generously provided time and facilities for the preparation of this volume. I also wish to thank Drs. Lewis P. Lipsitt and John Money for their editorial assistance. Hayne W. Reese


Volume 17


THEDEVELOPMENTOF PROBLEM-SOLVING STRATEGIES

Deanna Kuhn LABORATORY OF HUMAN DEVELOPMENT GRADUATE SCHOOL OF EDUCATION HARVARD UNIVERSITY CAMBRIDGE, MASSACHUSETTS

Erin Phelps LABORATORY OF HUMAN DEVELOPMENT GRADUATE SCHOOL OF EDUCATION HARVARD UNIVERSITY CAMBRIDGE, MASSACHUSETTS

I . INTRODUCTION AND RATIONALE UNDERLYING THE METHOD 11. METHOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. PROBLEM SELECTION., . . . . . , . . . . , , . , . . . . . . . . . . . . . . . . . . . . . . . . . . . .

B. INITIAL SUBJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111. STRATEGY ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

A. B. C. D.

INTRODUCTION .........................-........................ HYPOTHESIS STRATEGIES. . . . . , . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EXPERIMENTATION STRATEGIES . . . . . . , . . . . . , . . , . , . . . , . . . . . . . . . . . INFERENCE STRATEGIES . . . . . . , , . , . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . .

IV . RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. THE POWER AND PERSISTENCE OF INVALID STRATEGIES.. . . . . . . . . B. PATTERNS OF CHANGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. PREDICTION OF CHANGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. RECURRENCE OF INVALID STRATEGIES. . . . . . . . . . . . . . . . . . . . . . . . . . . V

VI

2 4 4 7 1 9 9 12 14 15

18 18 24 29 31

REPLlCATlON AND VARIATIONS.

32

DISCUSSION AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. THE METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. THE FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36 37 39 42

ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR, VOL. 17

Copyright 0 1982 by Academic Press. Inc. All rights of reproduction in any form reserved. ISBN 0-12-009717-6

Deanna Kuhn and Erin Phelps

2

I. Introduction and Rationale Underlying the Method The object of the research described in this article is to study the process of development, While research having this objective would seemingly be a central focus of developmental psychology, the study of developmental process is problematic and such studies are in fact few in number. In this article, we describe a method designed to permit the study of developmental process and present an initial and a replication study that illustrate the kinds of data and insights this method yields. The study of development poses a paradox not unlike the paradox posed by the measurement of intelligence by IQ tests. If one accepts one of the customary definitions of intelligence as “ability to learn” or “ability to profit from experience,” or some such variant, one must acknowledge the fact that no one on an IQ test is ever asked to learn anything-“ability to learn” is inferred in an indirect fashion from performance on the test. Similarly, those who would study development come up against the fact that they are unlikely ever to observe development taking place. At best, researchers employ a longitudinal method in which they observe the subject’s state at t , , observe the subject’s state at t,, and then undertake to make inferences about an underlying process of development that may have occurred between t , and t,. Researchers attempting to understand the process or mechanisms of development characteristically turn to some form of experimental method. Even this method, however, fails to involve observation of the process itself. The design of the experimental intervention study (or “training” study) in developmental psychology is by now well established: Subjects’ pretest behavior is assessed, a treatment is administered to a portion of subjects, and subjects’ behavior is reassessed at one or more posttests. The only legitimate data allowed by the research design come from the performance of subjects on these pre- and posttests. Moreover, even when the intervention study is “successful,” that is, the treatment proves sufficient to produce a change in behavior, this demonstration of sufficiency falls far short of a confirmation that the emergence of the behavior in question during the natural course of development is always, or ever, contingent on events like those that compose the researcher’s treatment. McCall (1977) has characterized the problem as the distinction between “can” versus “does. Even under the best of circumstances, then, the investigators remain unsure of the extent to which their treatment plays a role in the natural development of the behavior they are studying. The aim of our work has been to try to get around this “intervention study impasse” by developing an approach that would allow us to come as close as possible to studying the process of development directly, as opposed to making inferences about it based on indirect evidence. Such direct empirical data about ”

The Development qf Problem-Solving Strategies

3

developmental process, we would contend, have been largely absent and are sorely needed. How, then, might one go about observing developmental process? Ideally, perhaps, one would go out into the real world and observe the process taking place within those natural contexts in which it actually occurs. The limitations of this research strategy are also familiar: The phenomena of interest take place over a protracted period of time in an extremely complex, multivariable environment, making it virtually impossible to identify causal relationships or critical sequences of events. These limitations suggest the need for a compromise in the classic choice between external validity and experimental control. In our case, this compromise has meant the following. On the side of external validity, the approach, we have felt, must remain essentially observational and descriptive. This observation, moreover, must be extended over a period of time, in order to capture the process that is the object of interest. Two important constraints, however, are placed on the observation. First, a limitation is placed on the range of situations in which the subject is to be observed, so that categorization of types of behavior that are to be the object of observation becomes a manageable task. Second, the process to be observed must to some extent be condensed in time, without altering its essential characteristics, so as to facilitate observation of it. The subjects chosen for observation in the studies to be described in this article are those who are approaching the age when the particular cognitive strategies we focus on have been observed to emerge. Subjects are observed over a period of several months, during which time they are given frequent opportunities to engage in problem-solving activities that lead them to exercise existing cognitive strategies. The only feedback subjects receive during their activities is the feedback that comes from their own actions on the physical materials. The approach rests on the premise that exercise of existing strategies in at least some cases will be sufficient to lead a subject to modify those strategies. Put in the simplest terms, the approach we have employed involves the observation of a subject engaged in repeated encounters with a problem. Conceivably, our interest might have been in gauging the reliability of subjects’ performance, that is, the extent to which a subject displayed consistent behavior on successive occasions, and hence, perhaps, the value of the problem as an assessment instrument. To the contrary, however, we anticipated that at least some subjects would modify their problem-solving strategies during the course of repeated encounters with the problem, and it was this process of change that we wished to observe. Aside from some very early research pertaining to ski11 acquisition (e.g., Book, 1908), and two recent studies (Anzai & Simon, 1979; Lawler, 1981) to which we shall make reference later, psychologists of learning or development have not commonly utilized this seemingly straightforward method of observing an individual acquiring new strategies or modifying old ones in the course of

4


repeated encounters with a task or activity. Yet it would appear to be just such a process of repeated encounters through which such acquisition normally occurs. Clearly, numerous further issues might be raised regarding the method. To what extent can a natural change process be condensed in time by increasing density of exercise (of existing cognitive strategies brought to bear on the problem) beyond its ordinary level? Is the process of change that is observed one of learning or of development? Does presentation of the problem itself constitute an “intervention,” in the sense in which that term is customarily used? The most fruitful approach, we propose, will be to consider issues such as these following more detailed presentation of how we have employed this method and the sorts of results it yields. For the time being, we therefore limit our presentation to the preceding straightforward, though arguably oversimplified, rationale underlying our approach.

11. Method A.

PROBLEM SELECTION

If observation is to be limited to a single problem-solving situation, considerable care ought to go into selection of that situation. Our desire in the present work was to study the development of problem-solving strategies. For the most part, problem-solving strategies fall into the category of the sorts of cognitive strategies that are not normally the object of direct instruction. Presumably, such strategies develop as the result of some sort of indirect or broad, general experience, and this is a process that developmentalists are particularly interested to understand. Within the broad category of problem-solving strategies, we devoted considerable thought to selection of a particular problem-solving situation. Two major (and a number of lesser) criteria governed this selection. First, we desired a situation that would involve those activities generally associated with problem solving, in particular hypothesis generation and hypothesis testing. Second, we believed that the problem should bear a significant resemblance to problems persons encounter in their everyday experience. In addition to whatever intrinsic merit this latter criterion might be regarded as having, we regarded it as critical from the standpoint of the methodological considerations addressed in the preceding section: The cognitive strategies subjects employ in this situation should be those they would have occasion to employ in the course of their own experience, though perhaps in a less frequent, or dense, as well as less explicit manner. The research situation, in other words, should not lead them to do anything radically different, cognitively, from what they might do ordinarily.

The Development of Problem-Solving Strategies

5

The problem we chose is one of causal attribution, more specifically the identification of cause-and-effect relationships that are embedded in multivariable contexts. A number of antecedent events occur in conjunction with an outcome, and the subject’s task is to determine what causes the outcome. The problem involves a form of causal inference that we would contend is common in everyday reasoning. The form of problem we employed has not been the object of previous study, but two current lines of research in the field of cognitive development provide contexts in which it might be viewed. One is the research on casual reasoning by Shultz, Siegler, and others (Bindra, Clarke, & Shultz, 1980; Shaklee & Mims, 1981; Shaklee & Tucker, 1980; Shultz & Butkowsky, 1977; Shultz, Butkowsky, Pearce, & Shanfield, 1975; Shultz & Mendelson, 1975; Siegler, 1975, 1976; Siegler & Liebert, 1974). This work has dealt with the pattern of associations between antecedent and outcome that subjects of various ages accept as evidence of a necessary or sufficient causal relation. Only in a peripheral way has it dealt with the subject’s disembedding of a causal relation embedded in a multivariable context. Nor has this research dealt with subjects’ ability to conduct their own investigations to determine whether a causal relationship is present. The other line of research is Piagetian. Rather than Piaget’s work on causality (Piaget & Garcia, 1974), which deals with causal mechanism rather than causal attribution, however, it is the research on what has been labeled “isolation of variables” by Inhelder and Piaget (1958) that is related to the present work. In contrast to the work on causal reasoning referred to in the preceding paragraph, the isolation-of-variables research has been focused on the strategies subjects use in investigating whether one variable is causally related to another. In particular it has been focused on whether they employ an “all other things equal” strategy in their investigations. Less attention has been devoted, however, to the causal inferences that follow application of the investigative strategy. Nor, with a few exceptions (Kuhn & Brannock, 1977; Tschirgi, 1980), have subjects been presented sets of events in which causal relationships are embedded in multivariable contexts. We believed this latter characteristic to be the most important in terms of the problem’s external validity, that is, its resemblance to the sort of problemsolving situations individuals encounter in their everyday lives. This multivariable context, we would argue, is the one in which people typically encounter and make inferences about causal relations. Having settled on the form of the problem, that is, the identification of causeand-effect relationships embedded in multivariable contexts, we proceeded to choose its content. This choice was preceded by a good deal of pilot work and deliberation. It was a difficult choice in large part because of some conflicting objectives. Given the concerns with external validity just raised, we preferred that the content, as well as the form, of the problem come from subjects’

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everyday lives. As we experimented with different kinds of problems, however, a second objective became evident. Subjects were to be asked to experiment with the problem materials to determine for themselves what causal relations were present. It is highly desirable that these relations be easily producible by the subjects themselves while working with the material, as opposed to having them externally supplied via the authority of the experimenter. Most everyday problem contexts, for example, the baking of cakes studied by Tschirgi (1980) or the care of plants in our own earlier work (Kuhn & Brannock, 1977), cannot be represented in an experimental situation unless the experimenter artificially supplies the outcome, for example, tells subjects the cake came out good or bad, or, at best, shows subjects the sick or healthy plants. Subjects do not have the opportunity to confirm for themselves that the antecedent variables indeed produce the outcomes the experimenter alleges. This characteristic was missing in our initial attempts to employ the methodology described in this article (Kuhn & Angelev, 1976). We ultimately decided that this latter characteristic is the most critical one and therefore settled on a problem content in which effects could be produced directly by the subject. The content involves the production of chemical reactions. Though this particular content is not ordinarily a part of subjects’ everyday lives, our pilot work indicated that it had sufficient interest value that subjects quickly became comfortable with the task and remained absorbed in carrying it out over several successive sessions. We therefore predicted that the problem would sustain subjects’ interest during the several-month period that the study was to entail. This prediction turned out to be warranted, at least for the preadolescent samples who were the focus of the studies to be described in this article. Problems involving chemical reactions were originally studied by Inhelder and Piaget (1958), who used one such problem to study subjects’ ability to generate systematic combinations. More recently, Pitt ( 1976) has used chemical reaction problems to investigate both combinatorial construction and subjects’ ability to engage in a more sophisticated form of problem solving akin to the qualitative analysis actually performed in chemistry. Chemical reaction problems are utilized in the present work in a somewhat different manner. With the exception of some advanced problems presented to only a minority of subjects, in all of the problems only a single chemical, of the three identified as present in a demonstrated mixture, was responsible for producing a chemical reaction (when the special “mixing liquid” was added to the mixture). The task objective, therefore, was not one of producing systematic combinations of elements so as to discover how to produce the reaction. Rather, the subject’s problem was to isolate which of the elements that were present were in fact causally related to the outcome. The problem is thus a very simple one of disembedding a causal relation from its context: To solve it, one need do no more than try each of the


I

elements in the outcome-producing combination in isolation, to assess its individual effect. As we shall see, however, for the preadolescent subjects in our samples the problem was in fact not at all a simple one, and mastery typically was achieved only slowly and with difficulty. B . INITIAL SUBJECTS

Subjects in our first sample were fourth- and fifth-graders. Our pilot work indicated that this is the age level at which subjects first begin to show some use of isolation as a solution strategy. Our objective, therefore, was to select subjects in this chronological age range who did not yet exhibit an isolation strategy, so as to be able to observe the manner in which it might develop with repeated exercise of the existing, less advanced strategies these subjects did use. Kuhn and Brannock’s (1977) plant problem was used as a screening device to identify subjects who would be likely to fulfill the criterion just indicated. Those subjects who scored at level 0 in the Kuhn and Brannock (1977) scoring system (approximately half of the subjects tested) were presented the initial chemicals problem. None of them used an isolation strategy. Fifteen such subjects were randomly selected for inclusion in the present sample. Chronological age ranged from 9:9 to 11:2. None of the subjects had any recognized learning disabilities or other exceptional characteristics. All were reported by their classroom teachers to be within an average range academically. The school was a public one in a middle-class suburban neighborhood. C . PROCEDURE

The problem-solving sessions took place once each week for a total of I I weeks. A 1-week school vacation extended the total period of observation to 12 weeks. At each session the subject came to the workroom, which contained a large table with a supply of glassware and chemicals. The materials consisted of(a) a large supply of colorless, odorless liquids in 2- to 3-dram snap-top vials, each labeled with a letter (B, C, D, E, or F); ( b )a large reagent bottle (labeled A) referred to by the interviewer as “mixing 1iquid”;and (c) an assortment of 50- to 100-ml glass beakers, for mixing. In the initial problem, one of the liquids was sufficient with the addition of the mixing liquid to produce a chemical reaction, either a color change or a precipitate. To demonstrate the reaction, the interviewer selected a vial each of B, C, and D and emptied them into a beaker. She placed the empty labeled vials adjacent to the beaker, so as to identify the components of the mixture. She then

8


selected vials of D, E, and F and likewise emptied them into a beaker, leaving the empty labeled vials adjacent. She then added mixing liquid to both beakers, and the subject observed that the first mixture turned red (or cloudy) while the second mixture remained colorless. The subject was asked the following questions: ( I ) What do you think makes a difference in whether or not it turns red (cloudy)? (2) How do you know? (3) Can you be sure what makes a difference? Why/why not? (4) (If subject indicates only certain elements as effective) Do the others have anything to do with it? Which ones? How do you know? The subject was then asked, “Are there any other ways of doing it you’d like to try to find out for sure what makes a difference?” Subjects were encouraged to plan as many experiments as they wished, by setting up the appropriate vials next to a beaker. After subjects indicated they had set up as many “ways of doing it” as desired, the following questions were asked, before the actual mixing began: (5) What do you think you will find out by trying it these ways? (6) How do you think it’s going to turn out? Why? The interviewer then assisted the subject in carrying out the mixing if necessary, following which she asked: (7) What do you think about how it’s turned out? (8) What have you found out? Questions 1-4 were then repeated. Thus, while the interviewer asked questions that encouraged the subject to analyze and interpret what was taking place, no solutions to the problem or strategies for obtaining a solution were suggested. Nor were subjects given any reinforcement for the strategies they did employ or for any subsequent modifications in these strategies. The only feedback subjects received came from their own actions on the physical materials. At the second session the interviewer explained that the liquids in the vials were not necessarily the same ones that had been there the previous week, and therefore the subject could not be sure the results would be the same. The procedure for each of the 11 sessions was identical to that described above. Only the effective element and the form of the reaction (color change or precipitate) were varied. Subjects were regarded as having mastered this initial problem when they specified the single effective element as causally related to the outcome and excluded all other elements as “having nothing to do with” the outcome. When this mastery occurred, a more advanced problem, in which either of two elements produced the outcome, was presented at the next session. If mastery of this problem was achieved, a third problem was presented in which any of three single elements produced the outcome. Those subjects who mastered the third problem (4 of the 15) went on to a set of more advanced problems in which combinations of two and then three elements were necessary to produce the outcome. The subject’s own performance thus determined the rate of progress through the sequence of problems.


9

111. Strategy Analysis A.

INTRODUCTION

As a prerequisite to examining patterns of change over the series of problemsolving sessions, problem-solving strategies that subjects apply to this problem were identified. The scheme summarized in Table I was based on intensive analysis of roughly half of the 165 individual session protocols. The completed analytic scheme was then applied to the remaining protocols and proved to be exhaustive, that is, no new strategies were observed in the second set of protocols. A second rater was familiarized with the coding scheme and independently coded the entire set of protocols. As shown in Table I, the strategies observed fell into three major categories, hypothesis strategies (HO-H4), experimentation strategies (EO-E5), and inference strategies (10-18), reflecting the three major phases or components of the problem-solving processes. (For ease in interpretation, the examples in Table I have been altered as necessary to reflect the identical problem situation in which BCD is observed to produce the reaction, DEF is observed not to produce it, and B is the effective element.) Within a single session, a subject’s hypothesis strategies were typically of only a single type. The same was true of experimentation strategies, although in both cases there were occasional instances of sessions containing multiple types. In contrast, a subject typically applied more than one type of inference strategy within a single session; the average was between two and three. Percentage agreement between raters was 90% for hypothesis strategies, 77% for experimentation strategies, and 87% for inference strategies. Disagreements were resolved through discussion. The major single source of unreliability was the differentiation between strategy E2 and strategies EO or E l . In this case, unlike all the others, a number of intermediate cases clearly existed, and the coding was based on which strategy type appeared dominant. The scheme in Table I covers only the initial three problems, those in which single elements are sufficient to produce the effect. The strategy sequence H4-E5-18 represents the optimal solution to these problems. The more advanced problems involve additional strategies, for example, hypotheses of interaction effects and experimentation strategies involving systematic combination of elements. Only four subjects in the present sample reached these more advanced problems. (Fourteen of the 165 sessions involved the advanced problems.) Accordingly, performance on these problems will be covered only briefly in an anecdotal manner. As one might anticipate, characteristic “paths” existed across the three strategy categories, that is, frequent patterns of a particular hypothesis strategy, experimentation strategy, and inference strategy occurring in conjunction with

TABLE I Strategy Analysis Hypothesis

Experimentation

Inference

Pseudohypothesis Pseudoexperimentation Invalid inference HO. Absence of anticipatory reasonEO. Testing of specific mixtures with no a p 10. Inference based on extraneous parent systematization in the mixtures ing. In response to questioning ple: “It’s B and C because they’re chosen and no single-element mixtures (e.g., “What do you think you will alphabet.’’ fmd out?”), subject replies either E l . Testing of specific mixtures with no ap11. Inference based on alleged actions of chemicals but with “I don’t know” or gives a response logical inconsistency, i.e., an individual element or a particparent systematization in the mixtures such as “Different things,” with chosen but including some single elements ular mixture of elements is not assumed to have a consistent inability to elaborate. effect. Example: “D helped this one [mixture] stay clear and E2. Replication of the demonstrated successful HI. An anticipatory statement focused (i.e., effect-producing) and/or unsuccessful D made this one cloudy.” on obtaining the outcome rather mixtures with minor variations 12. Inference of consistent effects, but at the level of mixtures than explaining what caused it. Exrather than individual elements. It is asserted that the ample: “I’m hying to make them effective mixtures are all those in which an effect occurred. turn pink.” Example: “BC or BF are the right ones to use because when H2. An anticipatory statement focused I tried those I got red.” on obtaining information, with in13. Inference of false inclusion. Inference is at the level of ability to elaborate. Example: “I individual elements; an element’s presence in an effectwant to see which ones [mixtures] producing mixture, however, is suficient for the inference turn pink.” that it played a role in the outcome. Examples: “It’s B H3. Anticipation regarding one or because it was in the one that tumed red”; “It’s C and D more specific mixtures. Examples: because the one that had them got cloudy.” (Strategy is “BC will turn cloudy”; ‘‘I want to labeled 13a if data are present that contradict the inference, see if CD turns pink.” otherwise I3b.)

Valid but insufficient inference 14. Exclusion of aq element that appears in an unsuccessful mixture. Example: “D has nothing to do with it because DEF stayed clear.” 15. Exclusion of an element that cooccurs with both outcomes. Example: “It can’t be D because D was in both of them” i.e., in both mixtures, one which turned cloudy and the other which didn’t. Genuine experimentation-sufficient but Valid, sufficient, but inefficient inference Genuine hypothesis inefficient E3. Experiment conducted for the purpose of 16. Inclusion (I6a) of a single element as cause of the effect, H4.An anticipatory statement regardbased on a consistent correspondence between its presence assessing the effects of one or more indiing the effects of individual eleor absence and presence or absence of the outcome, where vidual elements but with superfluous elements. Examples: “It could be all alternatives can be logically excluded, i.e., no other ments included in one or more mixtures. either B or C that’s making it red”; consistent element-outcome correspondences exist. (ExcluExample: The mixtures BDE and CDE are “I want to see if D has anything to sion of an element based on the lack of such correspondence generated to assess “whether it’s B or C.” do with it.” is labeled 16b.) Example: “It must be B because every time E4. Experiment consisting of systematic incluwe had B in it, it turned pink.” sion or exclusion of a single element from a mixture, for the purpose of assessing its 17. Inclusion (I7a) or exclusion (17b) of an element by means effect. Example: The mixtures BCD and CD of a specific comparison between two mixtures that are identical except that one includes the element and the other are generated and compared. to assess the does not. Examples: “It must be B because I got red with effect of B. BCD but not with CD”; “It’s not C because BD still turns when you leave out the C . ” Genuine experimentation-sufficient and efficient Valid, sufficient. and efficient inference 18. Inclusion or exclusion of an element based on its effect in E5. Experiment to assess the effects of one or isolation from other elements. Examples: “It’s B because it more individual elements by means of exturned cloudy when we tried it by itself”; *‘It’s not C perimental isolation, i.e., each element to because C alone didn’t do anything.” be assessed is examined individually for its effect.

12


one another. An obvious question, then, is whether it is warranted to define strategies separately within each of the three categories, as opposed to identifying ‘‘macrostrategies” that encompass all three categories. Our justification for retaining the three distinct categories is simply that with a few specific exceptions, all possible combinations of strategies across the three categories occurred. For example, strategy E4 and strategy I7 bear an obvious logical relation to one another and tended to occur together (see Table I); yet, as will be illustrated later, each occurred on some occasions in the absence of the other. The rest of this section is devoted to some explanatory comments regarding the strategies themselves. 8 . HYPOTHESIS STRATEGIES

Hypothesis strategies are summarized in the first column of Table I. Hypothesis strategies were often expressed spontaneously, but some hypothesis strategy was always coded on the basis of the subject’s response to the interviewer’s queries at the time the experimental investigation was planned (Q5 + Q6), which were asked unless the subject spontaneously expressed what would have been responses to these questions. As shown in the final column of Table 11, which summarizes usage frequencies of hypothesis and experimentation strategies over the 151 sessions (between- and within-subject data combined), subjects most often employed a single type of hypothesis strategy, even though that strategy may have been employed repeatedly at different points in the session. Subjects using strategies HO through H3 gave no indication of recognizing the possibility that it might be a single element that was responsible for the effect. HO reflects a total absence of anticipatory reasoning. Subjects using H1 were engaged in anticipatory thought regarding the outcome of their experiments in the sense that they saw their objective as getting as many mixtures as possible to show the reaction. This conception was consistent with their behavior following the mixing. The experiment was a success, that is, “came out good,” to the extent that a majority of the mixtures showed the reaction. No attention was focused on identifying the causes responsible for the reaction. H2 reflects a shift in focus toward obtaining information from the outcome of the experiments, rather than merely achieving the desired outcome. This anticipation was very diffuse, however; the subject was unable to predict any specific outcomes or anticipate what information was likely to be gained. H3, in contrast, tended to be very specific and conceivably might be regarded as genuine hypothesis. Subjects using it, however, tended to make isolated predictions, rather than using the strategy to predict the outcomes of the entire set of experiments they had constructed (as subjects usually did who utilized H4).Anticipations were always regarding mixtures, rather than single elements, and the subject showed no evidence of recognizing that a single element might be responsible for the effect.

TABLE I1 Hypothesis and Experimentation Strategy Frequencies Experimentation strategies Hypothesis strategies

w

HO HI H2 H3 H4 H4+ lower*

Other mixed

0 0 0 0 28 2

0 0

0

1 0

0 0 6 0

0 0 0 5 1

30

6

6

208

4%

4%

E2

E3

E4

E5

3

0 1 0

0 0

0

1

2 7 5 25 7 2

5 3%

48 32%

5 9 6 8

2 1

32 21% ~~~

E2 i higher"

El

1

Total % of all sessions

E3 + E5

EO

~

OE2 in combination with E3, E4, or E5. bH4 in combination with H3, H2, H1, or HO

0 0 10

0 10 7%

0 0 0 9 0 9 6%

0 0

4 0 5 3%

6 of all Total

sessions

6 9 10 36 76 14

4% 6% 7% 24% 50% 9%

151

100%

14


H3 was readily differentiated from H5 (not shown in Table I) used by some subjects in the advanced problems; in H5 the subject hypothesized that an interaction between two individual elements, rather than any single element, might be responsible for the effect. Subjects using H3, in contrast, lacked a conception of main effects, much less interaction effects. As seen from Table 11, HO, HI, and H2 were infrequently used strategies (6, 9, and 10 instances, respectively). These instances were attributable to 4, 5, and 5 different subjects, respectively, suggesting that although certain subjects showed some disposition toward use of each of these strategies, none of the strategies was the idiosyncratic production of a single subject. A subject using H4 recognized that a single element might be responsible for the effect. As we shall see, however, this recognition by no means implied successful solution of the problem. In 9% of the sessions (see Table II), H4 occurred in conjunction with one of the preceding strategies. Multiple coding of hypothesis strategies occurred only when the different strategies occurred in distinctly different contexts within the session. In contrast, a subject often began with one of the lower level strategies and then elaborated the reasoning into what became a higher level strategy. In these cases, only the higher strategy was coded. C. EXPERIMENTATION STRATEGIES

Experimentation strategies are summarized in the middle column of Table I. “Genuine experimentation” is defined as experimentation that is hypothesisdirected, that is, conducted for the purpose of testing one or more hypotheses and in fact capable of providing such a test. Strategies not meeting this criterion fall into the category labeled “pseudoexperimentation.” They are of two types. The first consists of the generation of mixtures to be tested with no discernible rationale dictating the selection of those particular mixtures. These included cases in which some of the mixtures generated were single elements ( E l , shown by four subjects on a total of five occasions), as well as the majority of cases which did not include single-element mixtures (EO). The other, quite striking strategy consists of the replication of the demonstrated mixtures with minor variations (E2). Predictably, subjects using H1 employed E2 as the dominant experimentation strategy, but E2 occurred as well in conjunction with more advanced hypothesis strategies, notably H3, and was in fact the most frequent experimentation strategy overall (Table 11). E2 occasionally included a single-element mixture (7 of 48 instances), as, for example, when the subject broke the demonstrated mixture, BCD, into two parts (e.g., BC and D), but most often it did not. Under the “genuine experimentation” heading are three strategies. All three are sufficient, when utilized appropriately, to assess the role of individual ele-


15

ments in producing the outcome. One (E5) is also labeled as efficient, based on the criterion of requiring the least possible amount of experimentation (defined by number of vials of chemicals required). Some subjects followed the successful application of E5 with additional experimentation consisting of random, partially systematic, or systematic combinations of two or more elements, although this additional experimentation tended to drop out of a subject’s experimentation procedure soon after the subject realized that a single element produced the outcome. Because these strategies are superfluous in the initial problems and did not interfere with effective problem solution, they were not coded for the 151 protocols based on the initial problems. The infrequently used strategies, E l , E3, and E4, were each shown by at least four different subjects, suggesting (as in the case of the infrequent hypothesis strategies discussed previously) that although certain subjects showed a disposition toward use of these strategies, none was the idiosyncratic production of a single subject. Given the way “genuine experimentation” has been defined, it can occur only in the presence of “genuine hypothesis.” Thus, as reflected in Table 11, E3, E4, and E5 never occurred in the absence of H4. This fact should not be misinterpreted, however, as an empirical outcome; rather, it is a consequence of the way in which the strategies have been defined. H4, in contrast, did occur frequently in the absence of the “genuine experimentation” strategies E3, E4, or E5 (Table 11). In a small number of cases, multiple coding of experimentation strategies occurred. The multiple coding E3 and E5 was used if the effects of some elements were assessed by isolation while others were assessed with the inclusion of superfluous elements. E2 also occurred occasionally in conjunction with a more advanced strategy: Typically, the subject began with the E2 variation of the demonstration and then either saw the possibility of applying an E3 or E4 strategy to these variations or went on to construct additional mixtures in a way that reflected one of the more advanced strategies. Occasionally (four instances overall), a subject who relied on one of the lower level strategies stated an intention, or recognition of the need, to use a higher level strategy (e.g., “I should have tried E and F by themselves”). These instances were coded based only on the lower level strategy the subject actually employed. Intended experimentation strategies, however, are included in Table IV, summarizing individual subjects’ progress over the sessions. D. INFERENCE STRATEGIES

Inference strategies are summarized in the right-hand column of Table I . A strategy type was coded only once for a given session, even if the subject used that strategy more than once during the session. Mean number of different

16


inference strategy types within a session was 2.60. Table 111shows the number of inference strategies of different types that occurred, overall and as a function of experimentationstrategy type. Because Table 111 combines within- and betweensubject data, it does not reveal what combinations of inference strategies tended to be used within a single session. This information will be presented in Table IV, which summarizes each subject’s strategy use at each session. All inference strategies were displayed by at least six different subjects with the exception of 16b which was used by four subjects and 17a which was used by three subjects. The first four inference strategies in Table I reflect invalid inference. Although 10, 11, and I2 were relatively infrequent, I3 was a very frequent inference strategy, in fact the most frequent overall. In roughly one-fourth of the instances of its usage, it appeared as variant 13a (some of the data present contradict the inference), that is, mixtures had been generated which ( a ) included the element(s) the subject alleged to be effective but had not produced the outcome, or ( b ) did not include the alleged effective elements but produced the outcome. While 10, I1 , and I2 occurred primarily in conjunction with a lower level experimentation strategy, I3 occurred as well in conjunction with the advanced experimentation strategies. We shall turn shortly to illustrations of such instances. I4 and I5 are labeled valid but insufficient strategies in that, although valid, they are not sufficient in themselves to yield the solution to the problem. I6 and I7 are closely linked to the experimentation strategies E3 and E4, but as mentioned earlier and reflected in Table 111, the experimentation and inference strategies did not always occur in conjunction with one another. I6 and 17 are labeled as “valid, sufficient, but inefficient” according to the criterion of being sufficient to yield the solution to the problem but requiring a larger, more redundant data base on which to base the inference than does the efficient inference strategy, 18. In particular, 16a frequently occurred following lower level experimentation strategies, in fact more frequently than it occurred as the result of a planned experiment. In other words, after generating a series of mixtures based on strategies EO, E l , or E2, the subject observed post hoc that presence of a particular element consistently covaried with the outcome, that is, that 16a could be applied. 17, in contrast, most often, though not always, occurred following a planned experiment (E4). Whenever I7 was present, previous usage of I4 and I5 during that session was not coded, as I4 and I5 are degenerate forms of 17. The most advanced inference strategy, 18, occurred only in conjunction with E5. (More advanced inference strategies dealing with interaction effects do not appear in Table I, as they did not occur in the initial problems.) It should be noted that the E5-I8 sequence did not always lead to full problem solution because it was sometimes incompletely applied, that is, the effects of only some of the individual elements were tested.

TABLE 111 Experimentation and Inference Strategy Frequencies Inference strategies I0

11

I2

13a

13b

14

I5

16a

16b

17a

17b

I8

Totalnumber number Total inferencesD ofofinferences”

EO El E2 E3 E4 E5 E3 + E5 E2 + higherb Other mixed

6

4 1 2 0 1 0 0

12 0 5 0

11

6

4 1 25

20 2 12 6

5 0

1

0

0 0

1

23 2 45 8 6 9 4

0 0 0 0 0 30 6

92(32) 10(5) 134(48) 26(10) 30(9) 55(30) 17(6)

0 0

0 2

1

0

1

Total

14

8

20

34

Expenmentation strategies

-

4

1

4 1

0 2 0 0

1

2 I1 3 3 1

1

26 3 4 6

4

1

0

0 0 3

1 0 0 0

0 4 0 0

0 4 0 0

6 6

1

1

0

5

5 2

2

2 0

1 2

0

0

0 0

3

1

2

3 2

17(6) 12(5)

104

50

48

50

7

5

12

41

393(151)

13%

12%

13%

2%

1%

10%

100% 100%

1

% of all

inferences

4%

2%

5%

“Number of sessions is shown in parentheses bE2 in combination with E3, €3,or E5.

9%

27%

3%

18


IV. Results A summary of each subject’s strategy usage at each session is displayed in Table IV. This summary includes performance only on the initial three problems (in which a single element is sufficient to produce the outcome), covered by the analysis in Table I. How might one analyze data like those in Table IV? Our approach, in effect, was to treat each subject’s record as a “case study” of the change process, hoping that each case would contribute some insight into features of the process. In this section, we would like to take the reader through something like the investigative process we ourselves went through in studying these cases. A . THE POWER AND PERSISTENCE OF INVALID STRATEGIES

The two most striking, and we think significant, strategies we observed were E2 and 13. I3 occurred most frequently in conjunction with E2, although it also occurred commonly in conjunction with EO (or occasionally El). More surprisingly, as we shall illustrate, I3 also occurred in conjunction with the more advanced experimentation strategies. S2 represents a subject who relied almost exclusively on a very common H3-E2-13 pattern, and his protocols illustrate nicely the power of this sequence of strategies. S2 is a particularly striking case because he actually showed some advanced strategy usage in the first two sessions, before settling into the H3-E2-13 pattern, which he then relied on throughout the remaining sessions. Session 2, in fact, consisted of the most advanced H4-E5-18 sequence, with the addition of some I3 usage. (All excerpts are quoted verbatim. Deletions made for the sake of brevity are indicated by suspension points.) S2-2 (subject observed BCD and DEF, with F effective): Maybe E. . . . (Sure?) No, maybe F (Why?) ‘Cause F was with E.

He set up the following experiments, in the order indicated (those producing the reaction are followed by a plus sign): D, E, FS. (What will you find out?) One of them will turn pink. . . . (S adds mixing liquid) . . . It’s F . . . (Others have anything to do with it?) E and D didn’t do it. B and C . . . maybe, ‘cause I didn’t try them.

S2-2 thus engaged in successful use of an isolation strategy, despite the minor hints of false inclusion (13) in his invoking F as a cause on the basis of its occurring “with E” and his unwillingness to exclude B and C despite his observation that BCD did not yield the effect. (Such characteristics were never

TABLE IV Summary of Performance by Subject Session Subjecta

2

1

2 (M)

Mb

M*c.d

10(F) 14 (M) 15 (F) 1 (M) 8 (F) 12 (F) 13 (M) 5 (F) 6 (M) 11 (F) 7 (M)

I* I I M* I I I I I M* I

M M* I M* M

9 (M)

1

3 (F) 4 (F)

M* M*

M

M I 1

M* I M* M* M*

3 Ie M I M

M* VI* M M I I* M I* M* M* M*

5

4

I I I M I M* I* VI* I M* M I* M* M* M*

I I M M I I VE*g M* M 1 M* I* M* M* IVE*

6

7

VI M* M l h VE* M* I VE* I* M* / VE* VE*

I I I M I M* M* VI* VE* I VE* VE* I M* VE* VE*

1

I M M

I

"Subject's sex is shown in parentheses. bM, Mixed invalid and valid sufficient (inefficient or efficient) inference. CItalics, Genuine hypothesis present. d * , Genuine experimentation present (or articulation of the intent or need to use it). eI, Invalid inference predominant; valid insufficient (but no higher) inference may be present. NI, Valid inefficient inference; no invalid inference. gVE, Valid efficient inference; no invalid inference. Point of stabilization at valid efficient strategy. ' 4 , Advanced problems.

8

I M I M M M M M VE* VE* A M* M* VE* A

9 I I M M I M I M A' A A I* I VE* VE* A

10

11

A A / VE* VE* VE* A

I I VIf M* M VI* M* VI* A A A VE* VE* VE* A

I I M M M M I M A

20


present among more advanced subjects, who showed consistent usage of the H4-E5-I8 sequence.) In the next session, however, S2’s strategy usage was radically different: S2-3 (BCD and DEF, B effective): It’s BC, ‘cause D was in both and it didn’t do anything (Sure?) Yes.

The following mixtures were constructed: BC+ and D. I’m doing BC to see if it will turn cloudy. D will turn clear. (S adds mixing liquid) . . . (Think about how it’s turned out?) It came out like I said it would. B and C made it cloudy. (So. what makes a difference . . . ?) B and C. (How do you know?) ‘Cause I tried them alone and they turned cloudy.

Despite their striking difference with respect to logical validity, the approaches used by S 2 in sessions 2 and 3 bear some similarity. In session 2 , S 2 hypothesized that the effect was caused by a single element and proceeded to test this hypothesis by testing the single elements he believed might be responsible. In session 3, he hypothesized that BC caused the reaction (although in this case we term it a pseudohypothesis, as it derives not from the speculation that these individual elements in unique combination with one another produce the reaction but rather derives from the failure to conceive of B and C apart from one another). He proceeded to test this pseudohypothesis in a comparable way, that is, by trying out the mixture he believed responsible for the effect. In this case, however, his strategy led him to an invalid conclusion. Thus, as we will illustrate later with additional examples, the first appearance of an advanced strategy did not necessarily signify that the subject was in full command of that strategy, that is, fully understood the logic of what he or she was doing. The latter proved to be a much more gradual and difficult achievement. In the case of S2, however, this achievement in fact never occurred. S2 was the only subject who evidenced a clear suggestion of regression, and it is the H3-E2-13 pattern to which he gravitated and in which he then became rigidly locked. (S2 used this strategy sequence, virtually unchanged, in sessions 3 through 11.) What is most noteworthy about this pattern, of course, is that the subject chooses the E2 variations in such a way that confirmation of the initial inference is assured. The prevalence of this strategy sequence in the present data accords with previous suggestions from the literature on reasoning that people seek information that will confirm rather than disconfirm their hypotheses (Moshman, 1979; Snyder & Swann, 1978; Wason & Johnson-Laird, 1972). Occasionally, a subject included some variations that did provide data that stood in contradiction to the initial inference. Was this sufficient to disrupt the subject’s approach? Usually not. In these cases, the subject typically simply ignored the contradiction. For example, following the initial observation that

The Development of Problem-Solving Strutegies

21

DEF turned red and BCD did not, S6-5 inferred that EF caused the reaction. She constructed the following mixtures: E F + , BC, D, and E. On observing the outcome, she affirmed her original inference: ‘cause I tried it and it turned pink.” She herself had produced data indicating that D or E in isolation showed no effect, but she ignored these data and they never led her to speculate that F alone in the original DEF or in EF may have been responsible for the effect. In contrast, from the fourth session on, S2 tended to construct only the single mixture he predicted would yield the outcome. When he did venture further experiments, they were constructed in such a way as to preclude the possibility of obtaining conflicting data. In session 7, for example, he inferred that BC was responsible for the effect and proceeded to construct the following mixtures: BC+, BCF+, D, and EF. The expected predictions were made and S2 interpreted the outcome as affirming his original inference. Clearly, then, a subject using S2’s strategies meets with consistent success. Confirmation of the initial inference is ensured, and in this sense the whole experimentation process is superfluous, serving from the subject’s perspective more as a demonstration of the correctness of the initial inference than as a test of it. The system, then, is closed, and one can see why the approach might rigidify, as it did in the case of S2. The E2-I3 combination just illustrated was very prevalent. Nine of the 15 subjects used it at some point, and many did so consistently. 13 itself was even more conimon. All but two subjects showed in at least one session primary reliance on the 13 strategy (in conjunction with either EO or E2), failing to show any valid sufficient inference. Subjects in these sessions were clearly capable of a reasonably sophisticated form of logical inference, however: All of them in at least some sessions employed the valid insufficient strategies, I4 and 15. An obvious question, then, is what sorts of occurrences might be sufficient to lead a subject out of reliance on the EO-I3 or E2-I3 approaches. One possibility might be that if subjects simply generated enough data, they would begin to observe a consistent pattern, leading ultimately to adoption of the 16a strategy and rejection of 13 as fallacious. S 6 (who used the E l variant of EO, in which single elements are included) provides a striking example of the fact that this was not the case. “

Sh-1 (BCD and DEF, E effective): You need F. (Why?) Because BCD doesn’t turn red

The following mixtures were constructed: DF, CB, DC, BFC, F, CD, EF+, BEDF+, ECBF+, FD, B, C, D, E + , FBD. BCDEF+, BDF, BCD, DF, E F + , CD, D E + , and FC. (What will you find out. . . ? ) What different colors will turn out. (How will it turn out’?) I don’t know. (S adds mixing liquid) . . . (What did you find out‘?) Which ones turn red. (So,

22


what makes a difference. tell?) 1 don’t know.

, ,

’?) I don’t know

. . . (pause) . . . I don’t know. (Any way to

Thus, the mere presence of adequate data to allow for a valid inference does not ensure that the subject will be able to make use of those data by applying an appropriate inference strategy. Once a subject does become able to see a pattern in the data, however, and achieves the insight that a single element is responsible for the outcome, we might expect that this insight would be sufficient to lead the subject both to a radically different experimentation strategy and to abandonment of the invalid 13 strategy. The fact that it was not is illustrated strikingly by the performance of S10 over the 11 sessions. The case of S10 also illustrates the surprising pattern of mixed valid and invalid inference that turned out to be extremely common. In session 1 , S10 established the characteristic E2-I3 pattern. Her initial inference strategy was I3 and the mixtures she constructed clearly reflected the E2 strategy (DEF produced the outcome and BCD did not produce it): DEF+ and CEF+. . . . find out . . . ?) I don’t know. ( . . . turn out‘?) I hope it turns pink. (S adds mixing liquid) . . . ( . . . found out?) Both make the same thing. (So, what makes a difference. . . ?) F and E. (How do you know?) Just guesses. (Sure?) I can’t be sure. . . . You would need to try each one alone. (

Despite the occurrence of this surprising insight at the end (coded as intended E5), it had no influence on her approach at the next session: S10-2 (BCD and DEF, F effective): Use DEF. (?) ‘Cause of the color. I t turned pink.

The following mixtures were constructed: CDF+ and BEF+ . ( , . . find out . . . ?) BEF is the same as DEF ‘cause both use E and F. ( . . . turn out’?)CDF will turn out pink. (S adds mixing liquid) . . . It came out the same as yours. . . . ( . . . find out‘?) F makes it turn out. F is used in those that turn pink. , . . (So, what makes a difference?) F. (Others have to do with it?) They help it. D in CDF and E in BEF helped it.

SlO’s insight in session 1 regarding the limitations of her experimentation strategy did not lead her to change that strategy in session 2 . Probably by chance, however, she did not consistently pair two critical elements in session 2 , as she had in the first session, thus making possible the recognition that only a single element covaries with the outcome (I6a). Immediately following use of this valid strategy, however, she again applied the false inclusion strategy, I3b, in asserting that other elements present in the mixtures “helped.” Nor did the insight reflected in 16a carry over to the next session, as we might have anticipated. Session 3, in fact, was a carbon copy of session 2 , including


23

the initial I3 strategy, E2, the subsequent I6a, and the final recurrence of 13 reflected in the assertion that other elements “helped,” although this time S10 remarked, “They just help a little.” In session 4 (BCD and DEF, C effective) S10 consistently paired two critical elements so that, as in session 1, I6 was not possible. The following mixtures were constructed: BCD+ and BCF+. ( . . . find out . . . ? )If they will turn cloudy. ( . . . turn out’?)Cloudy, I hope. (S adds mixing liquid) , , . ( . . . turned out?) Both turn cloudy. ( . . . found out’?) BCD makes the same thing as BCF. (So, what makes a difference , , . ?) Use B and C (?) ‘Cause I used it there (indicates) and you did too.

In session 5 (BCD and DEF, F effective), S 10 did not consistently pair critical elements, so that an inference regarding the effective element was possible. The following mixtures were constructed: DEC and DEF+ . ( ... ( .. .

find out . . . ?) turned out?) Not by subject) both make ‘Cause I used them in

Both will turn pink. I just hope. . . . ( S adds mixing liquid ) . . . too good. ( . . . found out?) These (original DEF and DEF constructed the same . . . (So, what makes a difference . . . ?) D, E, and F. (?) mine and you used them in yours.

Even if this subject had never shown any higher level reasoning than this, her failure to make the obvious inference she might have at this session would still be surprising. Her failure to do so, however, is indeed remarkable in view of the fact that on two previous occasions she had recognized that only a single element was responsible for the outcome. Yet, clearly, her insight on those two occasions was not sufficient to effect a lasting change in the way she conceptualized the problem. Sessions 10-6 and 10-7 were similar in that no valid sufficient strategies appeared. Session 10-6 was similar to 10-4 in that the critical elements were consistently paired and 10-7 was similar to 10-5 in that they were not, allowing the possibility of a valid inference. In session 10-8, I6 reappeared: SIO-8 (BCD and DEF, E effective): D, E, and F (?) ‘Cause we used it and there was no D, E, or F in BCD and it didn’t turn out good , . ,

The following mixtures were constructed: BEF+, BDF, and ECD+ . BDF will turn out cloudy. (?) Because I want it to. ( S adds mixing liquid) . . . ( . . . found out?) BEF and ECD turn out cloudy. . . . (So, what makes a difference . . . ?) E. All with E turn cloudy and there’s no E in BDF and it didn’t turn out.

This is the last time, however, we see I6 in S 10’s reasoning. She repeated the E2-


24

I3 pattern in the rest of the sessions and by session 1 1 she exhibited a remarkable inability to “see” what the data clearly indicated: S10-11 (BCD and DEF, F effective): D, E, and F. (?) ‘Cause it turned pink

The following mixtures were constructed: F+ , DE, and FDC+. . . . find out . . . ?) I don’t know. ( . . . turn out?) FDC will turn pink because it has F and D in it. ( S adds mixing liquid) . . . ( . . . turned out?) Good. ( . . . found out?) F makes the same as FDC. (So, what makes a difference . . . ?) D and F. (?) ‘Cause the experiments with them turned pink. (

Most subjects met with more success overall than did S2 and S10. Nevertheless, the mixed (valid and invalid) inference pattern, illustrated by SlO, was extremely common, both within and across sessions. Indeed, every subject showed a mixed inference strategy pattern in at least one session, and most often over repeated sessions. The I3 and I6 strategies were the ones most frequently combined, as was illustrated in the case of S 10. S 14 and S 15 were similar to S 10 in showing repeated usage of I6 in combination with 13, although they both showed more consistent usage of I6 than did SIO. Unlike SlO, S14 and S15 occasionally anticipated the fact that a single element might be responsible (H4), but this recognition did not deter them from the subsequent use of 13. Other less common mixed inference patterns consisted of I3 in conjunction with I7 and occasionally even 18, as will be illustrated in some later examples. Thus, recognition that a single element was responsible for the outcome (16, 17, or 18) did not necessarily lead to a radical change in a subject’s experimentation strategy. Nor did it lead subjects to discard false inclusion inference strategies. More generally, competence in executing advanced strategies was not a sufficient condition for problem mastery, and the co-occurrence of valid and invalid strategies within a single session proved to be the rule rather than the exception (Table IV). B.

PATTERNS OF CHANGE

As Table IV further indicates, however, some subjects did eventually master the problem. We refer to the last seven subjects in Table IV, who began to show consistent usage of the H4-E5-18 pattern.’ With the exception of a single ’The criterion for advancement to the next problem in the series, as indicated in Section IIC, was specification of the correct element as effective and the exclusion of all others as ineffective. Thus, a subject could advance to the next problem without having utilized the H4-E5-18 sequence. Advancement to the next problem without utilization of the H4-E5-18 sequence occurred occasionally in the case of advancement to problems 2 and 3 (any of two or three elements effective) but, as can be inferred from Table IV, never in the case of advancement to the more advanced problems. Conversely, use of H4-ES-I8 ordinarily implied advancement to the next problem. Occasionally it did not (e.g., in the case of S3) because the H4-E5-18 sequence was incompletely applied and did not lead to full problem solution.


25

instance (S12-5), once the H4-E5-18 pattern appeared, it began to be used consistently, and lower level strategies were soon abandoned. In contrast, neither the inefficient experimentation nor inference strategies (E3, E4, 16, and 17) constituted stable approaches by themselves (even though, as their label implies, these strategies are sufficient by themselves, though inefficient, for problem mastery). Subjects never showed prolonged usage of either the E3-I6 or the E4-I7 sequence (or of either of the experimentation or inference strategies alone, without the other) without the additional use of the invalid inference strategy, 13. To state it another way, subjects did not fully abandon the less adequate (false inclusion) strategies until they achieved stable usage of the valid, maximally efficient strategies. What led to this achievement? S5 is an appropriate case to consider first in investigating this question, for the manner in which S5’s attainment occurred accords quite closely with what we might have predicted: Sudden insight (that a single element is responsible for the outcome) produces a radically different representation of the problem (the problem is “cracked”) and accordingly a dramatic shift in the mode of solution. In this regard, S5 stands in striking contrast to SIO. S5’s first four sessions were very similar. Session 4 provides an example: S5-4 (BCD and DEF. C effective): C and B. (’?) ‘Cause they’re close together in the alphabet. . . . (Others have anything to do with it?) No. ‘cause we didn’t try others. BD might make i t turn cloudy.

The following mixtures were constructed: CB+ and DB. ( . . . find out . . . ? ) Just a guess. I don’t know. ( . . . turn out?) Good. ( S adds mixing liquid) . . . ( . . . turned out‘?)Good. ( . . . found out?) New ways to do it. (So, what makes a difference . . . ’?) The different chemicals. (?) ‘Cause we did it.

Session 5 was as follows: S5-5 (BCD and DEF, F effective): E and F (?) Just a guess

The following mixtures were constructed: F E + , FD+, DF+ and BE. , , find out , . , ?) I’m just trying to make it pink. ( . . . turn out . . . ? ) Pretty good. (S adds mixing liquid) . , , ( , , . turned out , , , ? ) Good. ( . . . found out . . . ?) 1 think it’s F. All those with F turn red.

(

,

Following four sessions with no such insight, S5’s unexpected recognition that a single element covaried with the outcome (16) dramatically changed her subsequent performance. In the next session, she proceeded to assess systematically the effect of each element in isolation (H4-E5-18), and she continued to employ

26


this approach through the rest of the sessions, with no recurrence of invalid strategies. Of the seven subjects who achieved stabilization at the valid efficient strategy level, however, S5 was the striking exception. The more characteristic pattern was a much more gradual acquisition, with a sustained period during which more advanced strategies were used in conjunction with less advanced ones. In this sense, subjects during this period appeared similar to the subjects who never achieved stabilization at the efficient strategy level. A critical question that arises, then, is this: Did the two groups of subjects differ in any discernible way prior to the achievement of stabilization in the one group? Before attempting to answer this question, let us take a closer look at the patterns of change among subjects who did achieve mastery. We were able to identify two sources of difficulty these subjects experienced, which help to explain why (with the striking exception of S5) they took as long to achieve mastery as they did. First, when they initially appeared, the advanced experimentation strategies did not always function in a complete, or completely correct, manner, and it was only after repeated application that they became fully functional. Second, when the experimentation strategy was utilized in a completely correct manner, the false inclusion inference strategy was often superimposed on what would otherwise be a valid solution, and sometimes even precluded the valid inference strategy from being used. S3 provides a good example of a subject who experienced both sources of difficulty: S3-1 (BCD and DEF, E effective): Couldn’t have been D. F sort of had an effect. (?) Both had D and BCD didn’t turn red. DEF did turn red, so it had to be F.

The following mixtures were constructed: BEF+, EF+, and DEF+ . . . find out . . . ?) If D had anything to do with it . . . . ( S adds mixing liquid) . . , ( , , . turned out?) D didn’t have anything to do with it . . . . . (So, what makes a difference . . , ?) E and F. D didn’t do anything. EF turned out the same as DEF. (

In this session, S 3 used the sophisticated E4 strategy to test the effect of the element D, despite the fact she had already explicitly eliminated D before undertaking her experimentation. Thus, an appropriate strategy is used in an inappropriate way. When it became evident that the strategy had not yielded an adequate solution, she reverted to a more primitive strategy (13) to solve the problem. In sessions 2 and 3, the same E4 strategy was attempted, although in these instances S3 constructed an experiment that was not redundant with her previous inference:

The Devrloprnenr of Problem-Solving Strategies

27

53-3 (BCD and DEF. B effective): B, C. and D. . . . It might be B or C that does i t

The following mixture was constructed: BD+ . . . find out . . . ?) If B or C has anything to do with it. . , . (Sadds mixing liquid) , . . So it nust be the B and D. Just take out C from the BCD and you would still get the effect.

(

In this instance, the E4 strategy was applied properly. It appears, however, that the “success” of the outcome was enough to divert S3 from a more careful interpretation of it, and though C was appropriately eliminated via the E4-I7 strategies, D was again falsely included, after its implicit elimination prior to the experimentation. In session 4, S3 turned to a completely new experimentation strategy, E5. Just as was the case with E4, however, E5 initially was employed in a partial, redundant, and therefore inadequate manner: S3-4 (BCD and DEF, C effective): Either B or C or D. Don’t know which one.

The following mixture was constructed: D ( . . . find out. . . ?) We’ll find out if it’s just the D alone. If not, it’s probably B or C. . . . (S adds mixing liquid) . . . ( . . . turned out?) Not so good. ( . . . found out’?) D didn’t do anything. (So, what makes a difference. . . Y ) B and C. (?) ’Cause B and C was in BCD and it turned out.

As in session 1 , the failure of an advanced experimentation strategy to yield an adequate solution appears to have led S3 to revert to the invalid strategy. In session 5, the E5 strategy was again used in an incomplete manner, and S3 remarked at the end of the session, “I should try D, E, and F separately.” In session 6, however, the ES strategy was still not completely functional. After the initial hypothesis that “It could be B alone or something else” (BCD and DEF, C effective), she constructed the following mixtures: D and E. ( . . . find out. . . ?) Whether it’s D, E, or C. . . . (S adds mixing liquid) . . . ( . . . turned out?) Not good. ( . . . found out?) I didn’t find out anything. (So, what makes a difference. . . ?) B , C, or D. D didn’t do anything, so 11‘s B or C.

In session 6, for the first time S3 did not resort to the false inclusion inference and remained aware that her E5 strategy had not yielded a definitive solution. In session 7, she finally applied the E5 strategy in a comprehensive manner and achieved full problem solution. The H4-E5-18 strategies were the only ones used in the remaining sessions. In sessions 8 and 9, however, E5 was again applied incompletely and full problem solution was attained only in sessions 10 and 11.

28

Deanria Kuhn and Erin Phelps

Like S3, S9 on one occasion showed the same inappropriate use of E4 to assess the effect of the element D which had already been eliminated (via 15). S9 similarly showed incomplete usage of E5 in a number of instances, leading to failure to fully solve the problem and reversion to false inclusion inference. S6 and S l 1 also showed incomplete or incorrect usage of the advanced experimentation strategies when they first appeared. S9 on occasion also showed a completely adequate usage of H4-E5-18, on which I3 was then superimposed. In session 2 (BCD and DEF, F effective), S9 constructed the following experiments: EF+, BC, E, and F + . ( . . . find out. . . ?) Could be just E that will make it pink. . . . (S adds mixing liquid) . . . I thought F alone would turn pink, and I knew EF would. . . . (So, what makes a difference. . , ?) F. (?) F alone turns pink. E and F together makes pink; it’s F. (Sure?) No. (?) F might help E turn pink.

Occasionally, the I3 strategy was so overpowering that it completely eliminated the valid inference strategy that might otherwise have followed from application of one of the advanced experimentation strategies. S7 was a striking case in this regard. He quite consistently employed an advanced experimentation strategy but was rarely successful in following it with an appropriate inference strategy: S7-3 (BCD and DEF, B effective): B and C, I think.

The following mixtures were constructed: BC+, FBC+, BF+, and CE. ( . . . find out. . . ?) If C by itself or something else will turn out. . . . (S adds mixing liquid) . . . ( . . . found out?) Some turn cloudy. B and C turn out cloudy. (So, what makes a difference. . . ?) B and C. B and C makes it cloudy.

Thus, S7-3 has conceived of the possibility that a single element might be effective (“C by itself”) and constructed an experiment (coded E3) capable of providing an adequate test. Yet it appeared that the initial inference exercised such a hold over the subject that it prevented his making an appropriate inference following his experimentation, even though his reversion to the I3 strategy required him to ignore data he had generated that were discrepant with it. S7’s use of E3 without a subsequent valid inference strategy recurred in sessions 4 and 5. In sessions 6 and 9, E4 was utilized in the same unsuccessful manner, without a subsequent valid inference. The protocols of an ultimately successful subject like S7 can be compared with those of a much less successful subject, S1. In session 3 (BCD and DEF, B effective) S1 applied an initial I3 strategy and then constructed the following mixture: CD.

The Development of' Problem-Solving Srraregies

29

. . find out. , . ?) We don't know that B has an effect. . . . (S adds mixing liquid) . . . . . turned out'?) CD has no effect: it didn't turn cloudy. But B has an effect. ( . , . found out?) B does it. . . . (Others have anything to do with i t . . . '?) C. because it was in BCD. (

(

.

Session 1-3 reflects the combination of an advanced experimentation and inference strategy used in conjunction with an invalid inference strategy. Session 1-4 (BCD and DEF, C effective) appears similar to 1-3 on the surface, but the intent underlying the experimentation was clearly very different. Following an initial 13 strategy, the following mixture was constructed: BC+. ( . . . find out. . . '?) What makes it cloudy'? ( , , . turn out'?) I have no idea. ( S adds mixing liquid) . . . ( . . . turned out?) Good. ( . . . found out?) B and C did it . . . (Others. . . ? ) I guess so. D might have helped it.

Comparison of SI-3 and SI-4 suggests that the power, or compellingness, of the false inclusion inference may swamp the subject's ability to use the more advanced experimentation and inference strategies. S 1-4 shows no evidence of a sophisticated intent, but the "success" of the experiment seems to preclude the subject's taking a more critical look at the outcome, although we know from thc preceding session that S 1 had the competence to do so. Indeed, the power of this approach is such that S1 duplicated it, virtually exactly, in the next three sessions, and the genuine experimentation strategies evident at the early sessions never reappeared. Some important similarities appear, then, in the protocols of the ultimately successful and unsuccessful subjects. In both, the advanced, valid strategies seem to compete with the less advanced, invalid strategies for dominance. Both groups of subjects had within their competence more advanced strategies with which to replace the invalid ones, but discarding the invalid strategies appeared to pose a formidable challenge for both groups, a challenge one group never successfully met. The particular power and persistence of I3 is perhaps best reflected in these remarks by S 15-5: Need F. Whenever I had F, it turned red. . . . (Others have to do with it?) No, except E might help it turn red in BEF. But you don't really need E to make red.

C.

PREDICTION OF CHANGE

We come. then, to the question of what, if anything, differentiated the performance of those subjects who were ultimately successful from those who were not. A number of factors that might have differentiated the groups in fact did not. The mere ability to generate sufficient data to make isolation of the critical element possible did not in and of itself lead to success, as was illustrated by

30


S6-1. Nor did the recognition that a single element might be responsible for the outcome (H4). Nor, as we have already noted, was competence in advanced experimentation or inference strategies a differentiating factor. Examination of Table IV, however, does reveal one clear difference between the two groups. With the striking exception of S 5 , all subjects who did eventually stabilize at the valid efficient strategy level showed frequent usage of genuine experimentation strategies, that is, a relatively high percentage of sessions at which genuine experimentation was displayed, prior to this stabilization. The lowest percentage is 5096, shown by S6, and the remaining percentages vary from 60 (S1 1 ) to 100% (S3 and S4). In contrast, among the eight subjects who did not achieve stabilization at the valid efficient strategy level, the highest percentage is 45% (S12) and four subjects showed only 9% (one session of the 11). How should this difference be interpreted? The use of genuine experimentation strategies, as we have defined these strategies, implies a planfulness or purposefulness in designing experimentation. Recall, however, that subjects using strategy E2 also often showed a decided purposefulness in conducting their experiments (i.e., to confirm their initial inference), as well as a very specific anticipation regarding the results (i.e., that the results would confirm their predictions and hence their initial inference). Recall, also, however, that in these cases the “experiment” that was selected was such that it could not disconfirm the subject’s anticipation. Thus, as we noted, in some sense the whole experimentation process was superfluous, serving more as a demonstration of the correctness of the initial inference than as a test of it. What appears significant, then, is the frequency with which subjects’ experimentation involves a plan, or “anticipatory scheme,” which includes possible alternative outcomes that will inform the inferences that are to follow from the experimentation. We can speculate that the application of this type of “anticipatory scheme” to the data generated by the experiments is what is important in overcoming the power of the invalid false inclusion inference strategy. Subjects who did not show frequent use of such a plan, we observed, rarely mastered the problem, even though their performance frequently reflected both the insight that a single element may be responsible for the effect (H4) and an inference as to which element is effective (16). Subjects who frequently employed such a plan, in contrast, eventually met with success. This was the case even though (a) the experimentation strategies they used may have been inefficient for solving the problem, (b) the strategies may have operated initially in a less than fully functional manner, and (c) invalid inference strategies may have been superimposed on the valid inference strategies logically following from them or even may have precluded use of these valid inference strategies. Worth noting in concluding this section is the fact that less successful subjects who rarely used genuine experimentation strategies occasionally exhibited some awareness of the limitations of the less adequate experimentation and inference


31

strategies they employed. It tended to take the form of an awareness that the strategies being used were not fruitful in yielding a solution to the problem, rather than an awareness that the strategies used yielded invalid solutions. The most articulate expression of this awareness came from S 15. In session 7, S I5 showed H4 (“See if B does it”) but then proceeded to test this hypothesis by generating six unsystematic three-element mixtures (EO), all of which turned red. After studying the outcome, she exclaimed, with evident frustration: I don’t really know. I’ve got chemicals all over the place. There’s so many ways I’ve done it, I can’t tell. They all turn pink. You need one that stays clear before you can tell anything.

D. RECURRENCE OF INVALID STRATEGIES

Before concluding the presentation of our findings, we should mention how the four subjects who proceeded to the advanced problems fared. Their performance on the advanced problems is noteworthy, as three of the four showed some recurrence of the false inclusion strategy, even though all had clearly mastered the use of the valid efficient strategy approach, without false inclusion, with respect to the simple problems. ( S 5 was the one subject who did not revert to false inclusion.) S4 provides an example. The H4-ES-I8 strategy sequence was well consolidated by the time S4 encountered the advanced problems (session 8). S4 was clearly competent from session 8 with regard to a two-way combinatorial strategy, and she had no problem in mastering a two-way interaction problem. In session 8, she constructed the following mixtures: EF, DF, CF, BF, DE, CE, BE, CD, BD, BC+, B, C, D, E, F, and BCE+. “It’s B and C,” she exclaimed spontaneously, on completing the mixing. “B and C together turned out and the others have no effect.” The next problem, however, presented in session 9, involved a three-way interaction, and S4’s combinatorial strategy was not sufficiently developed to generate systematic three-way combinations. She constructed the 10 two-element mixtures and five single-element mixtures, none of which showed the reaction. She then reverted to a much lower level strategy: “Use BCDE” (the demonstrated mixture). (?) “Because it worked. None of our experiments with two or one turned out.” S4 did acknowledge, however, “It could be just three chemicals, and one might have no effect.” In session 10, a three-way interaction problem was again presented. S4 included three three-element mixtures in her experimentation, one of which happened to produce the outcome, and she made the appropriate inference. In session 1 1, the problem presented was the most advanced type, involving inhibitive effects: BE was demonstrated as producing the outcome and BCDF as failing to produce it; the outcome was in fact caused by B or C, without D. S4 con-

32

LIeanna Kuhn and Erin Phelps

structed the following mixtures: E, EF, ED, EC+, DFE, BCE+ , DE, BD, BF+, BC+ ,D, and CF+ . After studying the outcome and noting several of its features without making any inferences, S4 concluded: “It’s B and E” (the demonstrated mixture). Thus, in the absence of having available any higher level strategies to apply to the outcome, S4 again reverted to a very low-level invalid strategy. On this occasion, however, no less than four discrepant outcomes were present, all of which she had to ignore in order to apply the inference strategy she used.

V. Replication and Variations We decided to replicate the original study for a number of reasons. A replication is desirable whenever the number of subjects in the original study is small. The patterns of change observed in the present initial study made a replication particularly important. Seven subjects zchieved performance mastery, that is, stabilization at the level of valid efficient strategy usage. Of these seven, one ( S 5 ) showed a very different pattern of attainment from the others. Further evidence would be very desirable, therefore, as to whether in fact two characteristic patterns of attainment exist, one less common than the other. Alternatively, the pattern shown by S5 may be so atypical that it cannot be described as “characteristic,” and would not recur in another sample. Subjects in the replication sample were 15 fourth-graders. Chronological age ranged from 9:9 to 11:3.* All subjects were reported by their classroom teachers to be within an average range academically. The school was a public one in a middle- to upper-middle-class neighborhood. The procedure was identical to that of the initial study, except that only the initial problem was used.3 Subjects continued the weekly sessions until they reached a criterion of four consecutive sessions of valid efficient strategy usage, except that all subjects continued until eight sessions had been completed. The sessions were discontinued after the thirteenth session, which was near the end of the school year. A school vacation of 1 week extended the total period of observation to 14 weeks. Protocols were coded by two raters, as in the initial study, and differences were resolved by discussion. Reliability improved for hypothesis and inference strategies, from 90 and 87%, respectively, to 98% for each, and remained 2Although the original sample included fourth-and fifth-graders, the age range was comparable in the two samples. ‘The primary reason for this modification was that so few subjects in the initial sample reached the advanced problems This modification also served a secondary purpose, however. Even though problems 1-3 in the initial study required identical strategies for solution, the second and third problems (in which any one of two or three single elements produced the outcome) conceivably yielded more complex data that in some way impeded the subject’s progress. The replication study enabled us to ensure that the performance variability that was observed was genuinely attributable to the subject, rather than to a change in the problem.

The Development of Probli,m-Solving Strategies

33

roughly equivalent for experimentation strategies-77 and 83% in the initial and replication studies, respectively. A summary of subjects’ performance is presented in Table V (comparable to Table IV for the initial sample). The data presented in Tables I1 and 111 for the initial sample were essentially replicated in the new sample, and therefore analogous tables have been omitted. The data in Table V are generally similar to those in Table IV. All subjects showed at least some competence in the use of advanced strategies, but only a portion of the subjects attained stabilization at the level of valid efficient strategy usage-in this case 9 of the 15, or 60%. (The number of subjects attaining this stabilization in the initial sample, by comparison, was 7, or 45%, as reflected in Table I V . ) None of the six subjects in the replication sample who did not achieve stabilization showed frequent usage of genuine experimentation strategies (E3-E5). Of the nine subjects who did achieve stabilization, the same two patterns of attainment seen in the initial sample appeared, with one again much more frequent than the other. One subject (S26) achieved stabilization too quickly to be unambiguously classified as adhering to one pattern or the other. Six of the nine subjects showed the pattern of gradual attainment that was shown by the majority of the subjects attaining mastery in the initial sample. As in the initial sample, stabilization in this pattern was preceded by a high percentage usage of genuine experimentation strategies. Two subjects (S19 and S20) showed a pattern of attainment similar to that shown by S5 in the initial sample, suggesting that this pattern of change is a secondary, or alternative, one that occurs with some frequency. One other noteworthy thing that appeared in the replication study was the occurrence of the valid inefficient inference pattern over a number of consecutive sessions, after invalid inference strategies had been discarded but prior to stabilization of the valid efficient strategy patterns (S17, sessions 4 through 7). In the original sample, and in all other cases except S17 in the replication sample, this order was reversed: Invalid inference strategies were not discarded until stabilization of the valid efficient strategy pattern had occurred. Space limitations permit only brief mention of a series of further studies in which we utilized variations of the basic method described in this article. Two studies in particular warrant mention because they were designed to address specific questions with respect to the method and findings presented here. In a dissertation by Lewis (1981), two conditions were compared. In one, the standard interview format described in this article was employed. In the other, only the initial question was retained (“What do you think makes a difference . . .”). The procedure was otherwise identical. The purpose was to assess the effect of the interview questions themselves. In order for change to occur, must the subject’s exercise of cognitive strategies be encouraged by the interviewer (“How do you think it’s going to turn out?”; “What have you found out?”; etc.), or is presentation of the problem itself sufficient? The answer was

TABLE V

Summary of Performance by Subject (Replication Sample) Session SubjectQ 27 18 28 25 30 24 19 P W

(F) (M) (FI

(D (M) (F)

(M)

20 (F) 29 (M) 23 (F) 21 (F) 16 ( M )

17 (F)

22 (M) 26 (M)

1

2

Ib

I I I I I M I I M* I I M* M* M* VE*

I I I I M I I I M I M M

M M

/

3

I I I 1

I M /g

VE*h

I I M* I* M* M*

M* VE*

4

I I I I I M VE* I M* M* I M* VI* M* I VE*

5

6

I I I*e I I M VE* I M* VI* M* M* VI* VE* VE*

I I I I I M VE* I M

7 I C

I I 1

M M*

M*

VE* M* I* VE* VI*

M*

M*

VI* VE* VE*

VI* VE* VE*

M*

I

/

8

9

I I M M M* M* VE* VE* VI* VE* M* VI* VE* VE* VE*

I M* M M M

“Subject’s sex is shown in parentheses. b I , Invalid inference predominant; valid insufficient (but no higher) inference may be present. cItalics, Genuine hypothesis present. dM, Mixed invalid and valid sufficient (inefficient or efficient) inference. e * , Genuine experimentation present (or articulation of the intent or need to use it). M, Valid inefficient inference; no invalid inference. 81, Point of stabilization at valid efficient strategy. hVE, Valid efficient inference; no invalid inference.

VIf

I I

VE* VE* VE* VE*

M* VE*

10

I I M I I VI VE* VE* VE* VE* VE* VE*

12

13

M*

I I M I* I VI*

I I M* M M* M*

VE* VE*

VE*

11 1

I M* M I

VE* VE* VE*

VE* VE*

VE*

clear-cut. Although absence of the interviewer’s questions made impossible the sort of detailed strategy analysis that has been presented in this article, when the full interview was presented to subjects in both conditions in the final (eleventh) session, differences between the two groups in the level of strategies used were insignificant. Another study, by Kuhn and Ho (1980), was undertaken to assess the role of the experimentation component of the problem-solving process. In order for change to occur, must subjects design and carry out their own experimentation, or do they also make progress exercising cognitive strategies with respect only to the inference component of the process, that is, in making inferences about data that already have been compiled? This question was a significant one, we believed, for it is the latter condition that more often characterizes problem-solving in natural contexts. In the study that was addressed to this question, Kuhn and Ho employed a yoked-control design in which each yoked-control subject was presented exactly those experiments that had been designed and conducted by the experimental subject to whom the control subject was yoked. (“Here are some other ways of doing it,” the interviewer said; the procedure was otherwise identical.) Thus, the information each member of the pair had access to was identical; the only difference was that in one case subjects designed the experiments that would yield this information and in the other they did not. Subjects in the yoked-control condition made significant progress. Thus, the designing of experiments is not critical for the occurrence of change. Experimental subjects, however, overall progressed further and faster than their yokedcontrol partners. This finding, too, we think is significant, especially in relation to our finding that subjects who make frequent use of genuine experimentation strategies are more successful than those who do not. We suggested in Section IVC that subjects in the former group employed “anticipatory schemes” in terms of which the experimental results could be interpreted and that use of such schemes facilitated their progress. Subjects in the Kuhn and Ho yoked-control condition were less likely to form the anticipatory schemes that might have come from designing their own experiments. Hence, we can speculate that they were less able “to make use of” (in the cognitive sense) data deriving from such experiments and therefore were less likely to progress than subjects who had thi: opportunity. In each of the studies described in this article, an attempt was made to assess the durability and/or generality of the changes that were observed. Space does not pemiit presentation of these findings in detail, but they are similar to what might be anticipated and can easily be summarized. Subjects in the replication study were presented the chemicals problem on a final occasion 6 months following the end of the observation period and were found to show no significant change in performance. In the Lewis study, two parallel forms of the problem, differing only with respect to content, were designed. One involved an electric


36

light controlled by one of several possible switches; the other was the chemicals problem already described in the present article. The electric switch problem was employed during the main portion of the study, and the chemicals problem was presented at the end of the observation period as a measure of transfer. The majority of subjects used the same strategies in the chemicals problem as in the final session with the electric switch problem. The incidence of decrease in level of strategy applied to the new problem was only slightly greater than the incidence of increase. In the Lewis study, as well as some of the other studies, however, when subjects were presented other problems less similar to the problem encountered during the observation period, a decrease in level of strategy usage tended to occur as the similarity between the two problems decreased. We will say something more about the significance of this finding in discussing our results (Section VI). Each of the studies we have described replicated the main study reported in this article with respect to what we believe is the main study’s major finding: The predominant pattern of change involves an extended period of highly variable performance in which higher level and lower level strategies are used in conjunction with one another. Several other studies suggest that both the method and this particular fitlding are fairly robust even with more radical variations of the basic method described in this article. In her doctoral dissertation, Forman (1981) employed the same basic method and problem except that subjects worked on the problem in pairs rather than individually. Commons and Davidson (in preparation) used a form of causal attribution problem similar to the present one but increased the density of problem presentation and reduced the total period of observation. In both cases, subjects showed change; moreover, it tended to involve the same extended period of mixed strategy usage, or variable performance, we found. A dissertation by Tivnan (1980) represents an even more radical variation of the present method. Tivnan studied slightly younger children learning to play a two-person game of strategy (“FOXand Geese”) in which Tivnan himself played the role of the second player. He did not undertake any explicit, didactic teaching of strategies, yet he made no attempt to avoid the influence of his own strategy usage as a model for the subject’s performance. Even under these quite different conditions, the period of variable strategy usage that was observed was similar to what we have reported here.

VI.

Discussion and Conclusions

The present results substantiate our earliest work (Kuhn & Angelev, 1976) in demonstrating that exercise of existing cognitive strategies is sufficient to effect their modification. We believe this finding has both theoretical and methodological implications. Let us begin with the methodological implications.

The Developmen/ A.

of

Problem-Solving Strategies

31

THE METHODOLOGY

We believe that the method illustrated in this article has significant advantages over the conventional intervention or “training study” method in yielding insight into the process of cognitive change. Clearly, the problems we posed to subjects constitute an intervention in the sense that subjects undergo a particular experience they otherwise would not have undergone. We would maintain nevertheless that a significant difference exists between interventions that consist of the presentation of problems, as ours did, and interventions that consist of the presentation of solutions (or strategies for solution) to problems, as does the conventional training study. The distinction is one of providing subjects opportunities to do what they already know how to do, versus trying to get them to do something different. Perhaps the strongest argument against the conventional method is that it has failed to provide decisive information regarding the way in which cognitive concepts or strategies change (Kuhn, 1974, 1978). To take the prototype of conservation attainment, despite the vast number of studies and the now widely accepted conclusion that training can induce conservation, a remarkable variety of divergent theories continues to exist regarding the process by which conservation develops (Acredolo, 1981; Anderson & Cuneo, 1978; Brainerd, 1979; Pinard, 1981; Shultz, Dover, & Amsel, 1979; Siegler, 1981). With regard to the issue of whether we have studied learning or development in the present work, we would like to take the position that this is in fact a pseudo-issue. In the past, developmentalists have held considerable investment in maintaining a conceptual distinction between learning and development. To erase it was presumably to accept the operant conditioning position that all development (and learning) is under the external “stimulus control” of the environment (Baer, 1973). But, as Flavell (in press) has put it, “Unlike the stereotype S-R learning theorist of yesteryear, today’s cognitive scientists attribute a great deal of complexity to the system that does the learning, to what it learns, and to the structure and processes that accomplish the learning.” Learning, in other words, like cognitive development, is now widely regarded as involving an organism-environment interchange characterized by a high degree of complexity and organization. Clearly, questions of generality, reversibility, and universality of change continue to be important ones. The similarities between processes labeled “development” and processes labeled “learning,” however, may turn out to be as important as the differences. Attributable in large part to the influence of Piagetian theory, emphasis in the field of cognitive development has been on the universality of developmental attainments, regarded in polarized contrast to the specificity of learned attainments. (Piagetian theory also has been the source of an assumption of universality with respect to the mechanisms or process of development, an assumption

38


which the results in this article likewise suggest may not be warranted.) All of the pertinent evidence, however, suggests that attainment of a cognitive strategy (such as, for instance, “isolation of variables”) is rarely if ever a completely general, that is, context-free, attainment. Rather, any cognitive attainment is wedded to a context in which it occurs, or as Fischer (1980, p. 478) put it in incorporating this point of view into his theory of cognitive development, cognitive attainments are “always defined jointly by organism and environment. ” In the case of our observations, we clearly were not observing the “once-andfor-all’’ acquisition of completely generalizable reasoning strategies. It would be a digression to argue the case here, but a rudimentary form of hypothesis testing is almost certainly present in children younger than our subjects. Conversely, the invalid strategies we observed, notably E2 and 13, most likely linger through adulthood, more prevalently probably in some adult thinkers than others. The achievements we observed, then, would appear to be ones that occur not once but many times over as the occasions for use of the relevant strategies arise in new and varied contexts. Indeed, all we had to do was complicate the problem slightly or modify its format and the invalid strategies typically reappeared. Whether we were observing a process of development or learning, then, is not resolvable by resorting to a criterion of generalizability (Kuhn, 1974); more important, this issue does not seem to us to be critical with respect to an attempt to study some of the major features of the process. If one subscribed to the view that cognitive achievements were completely general, one could undertake to induce an individual’s mastery of powerful strategies like isolation of variables or goal-recursion (Simon, 1975) in a single problem context, with the expectation that the individual then automatically would have these strategies available to apply in any appropriate context that arose. If one subscribes instead to the view we have taken here of “contextlinked cognitive development,” then a central aspect of any analysis of cognitive development or learning becomes one of analyzing how the (context-linked) capabilities the subject brings to the task interact with the demands posed by this specific task. Such a perspective may provide the most productive approach to Piaget’s problem of “dkcalage.” In the two observational studies of change mentioned in Section I, the analytic task is conceptualized in essentially this way. In describing their study of a subject learning to solve the Tower of Hanoi problem during a single 1% hour session, Anzai and Simon (1979) claim: Her ability to [form new and more effective strategies] depended on her having already available . . . some sophisticated learning capabilities and some prior knowledge of possible types of strategies (e.g., means-ends analysis). From her protocol, we can infer . . . ways in which prior knowledge was combined with new information gathered while solving the problem to contribute to the learning process [Anzai & Simon, 1979, p. 1301.

Lawler’s (198 1) study of one child’s development of mathematical concepts over

The Development of Problrrn-Solving Strutegies

39

a period of months similarly is devoted to the analysis of how concepts initially tied to specific contexts and experiences become applicable to new and broader task domains. B. THE FINDINGS

The findings described in this article have implications with respect to the understanding of causal reasoning. We shall defer discussion of these implications to elsewhere (Kuhn & Amsel, in preparation), however, so as not to detract from the central purpose of the present work, a study of the process of change. The data we have presented might be regarded within the framework of any of several different theoretical accounts of the process of cognitive development, for example, Case (1978), Fischer (1980), Pascual-Leone (1980), Piaget (1977), Vygotsky (1978), or, especially in light of the role of anticipatory representations suggested by the data, the “distancing” theory proposed by Sigel (Sigel & Cocking, 1977). Our purpose in this article, however, is not to discuss any of these theories in detail, but rather to provide data about developmental process that any of these theories would need to account for. What constraints, then, do the present data impose on a theoretical account of the process of cognitive development? Let us first review our findings. We have presented data on the performance of preadolescent subjects engaged in repeated encounters with what appears on the surface a very simple problem. The single most striking feature of these data is the variability in the strategies a subject applied to the problem, both within a session and across sessions. A most remarkable aspect of this variability is that a subject’s expertise or “insight” into the problem did not carry over from one session to the next. Repeatedly, we observed cases in which the subject “solved” the problem in a given session, in the sense of recognizing that a single element had been responsible for the outcome, and yet in the next session began again with the least advanced hypothesis, experimentation, and/or inference strategies, without any evidence of benefit from the insight that had been achieved in the previous session. Two quite different patterns of change were observed. The rarer pattern was characterized by an abrupt and dramatic change from invalid to valid strategy usage. In contrast, the predominant pattern of change involved an extended period of highly variable performance in which valid and invalid strategies were used in conjunction with one another and appeared to compete with one another for dominance. For these subjects, the post hoc recognition (following experimentation) that a single element had produced the effect (16) was not sufficient to effect a major change in their approach. Only if these subjects frequently applied “anticipatory schemes” to the data generated by the experiments (i.e., E3-E5) were they eventually successful in mastering the problem. Invalid strategies

40


rarely disappeared until this mastery was consolidated, that is, until the subject showed consistent usage of the valid efficient strategies. The first implication of these findings has to do with a supposedly methodological issue that has been the subject of much attention within the cognitive development literature: We refer to what has come to be called “method variance.” Abundant data are now available showing that seemingly superficial variations in a task often produce profound variations in performance. The variability we found in the performance of a subject encountering repeated presentations of the same task (as opposed to slightly different versions of a task) suggest the possibility that some of the variability in performance evident in the previous literature may in fact be attributable to the subject, rather than to task variation as has customarily been assumed. This possibility is clearly an important one to pursue. To the extent it is true, such variability becomes an important subject of substantive investigation, rather than a methodological source of error that the researcher seeks to eliminate. The remaining implications pertain to developmental theory. The present findings, we believe, underscore the need for a theory of development that encompasses both the development of competence and the development of performance, rather than a theory limited to the development of competence. For the most part, subjects in the present study possessed considerable competence in the advanced strategies necessary for successful problem solution. This competence, however, did not necessarily or automatically yield performance mastery, that is, stabilization at the level of valid efficient strategy usage, as was illustrated strikingly in numerous cases. What, then, does the development of performance mastery entail, if it is more than the development of competence? Our results suggested two sources of difficulty in achieving performance mastery. One was the need to perfect, or consolidate, the utilization of advanced strategies (as illustrated in the cases of S3, S6, and S9 in Section IV). The other was the discarding of less adequate strategies. Virtually all of the attention in developmental psychology has been devoted to the development of new strategies or behaviors, rather than the abandonment of old ones. The present findings, however, suggest that the second of these two achievements may pose the more formidable challenge, which is a reversal of the way we usually think about development. Thus, one might think of the process leading to performance mastery as composed essentially of consolidation or perfection of strategies through practice, and clearly such consolidation is at least in part what was achieved through a subject’s repeated engagement with the problem in the present study. Our findings imply that something more is involved, however. The problem we posed to subjects is one in which lower level strategies requiring relatively superficial processing of the presented data compete with higher level strategies requiring more extensive, complex processing, the kind of problem Pascual-


41

Leone (1980) characterized as invoking his “F” f a ~ t o r Our . ~ study of individuals’ repeated performance on such a problem we believe points to the importance of “metastrategic” knowledge of what strategies are effective for a given problem, in contrast to strategic knowledge of how to execute effective (or ineffective) strategies (Kuhn, 1983). If, in the course of an encounter with the problem, subjects were doing no more than gaining practice in the application of a set of strategies to the problem, strategy use would remain relatively constant rather than change. They are also gaining knowledge about the problem and, in particular, knowledge about their own strategies as they apply to this problem. In short, to put it in the terms we did earlier, they are gaining understanding of how the capabilities they bring to the task bear on the demands posed by the task. In a problem such as the one used in the present research, the metastrategic knowledge to which we are referring includes knowing that the most advanced strategy is the preferred strategy to apply to the problem, that is, knowing that this strategy works, exactly how and why it works, and why it is the best strategy to use. In addition, it includes comparable knowledge with regard to each of the less efficient and/or invalid strategies-that they do not work (or do not work efficiently), why they do not work, and what errors they lead to. We would suggest, then, that during that period preceding performance mastery, a subject was achieving not only perfection of advanced strategies through practice but in addition was acquiring the kind of metastrategic knowledge referred to above. Furthermore, it was this latter knowledge, we would speculate, that played a strong contributory role in the subject’s eventual stabilization at the level of valid efficient strategy usage and, particularly, in the subject’s abandonment of the less adequate, invalid strategies. If we are correct, one finds it less surprising that so long a time usually elapsed between first appearance of an advanced strategy and stabilization at the valid efficient strategy level, for the metastrategic knowledge we have indicated is considerable in both amount and complexity. Our interpretation also makes understandable the fact that the invalid strategies were rarely discarded until this stabilization was achieved-until from the subject’s perspective the subject “had conviction about” what he or she was doing. Most important, if we are correct, then any theoretical account of developmental process must incorporate both components-strategic and metastrategic-in its account. The simple fact that individuals do modify their strategies during the course of repeated encounters with a problem, in the absence of instruction or other external influence, points to the important role the latter may play. In the case of the present problem, it would have been relatively easy to teach the strategic, as opposed to metastrategic, knowledge necessary for mastery. 4Pascual-Leone (1980) defines F as “an organismic factor . . . somewhat analogous to the Gestaltist Field factor, or Prugnanz. ”

42


Subjects easily could have been instructed to try each element in isolation, and to some it may seem pointless to observe subjects grappling with the problem over so long a period while withholding this simple bit of instruction. It is unlikely any subject would have had difficulty following such an instruction. Yet, following it, and thereby employing the advanced solution strategy, is very different from understanding its significance. Just this gap is what would appear to be in large part responsible for the widely observed “generalization gradient”: The less similar the transfer situation is to the original one, the less likely is the subject to apply the newly-learned strategy, even though it is equally applicable and necessary in the new situation (Glaser, 1981). In the present research we deliberately chose a problem in which the noninstructed context is the typical one: Individuals do not routinely receive formal instruction in the bases for inferring causality. In such noninstructed contexts, the second, metastrategic kind of knowledge referred to above is what will determine whether or not an adequate strategy is applied. We conclude with this final point: The difficulty experienced by many of our subjects in mastering the problem we posed to them serves as a humble reminder of a fact occasionally forgotten by social scientists-“evidence” does not exist in a body of data itself, it exists in the eye of the beholder. Recall these data generated by S10 in her final session: F+ and FDC+. Most of us, were we to encounter these data, would not hesitate in infemng that in the second mixture F was responsible for producing the outcome. S10, as we saw, simply did not see things that way, which suggests the importance of our seeking to see them her way. ACKNOWLEDGMENTS The authors wish to acknowledge the contributions of Victoria Ho to the present work. Thanks are also extended to Noel Capon, whose expertise in chemistry provided an essential contribution. The studies described in this article were financed by private support, which we wish to acknowledge with gratitude.

REFERENCES Acredolo, C. Acquisition of conservation: A clarification of Piagetian terminology, some recent findings, and an alternative formulation. Human Development, 1981, 24, 120-137. Anderson, N. H., & Cuneo, D. 0. The height and width rule in children’s judgements of quantity. Journal of Experimental Psychology: General, 1978, 107, 335-378. Anzai, Y., & Simon, H. A. The theory of learning by doing. Psychological Review, 1979, 86, 124-140.

Baer, D. M . The control of developmental processes: Why wait? In J . Nesselroade & H. Reese (Eds.), Life-span developmental psychology: Methodological issues. New York: Academic Press, 1973.

The Development of Problem-Solling Strategies

43

Bindra. D., Clarke, K . M., & Shultz, T . R . Understanding predictive relations of necessity and hufficicncy in formally equivalent "causal" and "logical" problems. Journal of E.rperimental Psvchologv: General. 1980, 109, 422-443. Book, W . The psvchologv of skill. Missoula, Montana: Montana Preas. 1908. Brainerd, C . J. Markovian interpretations of conservation learning. P.yho/ogiccf/R e t * i e ~ t ,1979, . 86, 181-213.

Case. R . Intellectual development from birth to adolescence: A neo-Piagetian interpretation. In R . S. Siegler (Ed,), Children's thinking: What develops;) Hillsdale, New Jersey: Erlbaum, 1978. Commons. M.. & Davidson, M. Patterns of change in pcrformance over practice trials without feedback in a causal inference task. Manuscript in preparation. Fischer, K. A theory of cognitive development: The control and construction o f hierarchies of skills. Psvchological Review. 1980, 87, 477-53 I . Flavell, J. H. Structures, stages, and sequences in cognitive development. In W. A. Collins (Ed.), Minnesota Symposium on Child Psychology (Vol. 15). Hillsdale, New Jersey: Erlbaum, in press Fornian. E. The role qf colloborarion i n prohlem-solvinsq in children. Unpublished doctoral dissena(ion, Harvard University, 1981. Glaser, R . The future of testing: A research agenda for cognitive psychology and pbychometrics. Atnericun Psychologist, 198 I , 36, 923-936. Inhelder. B . , & Piaget, 1. The xrowrh of l o g i r d thinkingfrom childhood to adolescwfc~e.New York: Basic Books, 1958. Kuhn. D. Inducing development experimentally: Comments on a research paradigm. Dcwlopmental P.s~holo~qY, 1974. 10, 590-600. Kuhn. D. Mechanisms of cognitive and social development: One psychology o r two? Humrm Development. 1978, 21, 92-1 18. Kuhn. D. On the dual executive and its significance In the development of developmental psychology. In D. Kuhn & J . Meacham (Eds.), On the development o f de\~elopmentalpsvchology. Basel: Karger, 1983. Kuhn, D., & Anisel. E. Causal inference in niultivariable contexts. Manuscript in preparation. Kuhn, I).. 8i Angelev. J . An experimental study of the development of formal operational thought. Child Deldopment, 1976. 47, 697-706. Kuhn. D.. & Brannock. J . Development of the isolation of variables scheme in experimental and "natural experiment" contexts. Dewloptnental P.s~cholog,v.1977. 13, 9- 14. Kuhn. D . , 8i Ho. V. Self-directed activity and cognitive development. Journal of'Applied Devrlopholog?l. 1980. I , 119-133. Lawlcr. R . The progressive construction of mind. Cognitiw Science. 1981, 5 , 1-30. Lewis, L. The effects of adult questioning on students' aryuisition ($the isolation and control of' variables concept in a se/fdirected learning context. Unpublished doctoral dissertation, Teachers College, Columbia University, 1981, McCall, R . Challenges to a science of developmental psychology. Child Development, 1977. 48, 233-344. Moshman, D. S. Development of formal hypothesi\-testing ability. Dev~lopmental P.r\.c.holo,q,v. 1979, 15, 104-112. Pascual-Leone. J . Constructive problems for constructive theories: The current relevance of Piaget's work and a critique of infomiation-processing simulation psychology. I n R . Kluwe & H . Spatla (Eds.), Developmental models cf thinking. New York: Academic Press, 1980. Piaget. J . The der,elopment of thought; Eyuilihrution of cognitive StructureS. New York: Viking, 1977. Piaget. J . . 8i Garcia. R . Understanding c u i t s u l i ~ .New York: Norton, 1974. Pinard, A. The conserilation of conserwtion: The chiltl's ucytisition nf (I firn~lumentalc o i u q ) t . Chicago: University of Chicago Press, 1981


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Pitt, R. B. Toward a comprehensive model ofproblem solving: Applications to solutions of chemistry problems by high school and college students. (Doctoral dissertation, University of California, San Diego, 1976). Dissertation Abstracts International, 1977, 37, 4730. Shaklee. H., & Mims, M. Development of rule use in judgments of covariation between events. Child Development, 1981, 52, 317-325. Shaklee, H., & Tucker, D. A rule analysis of judgments of covariation between events. Memory and Cognition. 1980, 8, 459-467. Shultz, T. R. & Butkowsky, I. S . Young children’s use of the scheme for multiple sufficient causes in the attribution of real and hypothetical behavior. Child Development. 1977, 48, 461-469. Shultz, T. R., Butkowsky, 1. S., Pearce, J., & Shanfield, H. Development of schemes for the attribution of multiple psychological causes. Developmental Psychology, 1975, 11, 502-5 10. Shultz, T. R . , Dover, A,, & Amsel, E. The logical and empirical bases of conservation judgments. Cognition. 1979, 7, 99-123. Shultz, T. R., & Mendelson, R. The use of covariation as a principle of causal analysis. Child Development. 1975, 46, 394-399. Siegler, R. S . Defining the locus of developmental differences in children’s causal reasoning. Journal of Experimental Child Psychology, 1975, 20, 512-525. Siegler, R. S. The effects of simple necessity and sufficiency relationships on children’s causal inferences. Child Development, 1976, 47, 1058- 1063. Siegler, R. S . Developmental sequences within and between concepts. Monographs qf the Society for Research in Child Development, 46, 1981 (Serial No. 189). Siegler. R. S., & Liebert, R. Effects of contiguity, regularity, and age on children’s causal inferences. Developmental Psychology, 1974, 10, 574-579. Sigel, 1. E., & Cocking, R. R. Cognitive development from childhood to adolescence: A constructivist perspective. New York: Holt, 1977. Simon, H. A. The functional equivalence of problem-solving skills. Cognitive Psychology, 1975, 7, 268-288. Snyder, M., & Swann, W. B. Hypothesis-testing processes in social interaction. Journal of Personality and Social Psychology, 1978, 36, 1202-1212. Tivnan, T. Improvements in performance on cognitive tasks: The acquisition of new skills by elementary school children. Unpublished doctoral dissertation, Harvard University, 1980. Tschirgi, 3 . E. Sensible reasoning: A hypothesis about hypotheses. Child Development, 1980, 51, 1-10,

Vygotsky. L. S. Mind in society: The development of higher psychological processes. Cambridge, Massachusetts: Harvard Univ. Press, 1978. Wason, P.C. & Johnson-Laird, P. N. Psychology of reasoning. Cambridge, Massachusetts: Harvard Univ. Press, 1972.

INFORMATION PROCESSING AND COGNITIVE DEVELOPMENT

Robert Kail DEPARTMENT OF PSYCHOLOGlCAL SCIENCES PURDUE UNIVERSITY WEST LAFAYETTE, INDIANA

Jejfrey Bisanz PSYCHOLOGY DEPARTMENT UNIVERSITY OF ALBERTA EDMONTON, ALBERTA, CANADA

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45

11. A GENERIC INFORMATION-PROCESSING SYSTEM:

DEFINING THE METAPHOR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. PRETHEORETICAL ASSUMPTIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B . CORE CONSTRUCTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

48 4x

49

111. AN INFORMATION-PROCESSING LOOK AT RESEARCH ON COGNITIVE DEVELOPMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. DEVELOPMENT OF THE KNOWLEDGE B A S E . . . . . . . . . . . . . . . . . . . . . . . B. ATTENTIONAL RESOURCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

52 53 60

IV. THE ISSUE OFTRANSITION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A . INCREASES IN ATTENTIONAL RESOURCES . . . . . . . . . . . . . . . . . . . . . . . . B. KNOWLEDGE-MODIFICATION PROCESSES . . . . . . . . . . . . . . . . . . . . . . . . . C. A TRANSITIONAL SYSTEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

62 63 65 66

V. ADDITIONAL ISSUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. COMPARISON OF CRITICAL PRESUPPOSITIONS . . . . . . . . . . . . . . . . . . . . B. TASK SPECIFICITY.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

68 69 73

VI. CONCLUDING REMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75

16

I. Introduction Our goal in this article is to characterize information processing as a general framework for understanding human cognitive growth. The information-processing perspective has had enormous impact on the study of cognition, and over the 4.5 ADVANCES IN CHI1.D DEVtLOPMtNT AND BI,HAVIOR. VOL 17

Copyright 0 1982 by Academic Press. Inc All rights of reproducuan in any form reserved. ISBN 0-12-009717-6

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Robert Kail and Jeflrey Bisanz

past decade it has been adopted by a growing number of developmental psychologists. Journals and books now contain numerous articles about the development of information-processing skills in children, in sharp contrast to the recent past when such topics were only rarely mentioned in such an authoritative source as Carmichael’s Manual of Child Psychology (Mussen, 1970). Our view is that information processing, as a general perspective, has considerable potential for developmental work, and our intent is to describe some relevant characteristics and implications of information processing. Not all developmental psychologists share our enthusiasm for information processing and some categorically reject information processing as a developmental framework. A brief history of the information-processing tradition within developmental psychology may help to explain this state of affairs. When computers first became widely available to the scientific community in the early 1950s, they were viewed primarily as high-speed number manipulators. By the mid- 1950s, development of programming languages like FORTRAN led to the realization that computers were general symbol manipulators and not limited to just numbers. Newell, Shaw, and Simon (1958) were among the first to argue that humans, like computers, could be seen as general systems for processing symbolic information, and that knowledge of computer processes could be used to explore potentially similar mental processes. Over the next decade these notions were gradually assimilated into experimental psychology, such that during the 1960s numerous psychological theories and programs of research were based on the new computer metaphor. Familiar examples would include the work of Atkinson and Shiffrin (1968) on memory, Deutsch and Deutsch (1963) and Norman (1968) on attention, Neisser (1967) on perception, and Clark (1969) on psycholinguistics. Information processing was not influential in developmental psychology until the late 1960s and early 1970s, although Simon had outlined some informationprocessing ideas about cognitive development as early as 1960 (Simon, 1962). During this period two essentially independent events brought information processing to the forefront of developmental research. First, psychologists studying the development of attention and memory based their work, in part, on information-processing models derived from experimental psychology. Hagen’s (e.g., Hagen & Hale, 1973) well-known work on the development of selective attention, for example, drew upon Broadbent’s (1958) filter theory. Similarly, much of the work done in the early 1970s on the development of memory strategies (e.g., Hagen, Jongeward, & Kail, 1975; Ornstein, 1978; Reese, 1973) showed the influence of Atkinson and Shiffrin’s (1968) model of memory. Second, several psychologists from the information-processing tradition-notably Klahr and Wallace (1970) and Trabasso (Bryant & Trabasso, 1971)-became interested in Piaget’s description of children’s understanding of concepts like transitivity and class inclusion. These psychologists proposed radically different

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interpretations of the phenomena, and their research sometimes produced findings that were hard to reconcile with Piaget’s account of development. As the influence of information processing in developmental psychology grew in the 1970s, so did criticisms of the framework. Two general criticisms are often cited. First, information-processing models of cognition are often viewed as static entities that cannot adequately represent dynamic aspects of development. As Brown (1982) noted, “A system that cannot grow, or show adaptive modification to a changing environment, is a strange metaphor for human thought processes which are constantly changing over the life span of an individual.” Second, information-processing constructs are often viewed as inadequate for characterizing general structures of thought that transcend task-specific performance. Breslow (1981), for example, argued that “it is not clear . . . that information-processing theory, with its current focus on the real-time processing involved in particular tasks, can adequately describe the nature or development of cognitive structures that are abstract and pervasive” (p. 349). These criticisms are not so much wrong as they are misdirected. Certain information-processing models do indeed appear static and task-specific. But we would argue that these are shortcomings of particular theories and, more generally, of the current state of information-processing research. Information processing is a framework, not a theory, and criticisms such as those noted above do not necessarily apply to all theories that fall within the realm of information processing. The approach indeed differs in fundamental ways from the structuralist metatheory that encompasses the work of Baldwin, Werner, and Piaget. However, both Piagetian and information-processing theorists share the goal of formally characterizing human cognitive skills in a way that will illuminate their development. Piaget chose formal logic and verbal description to represent thought; information-processing psychologists have chosen to use the modern digital computer as a metaphor for representing human thought. Misconceptions about information processing exist, at least in part, because it has not been described as a general framework for developmental research. Case (1974), Klahr and Wallace (1976). Pascual-Leone (1970), and Reese (1973) have provided the most detailed analyses of information processing in a developmental context, but they described specific information-processing theories rather than a general approach. Siegler (in press) and Sternberg and Powell (in press) briefly discussed information processing as a developmental framework, but their primary purpose was to review research. Lachman, Lachman, and Butterfield (1979) provided an excellent and comprehensive account of the information-processing approach but did not consider developmental issues. It would be impossible to review, in a single article, the range of informationprocessing concepts and methods that are potentially relevant to developmental phenomena. Instead, we focus on the broad characteristics of the approach, and examine the insights it provides into issues of development. We begin, in Section

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11, by outlining the basic assumptions of an information-processing approach to cognition and describing a generic system that encompasses most current theories. In Section 111, we use the generic system as a framework for selectively reviewing research on cognitive change. Next, in Section IV, we suggest a general transitional system that might account for the changes described in Section 111. In Section V , we consider additional characteristics of information processing as a developmental framework.

11. A Generic Information-Processing System: Defining the Metaphor A.

PRETHEORETICAL ASSUMPTIONS

The theories about human information processing that have proliferated over the past 20 years are quite diverse, but most information-processing psychologists share a small set of beliefs about human thought that are best characterized as pretheoretical. As such, these beliefs are not strictly testable in an empirical sense. Instead, they may be evaluated as being more or less useful in advancing our knowledge of human thought. Specific theories and their implications for development often appear inconsistent unless these pretheoretical assumptions are specified, and so we describe below some of the fundamental beliefs associated with the information-processing perspective. (Additional assumptions regarding development arc described in Section v.) First, information processing is a cognitive psychology. Psychological acts of “knowing,” broadly defined, constitute the subject matter of information-processing psychology. As a cognitive psychology, information processing is not concerned primarily with mapping relationships between stimuli and responses. Instead, the focus is on specifying mental activities and properties that intervene between stimulus and response. Second, adherents to information-processing argue that the similarities between human cognition and computer operations are substantial enough to allow researchers to use the computer productively as a metaphor to study human thought. In particular, human cognition, like the operation of a computer, is viewed as the manipulation of symbolic information (Newell et al.. 1958). Hence, computer-based concepts and formalisms can be used to represent important characteristics of human thought, and our knowledge of computer operations can then serve as a source of hypotheses about human cognition. The strategy of using a well-understood system to analyze another, less understood system has been advocated in other contexts (Lorenz, 1974; Miller, 1956; Reese & Overton, 1970; Teitelbaum, 1977) and is certainly not unique to information processing. Computer operations serve not only as a source of hypotheses but also as a

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representational medium. Because they are extremely flexible, computer-based representations can be modified to incorporate useful concepts about cognition that do not originate in computer-based work, such as the ideas of “spreading activation” and “working memory.” Third, cognition involves distinct activities operating in concert. In principle, cognitive activities can be decomposed into a number of different components, which in turn can be decomposed further. A relatively small number of distinguishable components are assumed to underlie all mental activities. “It is one of the foundation stones of computer science that a relatively small set of elementary processes suffices to produce the full generality of information processing” (Newell & Simon, 1972, p. 29), and the same is believed to be true for information processing in humans. This belief does nor imply that cognition is to be understood only in terms of reduction into ever more microscopic components. Rather, information-processing psychologists believe that understanding human cognition will involve both ( a ) identifying elementary cognitive processes and (b)determining how such processes are structured to perform a selected cognitive task. Thus, information-processing psychologists believe that elementary processes can be distinguished but that these elementary processes, in isolation, are insufficient to account for human cognition. Fourth, cognitive activities require some amount of time, even if the activities are simple and effortless and if the interval of time is so small as to be imperceptible to an individual. Duration is assumed to be a salient and direct reflection of underlying cognitive processes, and identifying the temporal structure of such processes is an important aspect of information-processing research (Schweickert, 1980; Sternberg, 1969). Finally, many aspects of human cognition are viewed as active and constructive. This is not a statement about the ultimate source of motivation for thought; rather, the point is that human cognition is not stimulus-bound in any simple and pervasive sense. Mental processes and goals are structured internally and may well generate novel constructions and initiate stimulus-seeking interactions with the environment. In no way does the computer metaphor imply that thought is fundamentally passive and reactive. (In Section V,A we discuss this issue in greater detail.) B.

CORE CONSTRUCTS

Pretheoretical assumptions about the nature of thought have been translated into many specific information-processing theories, yet most theories embody a relatively small number of concepts that we might call the core constructs of the information-processing approach. In particular, theories of information processing nearly always include statements about representation, process and limited attentional resources.

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Consider representation first. Information must be represented internally for processing to occur. This information may originate externally, such as an object in the environment, or may already exist internally in another form. Objects or events are not represented directly; instead, symbols are used to designate an object or event. The nature of these symbolic representations may vary widely. For example, DOG may be represented by its orthographic characteristics (e.g., the features of D, 0, and G), or by its acoustic characteristics (rhymes with FOG). Representations can vary in level of abstraction as well: A particular dog might be represented as “Otto,” a “dachshund,” a “dog,” or an “animal.” The precise nature of symbolic representation has important implications for cognitive theories and considerable effort is spent on specifying varieties of representational formats. Processes are mental activities that generate, transform, or manipulate representations. For example, the stimulus “35 27” will be interpreted or encoded as numerical symbols related by an arithmetic operator. Subsequent processing of these symbols-such as adding the values in the ones column, carrying a unit to the tens column, adding values in the tens column-will result in another internal representation, “62,” and still other processing may lead to a written or spoken response. In each case processing is, essentially, manipulating symbols. Processes may operate in sequence, as in the present example, or simultaneously. Representations and processes together form a system of knowledge, or “knowledge base.” This system of knowledge is sometimes referred to as longterm memory or store, terms that we will avoid because they tend to connote a specific structure or location in the mind where knowledge resides. The important structural dimensions of knowledge vary considerably among theorists. For example, Paivio (1971) proposed a dual code theory in which knowledge included both visual images and verbal memories. Tulving (1972) distinguished episodic memory (knowledge of temporally tagged events that is organized autobiographically) from semantic memory (knowledge of words, symbols, relations, and procedures for manipulating this knowledge). Anderson (1976) distinguished knowledge of particular facts or events from the dynamic strategies and procedures that underlie various intellectual activities. Despite discrepancies among various theories, most theorists generally agree on the general properties of the human knowledge system. (a)There are, theoretically, no limits on the quantity of knowledge that can be stored. (b)Knowledge is not lost; “forgetting” reflects an inability to access knowledge. (c) Most knowledge can be accessed by multiple routes and multiple cues, reflecting the fact that knowledge is rich in interconnecting links. (6)Knowledge is characterized by a weak form of cognitive economy. Not all of one’s knowledge about concept X need be associated directly with X (Collins & Loftus, 1975). Instead, some of this knowledge is available only indirectly, via inference. (e) A process can operate on itself as well as on other representations and processes.

+

The number and complexity of representations that can be stored in the knowledge base are virtually limitless, but only a very limited subset of knowledge is “active” (i.e., involved in processing) at once. Active memory, primury memo?, and short-term memo? all refer to the small portion of the knowledge base that is involved in processing at any given moment. The term uttentiond resources refers to a limited reserve of resources used to activate knowledge, that is, to initiate and maintain knowledge in an active state. Some contents of the knowledge base do not require attentional resources; their activation is triitomutic and may be initiated by external stimuli or by other currently active contents. Other contents require the allocation of attentional resources to become active; in these cases, processing is referred to as voluntary or controlled. The major advantage of automatic processing is that it places minimal drain on available attentional resources, thus leaving more for the operation of controlled processing. Automatic processing tends to be fast and resource-efficient because multiple processes can operate more or less simultaneously. Its major drawback is that it is “involuntary” in that its initiation and execution are not easily modified. In contrast, controlled processing is slower and less efficient: because it requires attentional resources, only a limited amount of controlled processing is possible at any given time. The main advantage is that such processing can be inhibited or its sequence can be modified. Thus flexibility is characteristic of controlled processing, but at the expense of reducing available attentional resources. To illustrate these concepts, consider the act of solving number series problems. The task is to identify the “rule” that determines the order of the numbers and to use that rule to generate the next number in the series. A:24682 B: 1 2 4 7 ’ According to an information-processing analysis, solution begins by creating a program or control structure that may lead to solution. The first process is to represent the numbers of a series internally. Subsequent processes will be performed on these number-representations to induce the correct rule. In the case of Problem A, solution will usually be rapid and virtually automatic, presumably because numerous exposures to the series “ 2 , 4, 6, 8” in other contexts has made it an easily recognized pattern that is represented internally as a particular series rather than as a set of discrete numbers. Indeed, most aduIts would find it hard not to think of 10 when presented such a familiar series. In contrast, solution of Problem B usually will require more controlled processing in the form of sequential generation and testing of hypotheses. A variety of hypotheses may be attempted and, depending on available information and on processing biases, the solver may flexibly alter or terminate this hypothesis testing. The induced rule would be represented internally (e.g., “increment the last number by the

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RECEPTOR PROCESSES

KNOWLEDGE

Fig. I . A representation of a portion of the knowledge base for a generic mn/~~rmation-process in^ system. Open circles correspond 10 active nodes and closed circles to inactive nodes.

sum of one plus the difference between the last two numbers”) and used to determine the number in question. Many of these core constructs are illustrated in Fig. 1. Interactions between the knowledge base and the environment are conducted via receptor and effector processes. Contents of the knowledge base are represented by nodes (circles) that are connected by labeled links. Nodes denote either representations or processes, and labels describe the relationship between two linked nodes. For example, the node for “dachshund” might be connected to a node for “dog” by an isa link denoting “is a member of the category.” Unfilled circles indicate active nodes, processes that are ongoing, or representations that are being processed. Darkened circles depict inactive nodes. The concepts described to this point constitute the components of a generic information-processing system that is specific enough to be easily distinguished from other kinds of theories, yet is sufficiently general that it is fundamentally compatible with major information-processing accounts of cognition, such as those of Anderson (1976), Collins and Loftus (1975), Kintsch and van Dijk ( 1978), Newel1 and Simon (1972), and Norman and Bobrow (1976), among others.

111. An Information-Processing Look at Research

on Cognitive Development A conceptual framework provides a distinctive way for a psychologist to view the organism and hence determines, to a great extent, the questions that must be answered to “understand” that organism. A conceptual framework is thus “a unique ‘window’ through which reality is experienced. . . . The adoption of any

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scientific window is accompanied by the conventional acceptance of certain kinds of events as notable and recordable” (White, 1976, p. 102). The information-processing framework described in Section I1 is one such window and our primary purpose in this section is to provide a glimpse of research on cognitive development through this window. For more detailed reviews, see Siegler (in press) and Sternberg and Powell (in press). A second important function of this review is to provide background information needed for our discussion (in Section IV) of developmental transitions. Information processing is not intrinsically a developmental framework: The root of the information-processing metaphor-the computerdoes not necessarily develop. Given this state of affairs, many information-processing psychologists who are interested in development adhere to Klahr’s (1976) strategy that “the more precisely one states his model of what a stage is, the more precisely can one state a theory of the transition process itself” (p. 101). More generally, by identifying patterns of change common to numerous cognitive domains, information-processing psychologists hope to derive a system that accounts for cognitive change. A.

DEVELOPMENT OF THE KNOWLEDGE BASE

Mental representations and processes that form the knowledge base have a complementary relationship: Processes act on representations, and representations are accessed via processes. This complementarity means that representations and processes cannot, in principle, be studied independently (Anderson, 1976, 1978). In practice, to make most research problems tractable, investigators make somewhat arbitrary decisions about what is process and what is representation. [See, for example, Anderson (1976), Norman, Rumelhart, and the LNR Research Group (1975), and Newell and Simon (1972) for different decisions in this regard.] We follow this convention here for expository convenience.

I . The Development of Cognitive Processes As noted in Section II,A, a fundamental tenet of information processing is that a reasonably small number of elementary information processes underlie performance on diverse cognitive tasks. Hence, an important immediate goal for information-processing research is to identify ( a ) the processes that are involved in cognitive performance, and ( b ) the organization of those processes. One anticipated outcome of this research is a large catalog of complex cognitive procedures but a relatively small set of elementary processes that underlie these procedures. Within this broad characterization of cognitive processes, two types of cognitive change have been of special interest to information-processing psychologists. a . Change in procedures. As children grow they resort to different methods of solving problems. This insight is hardly unique to information-processing

54


psychology. Where information processing has made a unique contribution is in identifying these various procedures with greater precision than was the case previously. Furthermore, because procedures are specified more precisely, information-processing psychologists can identify patterns of procedural change across tasks. One such pattern involves use of increasingly suficient procedures. Research by Siegler (1976, 1978, 1981; Klahr & Siegler, 1979) on children’s understanding of the balance scale problem is illustrative. Children and adolescents were shown a balance scale in which weights were placed at various distances to either side of a fulcrum. Individuals were to decide which side of the balance scale-if either-would go down when supporting blocks were removed. Siegler (1976) identified a set of developmentally ordered procedures or “rules” that were used to make these judgments. All 5- and 6-year-olds used a rule (Rule I) in which only the number of weights was considered. If the weights were equal on the two sides, the child predicted that the scale would balance; if the weights were unequal, the child predicted that the side with the greater number of weights would go down. The 9- and 10-year-olds used two different rules. The simpler of the two (Rule 11) was a variant of Rule I: As before, when the number of weights on the two sides was unequal, children alway predicted that the side with more weights would go down. When the weights were equal, however, children no longer predicted that the scale would balance. Rather, they evaluated the distance of the weights from the fulcrum and then made accurate predictions. The other rule used frequently by 9- and 10-year-olds (Rule Ill) involved consistent consideration of both weight and distance. This rule is inadequate only in that it does not provide a means of evaluating conflicts arising when independent evaluation of weight and distance lead to different predictions regarding which side of the balance should go down (i.e., it does not include a rule for computing and comparing torque). Rules I1 and I11 were both used by 13- and 14year-olds, but Rule I11 was used by more than twice as many individuals as Rule 11. Finally, among 16- and 17-year-olds, a few individuals used Rule 11, most used Rule 111, and some used a modification of Rule 111that included procedures for computing and comparing torque (Rule IV). In short, Siegler (1976) demonstrated that between 5 and 17 years of age individuals use ever more powerful rules for dealing with balance scale problems. Similar developmental change has been documented for several other Piagetian tasks (Siegler, 1981; Siegler & Richards, 1979), for the rule used to determine the distance between objects in large-scale spaces (Allen, 1981), and for rules used to determine the difficulty of memory tasks (Hale & Kail, 1982). In each case, the earliest procedures are partially correct, allowing young children to solve many classes of problems accurately. Developmental change involves elaborating these procedures in ways that increase the scope of proficient problem solving.

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Another kind of change involves use of more eflicient procedures (e.g., Day, 1975). Children abandon inefficient procedures in which component processes are executed repeatedly in favor of routines that sometimes have more component processes but that minimize iterative processing. Put another way, repeated implementation of less powerful processes gives way to more powerful processes. Such a pattern of change was reported by Naus and Ornstein (1977), who studied children's search of active memory using the Sternberg (1 966) paradigm. Students in grades 3 and 6 were shown sets of 2, 4, or 6 stimuli, followed immediately by a single stimulus. The child's task was to report whether the single stimulus was included in the immediately preceding set. Of particular interest are trials in which half the stimuli were digits and half were consonants. Adults typically use the categorical structure of a list to direct search of active memory. In particular, adults use the random entry search algorithm depicted in Fig. 2 (Naus, Glucksberg, & Omstein, 1972). Adults randomly select a category (digits or consonants) and compare the probe stimulus with each element of that category. If the person happens to select the category of the probe stimulus, he or she responds after comparing the probe only with the elements of that category. If the person selects the category that does not include the probe, then the probe is compared with all stimuli presented. The random entry algorithm can be contrasted with an exhaustive search algorithm in which all elements of the

TARGET

SELECT CATEGORY

1 CHANOE

COMPARf

CATfGORV

WITH I n M I

MATCH7

"YES" CATEGORY7

& ,.*f"

"NO-

(b)

Fig. 2 . Two algorithms used to scan subspan lists of stimuli. ( a ) Random search algorithm; Exhnustive search algorithm.

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Robert Kail and Jeffrey Bisanr

stimulus set are always searched. The random entry algorithm is the more efficient procedure of the two, requiring fewer comparisons than the exhaustive search algorithm. Naus and Ornstein (1977) found that sixth-graders, like adults, used the more efficient random entry algorithm and third-graders used the exhaustive search algorithm.

b. Change in the speed with which component processes are implemented. Efficient algorithms are valuable in that they reduce constraints on processing due to limited resources. Another way to cope with limited resources is to execute processes more rapidly so that resources can be allocated to other activities sooner. Research we have conducted over the past few years (e.g., Bisanz, Danner, & Resnick, 1979; Kail, Pellegrino, & Carter, 1980) illustrates an information-processing approach to developmental changes in processing speed. Several studies (Carter, Pazak, & Kail, 1981; Kail et al., 1980) have concerned children’s ability to anticipate the appearance of an object from different spatial perspectives. One goal of this research was to determine the development of spatial processing during late childhood, adolescence, and young adulthood.* A method developed by Cooper and Shepard (1973) was used in which individuals were shown two versions of an unfamiliar, letter-like stimulus on each trial. The “standard” stimulus was presented in an upright position, and the second, “comparison” stimulus was rotated 0 to 150” from the standard. On some trials the comparison stimulus was identical to the standard; on other trials it was a mirror image. Individuals were to decide, as rapidly as possible, whether the two stimuli would be identical or mirror images if they were presented at the same orientation. One algorithm for making these judgments, depicted in Fig. 3, was described by Cooper and Shepard (1973). First an individual represents the stimuli in working memory, encoding their forms and orientations. The person then rotates the mental representation of the comparison stimulus to the orientation of the standard. Next, the rotated mental representation is compared with the standard. If they are identical an individual responds “same”; if not, a small additional amount of time is needed to implement a response of “different.” In fact, most individuals between 9 and 19 years of age appear to transform stimuli according to the algorithm described in Fig. 3 (Carter e t a f . , 1981; Kail et al., 1980). Unlike the case with many of the algorithms discussed earlier, this aspect of spatial processing seems to be characterized by invariance in the modal algorithm, at least through late childhood, adolescence, and young adulthood. A ‘Relative facilitation decreases as the number of categories increases because the probability of randomly selecting the probe category drops. ZAnother objective of this research was to determine the information-processing bases of individual differences in spatial aptitude. This aspect of the research is described in Pellegrino and Kail (1982).

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STIMULI

COMPARISON STIMULUS

(

DIFFERENT

)

Fig. 3. An algorithm for mentullv rotuting tuo stitnuli into congruencc.. (After Cooper und Shepurd, 1973.)

consistent pattern of developmental change was also found in speed of processing. According to the Cooper and Shepard (1973) model, the slope of the function relating response time to the orientation of the comparison stimulus provides an estimate of the rate of mental rotation. Rate of mental rotation decreased by approximately 4 mseddegree between 9 and 13 but less than an additional millisecond per degree between 13 and 19 years. Speed of mental rotation appears to asymptote during late childhood or early adolescence. c . Summary. In this section we have illustrated some common patterns of developmental change regarding procedures. As children develop, they may use increasingly sufficient and increasingly efficient procedures. In some cases the same procedures are used by individuals of different ages, but components of these procedures are performed more rapidly with increasing age.

2 . Development of Representations To focus on the process aspects of cognition, investigators often assume a particular type of representation. For example, Naus and Ornstein (1977) assumed that younger and older children do not differ in the way they represent digits and letters. When stimuli become more complex, or when processing appears to be developmentally invariant but performance nevertheless varies with age, such

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simplifying assumptions may not be warranted and the question of developmental change in representations arises. In our generic system, knowledge is represented as a network in which conceptually related items are linked associatively. Figure 4 represents a detailed example of a portion of a network that concerns knowledge about animals. This type of network conforms closely to a theory proposed by Collins and Loftus ( 1975) but does not seriously compromise other theories. Several characteristics are noteworthy. First, nodes in the network designate concepts rather than words. Thus the node DALMATION could be activated just as readily by seeing a picture of a dalmation as by reading the word “dalmation.” Second, associations between nodes are labeled: An association not only connects two concepts but also indicates the nature of that relation. Third, the length of the line represents the strength of the association, with shorter lines indicating greater strength. Fourth, properties are associated only with the most general applicable proposition and not with more specific propositions, indicating a strong form of cognitive economy. A structure such as that depicted in Fig. 4 includes elements (i.e., concepts) and relations between concepts. Developmental change in both elements and relations have been studied by information-processing psychologists.

h n

is.

F i g . 4. A porrion of the knowledge base concerning animals.

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a. Change in elements. An information-processing analysis leads us to consider two forms of development with respect to elements. First, the number of elements increases, as evidenced by the acquisition of new vocabulary items and concepts throughout the life span. Second, and of greater interest, the content of a representational unit or “chunk”4efined as a portion of the knowledge base that is always activated and deactivated as a unit-appears to increase with development. Older children seem to include more information per chunk than younger children do (Simon, 1974). For example, developmental differences in performance on memory tasks are often substantial when material is presented in meaningful units, but not when material is presented randomly (Mandler & Robinson, 1978). One explanation of this finding is that older children’s representation of the material to be remembered consists of fewer chunks than does younger children’s representation of the same material, with the result that older individuals must retrieve fewer chunks during recall. b. Change in relurions. Cognitive development is often characterized as a change from more perceptually based representations to more conceptually based representations (e.g., Bruner, 1964; Clark, 1973). Evidence for this general conclusion has been obtained from a variety of sources, including classification tasks (Annett, 1959; Denney & Moulton, 1976; Howard & Howard, 1977; Kagan, Moss, & Sigel, 1963; Saltz, Soller, & Sigel, 1972) and memory tasks (Bach & Underwood, 1970; Bisanz, Pellegrino, Kail, & Siegel, 1978; Melkman & Deutsch, 1977). For example, when young children form groups of similar objects, they often do so on the basis of perceptual similarity. Thus, 6-year-olds may say that apples and peaches are alike because they are round. Older children are more likely to invoke superordinate relations to explain similarity. This developniental trend can be discussed in terms of the type of knowledge structure depicted in Fig. 4. Younger children’s knowledge of the domain of fruits may be represented by the diagram in Fig. 5a. Both apples and peaches are lipked to fruit and to roundness, but the strength of the association to perceptual properties is greater. Links to fruit are included because young children can place objects in appropriate categories when asked (e.g., Nelson, 1974). Development of the knowledge structure to this point can be characterized in three ways. First, elements must have been acquired. Second, relations denoting perceptual properties (has, is) and categorical membership (isa) have been acquired and established between appropriate nodes. Third, the pattern of elements and relations has been coordinated so that both PEACH and APPLE have the same superordinate, FRUIT, as opposed to FRUIT1 versus FRUIT2. Thus, development of the representation in Fig. 5a involves changes in elements, relations, and patterns among relations. Figure 5a can be contrasted with the older child’s representation, shown in Fig. 5b, in which apples and peaches are again linked to both fruit and round, but


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F7 FRUIT

(a)

Fig. 5 . Portions of the knowledge base concerningfruifs. ( a ) The knowledge of a 5-year-old,for whom peaches and apples are alike primarily because they are both round. ( b ) The knowledge of an 8-year-old,for whom peaches and apples are similar primarily because they are both fruits.

with greater strength (indicated by shorter distances) to fruit. Thus the important developmental change is that category relations (isa in Fig. 5 ) become stronger relative to property links ( i s , has). To complete this account, we need to provide a set of processing mechanisms. We can speculate that when an individual forms groups of objects he or she (a)finds the nodes in the knowledge base corresponding to the two objects, ( b ) determines if they share a common node (or nodes), and (c) if multiple common nodes are found, he or she uses the node with the greatest associative strength as the basis for a response. c . Summary. Developmental researchers have begun to use the information-processing framework to clarify issues related to representation (e.g., Duncan & Kellas, 1978), but questions about process still dominate the literature. Hence, in describing developmental research on representation we have illustrated how information-processing concepts can be applied to such traditional topics as memory and classification. Information-processing theory and research have been extended recently to the representation of very complex forms of knowledge, such as stories and events (Abelson, 1981; Kintsch & Van Dijk, 1978; Nelson, 1978; Stein & Glenn, 1979; Voss, Tyler, & Bisanz, 1982), and we expect this trend to continue. B . A'TTENTIONAL RESOURCES

Another category of developmental change is the amount of attentional resources available for activating contents of the knowledge base. Most cognitive tasks impose some degree of load or demand in the sense that some attentional resources must be allocated for appropriate performance. Tasks vary in the load

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they impose. For example, consider the arithmetic problems “3 X 4” and “27 X 13.” When solved without the aid of external devices (such as a pencil or calculator), the second problem requires greater attentional resources than the first because the solver must simultaneously ( a )activate more processes (such as multiplication, addition, and “carry” operations), ( b )retain more information in an activated state, such as intermediate products and sums, and (c) activate an overall strategy for coordinating these various activities and pieces of information. In contrast, solution of “3 x 4” is virtually automatic for individuals who are proficient in arithmetic. Because total attentional resources are limited, an individual’s performance on two, simultaneous and attention-demanding tasks will deteriorate if the load imposed by one of the tasks increases (Baddeley & Hitch, 1974; Kahneman, 1973). For example, a moderately proficient musician could probably sight-read a piece of music while solving simple arithmetic problems or remembering a new telephone number. Given problems like “27 X 13,” however, we would expect performance to decline on one or both of the tasks; sufficient attentional resources would probably not be available to perform both tasks adequately, unless one or both of the solution processes were sufficiently automatized, as might be the case for an expert sight-reader (Hirst, Spelke, Reaves, Caharack, & Neisser, 1980). Load can be increased to a point where successful performance on a single task becomes impossible. For example, solving “347 x 127” would be impossible for most people even if they knew how to solve it, unless they were able to discover a resource-efficient strategy or to use external devices. Attentional resources are often studied by recording latencies for task A when it is performed separately and when it is performed concurrently with task B. If task B requires attentional resources, then latencies for task A should be longer in the simultaneous condition than in the separate condition; the difference would presumably reflect the time for additional controlled processing. In contrast, if task B does not impose additional load, no difference would be expected. Presumably, the more efficient the information-processing system is in handling additional load, the smaller the difference should be for task A latencies in the two conditions. This approach is illustrated in an experiment by Manis, Keating, and Momson (1980), who asked 7-, 11-, and 20-year-olds to respond to an auditory probe while solving a visual letter-matching task. Compared to a control condition in which only the auditory probe was presented, individuals at all ages responded more slowly when the two tasks were performed simultaneously. Moreover, the effect varied as a function of when the probe occurred in the letter-matching task: Interference was generally low during earlier phases of information processing on the matching task and greater during later phases. The 7-year-olds showed greater interference than 11- and 20-year-olds during all phases of processing, but 1 I-year-olds were slowed more than adults only during later phases. These results suggest that available attentional resources may increase with age.

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Robert Kail and Jejjrey Bisanz

More generally, a person with greater available resources may be more likely to engage successfully in the flexible and attention-demanding kinds of mental activity that typify “complex” reasoning and problem solving (Bruner, 1970; Case, 1978a,b; Pascual-Leone, 1970). For example, the sensorimotor period is often characterized by the infant’s inability to fuse temporally successive movements and perceptions into an integrated representation. With development the infant becomes progressively able to integrate more actions and events, thus permitting more complex skills to develop. Young children who attempt to solve conservation and class inclusion problems often fail because they “center” on one source of information and ignore others; success comes when they incorporate more information into their decisions. Similarly, solutions to formal operational tasks, such as combinatorial reasoning, often require that a number of stimulus factors be considered simultaneously and incorporated into a general solution strategy; failure to process all the relevant stimuli will result in an inadequate solution. These examples are all drawn from Piaget’s (e.g., 1960) work, and indeed much of the impetus for research on the role of attentional resources has arisen from the need to expand and supplement Piaget’s theory (Case, 1978b). These examples and recent research (Case, 1978a,b; Case, Kurland, & Goldberg, 1982; Manis et d.,1980) are all consistent with the hypothesis that attentional resources increase with age. [For alternative views on the nature of attentional resources, see Chi (1978) and Trabasso and Foellinger (1978).]

IV. The Issue of Transition Information processing is not intrinsically a developmental framework and consequently does not come equipped with an explicit mechanism to account for cognitive change. Hence, providing such a mechanism or set of mechanismswhat we call a “transitional system”-is obviously a priority if information processing is to be considered seriously as a developmental framework. A precise description of an adequate transitional system would necessarily be tied to a particular theory of cognition and is therefore beyond the scope of this article. However, given the broad theoretical framework described in Section 11 and the changes identified in Section 111, we can speculate on the general characteristics of an adequate transitional system for information-processing theories of cognitive development. Two general considerations guided the formulation of the transitional system presented here. First, the system should be organized internally to account for continued developmental change. A system that results in only a single transition is inadequate because cognitive development does not necessarily stop after a single increment or adjustment. Second, an adequate transitional system, in

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interaction with a normal environment, must generate a course of developmental change that is, in its general form, highly probable (Siegel, Bisanz, & Bisanz, in press; Waddington, 1969). A system that fails to meet this second requirement is incapable of accounting for invariant sequences in development. The framework formulated to meet these needs involves two general components: ( a ) increases in attentional resources and (b) procedures to modify the knowledge base. Similar components have been proposed separately elsewhere (Anderson, Kline, & Beasley, 1979; Case 1978a; Klahr & Wallace, 1976; Pascual-Leone, 1970), but neither alone is sufficient to address the two considerations described above. We suggest that attentional resources and knowledgemodification processes can be viewed as integral components that interact to ensure continuous and directed cognitive development. We first describe each component separately and then show how they are integrated in the transitional system. A . INCREASES IN ATTENTIONAL RESOURCES

As noted in Section 11, limited processing resources are central to informationprocessing theories because these resources are needed to activate knowledge and to maintain knowledge in an activated state. Given this crucial role in the cognitive system, increases in the availability of attentional resources constitute a plausible source of cognitive de~elopment.~ Such increases could occur in any of several ways. According to growth hypotheses, the total amount of attentional resources increases with development (Case, 1974, 1978b; Pascual-Leone, 1970). For example, Pascual-Leone (1970) devised a theory to explain cognitive growth by means of a “hidden parameter”: the size of central computing space M which increases in a lawful manner during normal development. The general structural characteristics of the piagetian stages would then be interpretable as qualitative manifestations of this internal computing system or M operator. (p. 304)

In Pascual-Leone’s (1970) system these increments occur in discrete steps. As can be seen in Table I, M increases by one unit biennially between 3 and 16 years of age. Another possibility is that increases in attentional resources are continuous and gradual. For example, developmental increases in digit span and in memory scanning speed (as studied with the Sternberg, 1966, paradigm discussed in Section II1,A) appear continuous (Kail, 1982). The growth curves are sigmoid in nature: Both digit span and scanning speed increases rapidly during childhood and continue to increase during adolescence, but at a much slower rate. The )Similarly, a decrease in processing resources might be responsible for some of the cognitive changes associated with aging, a possibility examined in detail by Salthouse and Kail (in press).

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TABLE I M Values at Different Developmental Levels Age (years)

Piagetian substage

M value

~~~~~

3 4 5-6 7-8 9-10 11-12 13-14 15-16

Early preoperational Late preoperational Early concrete operational Late concrete operational Early formal operational Middle formal operational Late formal operational

a a a a a a a

+ 1* + + + + + +

2 3 4 5 6 7

*The value of a is constant across ages and refers to the capacity necessary for implementing an executive routine that satisfies task instructions. (Adapted from Case, 1972.)

similar growth curves may well reflect a common underlying mechanism, such as continuous growth in processing resources. [See Wilkinson (198 1) and Wilkinson, DeMarinis, & Riley (in press) for related arguments.] Contrary to growth hypotheses, the total amount of processing resources may remain constant. Under certain conditions and with repeated use, resource-demanding processes may become increasingly automatic (e.g., Shiffrin & Schneider, 1977), thus freeing some resources. Similarly, subsets of information in the knowledge base may be combined to form larger units or “chunks” of information (e.g., Simon, 1974), thus permitting more total information to be activated with a given amount of resources. According to automatization hypotheses, these “local” changes in processes and representations have system-wide implications. Processes and representations that are recurrently activated actually require fewer attentional resources. Thus the amount of available resources may increase even if the amount of total resources is constant. Developmental improvements in performance are made possible by automatization and the concomitant increase in availability of resources (Bruner, 1970; Case, 1978a; Case, Kurland, & Goldberg, 1982). As an illustration of the differences between the two types of attentional hypotheses, consider the Manis et al. (1980) experiment (discussed in Section II1,B) in which individuals responsed to an auditory probe while determining if two letters matched. With development, performance on the letter-matching task was less disrupted by the probe. According to a growth hypothesis, children’s attentional resources increase with development, and so the residual resources available for responding to the auditory probe (i.e., those resources remaining after allocation of resources to the letter-matching task) are greater for older individuals who, consequently, are better able to perform both tasks simultaneously. According to an automatization hypothesis, the processes involved in the letter-matching and probe tasks are more likely to be automatized in older

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individuals, thereby decreasing the joint demands on limited attentional resources and again enabling older individuals to perform better. This example illuminates an important difference between the two types of hypotheses. According to growth hypotheses, the increase in attentional resources represents a change in a fundamental parameter of the cognitive system, such as M in Pascual-Leone's (1970) theory. As such, the increased resources should be apparent in performance on all resource-demanding tasks. Automatization hypotheses, in contrast, do not predict a uniform pattern of developmental change. Instead, age-related change is expected only when task-relevant processes are more likely to be automatized in older individuals. When younger and older children have adequate strategies for performance on a resource-demanding task but younger children have more automatic processes or larger chunks of information (due to greater experience with the task), then older children could be expected to perform more poorly than younger children (e.g., Chi, 1978). Growth and automatization hypotheses are not mutually exclusive and both may be involved in cognitive development. B . KNOWLEDGE-MODIFICATION PROCESSES

In Section III,A we catalogued a variety of developmental changes in the knowledge base. Given the heterogeneity of change discussed there, we need to identify procedures for modifying the knowledge base that are sufficient to account for these changes. Here we describe basic procedures at a general level in terms of (a) their functions, or effects on the knowledge base, and (b) the circumstances that evoke these procedures. One such function involves addition or deletion of nodes and their relational links in the knowledge base. These additioddeletion processes are necessary to account for new concepts and relations in the system, and for modifications in existing concepts. For example, adding superordinate and subordinate relations could convert several synonynous concepts (Otto, dachshund, dog) into an integrated structure (Otto isa dachshund isa dog). The system must also be capable of a second basic function, strengrhening or weakening links between nodes. Strengthening of relations can be viewed as a structural equivalent of automatization. Greater strength implies that activation of two related nodes (processes or units of information) requires fewer resources. For example, repeated processing of configurations of n stimuli may involve a process that strengthens connections among the nodes; ultimately the configuration could form a single chunk rather than n separate chunks. The additioddeletion and strengthedweaken functions can be applied to processes as well as to representations. Consider a process, A , that consists of the components A I , A2, A 3 , and A4 that are executed serially. We can consider these components to be nodes of the knowledge base related by links that define their

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temporal order of execution. An additioddeletion process would be needed to alter A to make it more sufficient; for example, A3 might be deleted and A5 added, and thus A might subsequently be more accurate and have a wider range of application. In addition, links among components could be strengthened, thus rendering A less resource-demanding and faster. Thus, additioddeletion and strengthedweaken processes are essential to account for a variety of developmental changes, including new or different representations and processes as well as increased speed of processing. Having provided the transitional system with these knowledge-modifying functions, we need procedures for determining that a change is needed. We assume that addition/deletion and strengthedweaken functions would be directed by analyses of feedback from both the environment and from within the system. One such analysis involves inconsistency detectors, processes that monitor the results of other, ongoing processes and compare them with each other, with internally specified goals, or with external events. Consider problem solving as an example. The outcome of a particular solution process may be disconfirmed by information from the environment (e.g., Kendler, 19791, or the results of two different processes may be inconsistent with each other (e.g., Inhelder, Sinclair, & Bovet, 1974). When an inconsistency is detected, the additionldeletion and strengthedweaken functions are activated until the inconsistency is reduced or eliminated. This notion of inconsistency detectors parallels Piaget’s ideas about “disequilibrium” and “equilibration” and is related to the widely used concept of “match” between cognitive structures and environmental events (e.g., Hunt, 1961). More recently, Anderson, Kline, and Beasley (1979) have provided a more detailed, information-processing theory of how inconsistencies might be detected and resolved. A second analysis involves regularity detectors, processes that monitor other, ongoing processes for recurrent, resource-demanding regularities. Such regularities include repeated use of a sequence of controlled processes or repeated activation of a set of representations. When recurrent regularities are detected, knowledge-modification processes are activated until a representation or process is modified so as to reduce the demand for resources. Regularity detectors are also useful for identifying redundant processes (e.g., Klahr & Wallace, 1976) so that more efficient procedures can be constructed. These detectors may also determine when two different procedures produce the same result, a characteristic that would be important for constructing general processes that transcend operations learned in, and limited to, a particular domain (e.g., Lawler, 1981). C. A TRANSITIONAL SYSTEM

Knowledge-modification processes and increases in attentional resources are linked in our transitional system by a simple assumption: Knowledge-modifica-

Infb,mation

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Performance Monitors : Regularity Detectors

/ Increased Availability of

Knowledge-Modification

Anontional Resources

Processes

A

I

Growth in Total Attentions1 Resources

I

Fig. 6 . A proposed transitional system involving chunges in altrntional resources and change in procedures 10 mod(fi the knowledge base.

tion processes place heavy demands upon available resources. Thus detection of inconsistencies and recurrent regularities does not necessarily result in changes to the knowledge base; sufficient resources must be available. Sufficient resources might become available through ( a ) automatization of appropriate contents in the knowledge base or ( b ) growth in total capacity that occurs independently of changes in the knowledge base. These general features are indicated in Fig. 6. To this point we have described a “circle” of effects, such that knowledgemodification processes and increased resources enable each other. Such a system could result in a steady state, at least in principle. This result is unlikely, however, if we assume that changes to the knowledge base alter the ways in which the system investigates or interprets its environment (e.g., Neisser, 1976), which it, turn alters the internal and external feedback monitored by inconsistency and regularity detectors. Stated differently, changes to the knowledge base enable the system to identify inconsistencies and regularities that were previously undetectable. Because some representations and processes change with each cycle, this transitional system results in a succession of states, rather than maintenance of a single state. Thus the system is organized internally to account for continual developmental change. Developmental theorists have traditionally stressed the importance of identifying the major regulatory principles that characterize the general course of development (Bertalanffy, 1967; Overton, 1976; Siege1 et al., in press; Teitelbaum, 1977). Werner’s (1 957) principles of “differentiation” and “hierarchic integra-

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tion” are prominent instance^.^ In the present system, constraints on the direction of developmental change are provided by the processes that monitor performance of the system. Detection of inconsistencies propels the transitional system to generate more suficient representations and processes. Similarly, detection of recurrent regularities propels the system toward more eficient representations and processes. Furthermore, the relationship between resources and modification of knowledge guarantees that performance requiring more attentional resources will be acquired later, generally, than performance requiring less resources. Each of these characteristics of developmental change is highly consistent with the data reviewed in Section 111. Thus our transitional system implies at least three regulatory principles: Information processing becomes more sufficient, more efficient, and more “complex” in terms of the resource demands of performance. Many important details have been ignored in our description of a transitional system. For example, we need to identify heuristics that might be used to generate more efficient processes and representations. Mechanisms are also needed to store and evaluate information about the frequency with which certain processes and representations are activated, so that recurrent regularities can be detected. Furthermore, the question of whether the monitoring processes themselves develop, or are invariant, needs to be addressed. Although our sketch of a possible transitional system is incomplete, we believe that it constitutes a framework for (a) accounting for the changes described in Section 111, and (b) providing a scheme that guarantees a general course of cognitive development.

V. Additional Issues Given an information-processing “window” on cognition (Section II), we have described some aspects of cognitive change (Section III), and we have outlined a transitional system that can account for some important characteristics of these changes (Section IV). In the present section we examine the information-processing framework from a broader perspective. Some of the basic tenets of information processing have specific implications when used in developmental work. To illuminate these implications we consider two critical issues. In so doing we seek to highlight important similarities and differences between information-processing theories and other theories of cognitive development. “With a few exceptions (e.g., Case, L978a; Klahr, 1976; Klahr & Wallace, 1976), the notion of regulatory principles or constraints has been ignored by developmental psychologists who use the information-processing framework. This oversight may have resulted because (a) the intersection of developmental psychology and information processing is relatively new and (b) information processing is not an inherently developmental framework. In Section IV we showed that informationprocessing concepts are sufficiently flexible to incorporate and represent developmental constraints.

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COMPARISON OF CRITICAL PRESUPPOSITIONS

As a way of comparing an information-processing perspective to other views on cognitive development, we examine it on several criteria used to differentiate world views that currently dominate developmental psychology. Reese and Overton (1970) identified two such world views, the mechanistic and the organismic views. Our characterizations of these views are necessarily brief and selective; for more thorough analyses, see Overton (1976), Overton and Reese (1973), and Reese and Overton (1970). Our goal is not to force a fit between the information-processing framework and one of these world views; rather, we seek to clarify some of the presuppositions that influence information-processing theories. The mechanistic world view incorporates the machine as a basic metaphor for human activity and is best exemplified by specific theories in the S-R tradition. The organismic world view uses a living organism as its metaphor and is best exemplified by the theories of Werner (1948) and Piaget (1971). The relation between these world views and information-processing theories is not entirely clear. For example, Reese and Overton (1970) suggested that the computer, the root metaphor for information processing, is fundamentally mechanistic, but Reese ( 1973) claimed that certain components of an information-processing theory of memory are nonmechanistic. To avoid needless confusion, the world view that dominates information-processing theories needs to be examined more closely.

I . Assumptions about Activity a . Organismic and mechanistic views. A fundamental difference between the mechanistic and organismic world views concerns assumptions about activity in the organism. According to the mechanistic view, the organism is inherently at rest. Like a machine, this passive organism shows activity only in response to external forces or antecedent conditions; spontaneous activity is merely epiphenomenal. According to the organismic world view, the organism is assumed to be inherently active. The organism not only responds to the environment, it actively interprets and or modifies its environment. Whereas “a passive organism . . . receives form from its experience,” an active organism “gives form to its experience” (White, 1976, p. IOO), so that “environmental event and organism stand in a relationship of reciprocal action in which each member affects and changes the other” (Overton & Reese, 1973, p. 79). Related to the active-passive distinction are assumptions about the nature of explanation. The explanatory framework for mechanistic theories has two components (Overton & Reese, 1973). Material cause refers to anatomical and/or physiological substrates of psychological phenomena, and efficient cause refers

I0


to antecedent events that “cause” subsequent reactions in the organism. The former concerns the current state of the organism, but the latter concerns change and is thus given priority in developmental work. As a consequence of accepting the concepts of efficient cause and a passive organism, causality is unidirectional: Environmental events impinge on the organism and cause changes, but not vice versa. Thus “change in the . . . behaviors of the organism is not seen as resulting from change in the structure of the organism itself” (Reese & Overton, 1970); indeed, the idea of internal structure is unimportant for explaining development. In contrast, when the organism is active and interprets the environment, efficient, unidirectional causality is not a sufficient framework for explaining behavior; two additional components of explanation are emphasized. The first is formal cause, by which psychological activity is explained in terms of its form, pattern, or structure. If the organism is assumed to be inherently active, then “the flux of behavioral appearances is the given and it is necessary to establish or construct or represent stability in the face of change”; thus “construction of an organization (structure) is necessary to generate a conceptual stability and completely explain the phenomena under consideration” (Overton, 1976, p. 81). In Piaget’s theory, description of developmental stages constitutes an example of formal cause. The second major component of explanation is final cause, by which activity is characterized in terms of its developmental course. In developmental psychology, Werner’s principles of “hierarchic integration” and “differentiation” are frequently invoked to characterize developmental change (Siege1 et al., in press) and serve as a final cause in explaining development. Formal and final causes are seen as regulatory principles that are fundamental to understanding organismic change and are not simply derived from material and efficient causes.

b. Information-processing assumptions. Given that a computer is a machine and the root metaphor of information-processing theories, one possible conclusion is that information-processing theories are fundamentally mechanistic. Thus the organism or system would be assumed to be passive. Such an inference would be misleading, however, because the computer metaphor includes characteristics of both the machine and the programs associated with the machine. This latter component is enormously flexible and precludes any facile classification of the metaphor. A more useful test would be to determine if information-processing theories contain concepts more consistent with one world view than the other. To accomplish this type of evaluation, we follow Overton’s (1976) suggestion that differences in assumptions about the active or passive nature of the organism are particularly salient in theories of perception and motivation.

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Consider perception first. A fundamentally passive organism is portrayed merely as a recipient of information from the environment, but an active organism transforms stimuli during the act of perception: “Active organisms have purposes and they attend, reason, and selectively perceive. All of this enables the active organism to select, modify, or reject environmental influences pressing upon it” (White, 1976, p. 100). Information-processing theories of perception contain components that are active and components that might be called passive. Perception is indeed selective and is guided, in part, by goals and heuristics that result from voluntary (i.e., resource-demanding) processes. The knowledge base is used to interpret the world, and perception is considered to be partly constructive. Some processes (automatic processes) are more “reflexive” in response to environmental stimuli and thus might be considered mechanistic in nature. However, even these processes operate in the context of a complex, organized internal system that is more characteristic of organismic than mechanistic theories. Thus, information-processing theories of perception involve a complex system of both “active” and “passive” processes. Efficient and unidirectional causality (the influence of environmental stimuli) is balanced against, and integrated with, formal causality (the structure of the knowledge base that contains processes of perception). Perhaps a more informative basis of comparison would be the topic of motivation. As Overton (1976) noted, “the very concept of ‘motivation’ implies the question of what gets the organism moving and such a question has meaning only to the extent that one believes that the lack of activity is fundamental” (p. 83). Theories positing internal or external forces that stimulate an otherwise inactive system presuppose a passive organism, and theories that require no such forces presuppose an active one. In fact, information-processing theories and research have proliferated without much consideration of “what gets the organism moving” (Simon, 1979), suggesting that an active organism is presumed.5 Motivation can be defined not only as “what gets the organism moving,” but also as the process of selecting between competing tendencies or purposes (e.g., Atkinson, 1964). The former definition is clearly consistent with the assumption of a passive organism because it underscores the critical nature of antecedent events (efficient causes), but the latter definition is more consistent with an active metaphor. Given the traditional importance of selectivity in information 50ne argument is that computer-based models are mechanistic because the computer itself is basically inert and has to be “plugged in.” At least with large installations, “plugging in” is not part of a user’s experience with a computer and thus is not a concept commonly related to computer usage. More importantly, the information-processing framework does not require an analog to the electrical power used by a real machine. The metaphoric and representational use of the computer extends to its operations and organization but does not include its physical characteristics, such as electrical requirements or composition of semiconductors.

12


processing research (Broadbent, 1958; Kahneman, 1973), we suspect that when information-processing theories begin to include the idea of “motivation,” the emphasis will be on selectivity rather than internal drives or external forces.

2. Stages and Change As noted in Section V,A,I, the assumption of inherent activity requires that a

theorist identify the nature of development by comparing successively abstracted stages (Siege1 et a l . , in press). Significant changes are assumed to be qualitative (changes in form or kind) rather than quantitative (changes in degree or amount). In mechanistic theories, all change is viewed as fundamentally quantitative, and the appearance of qualitative change in the structure of the organism is epiphenomenal (Overton & Reese, 1973; Reese & Overton, 1970). Concerning the necessity of stages, the information-processing framework is more consistent with the organismic world view. As indicated in Section 111, theorists must describe successive states of the information-processing system in order to characterize development. However, two important differences must be noted between information-processing and organismic concepts of stage (Kail & Bisanz, 1982). The first difference concerns the distinction between qualitative and quantitative change, concepts which have a long and confusing history in developmental psychology (Flavell, 197 1; Flavell & Wohlwill, 1969). Unlike organismic theorists, information-processing theorists are generally open to the possibility that developmental change may have both qualitative and quantitative components. Moreover, the “qualitative” or “quantitative” nature of change may depend on the level of analysis employed. For example, a qualitative-looking change in a mnemonic strategy may be an outgrowth of quantitative-looking change in available attentional resources, which in turn may be due to a “qualitative” reorganization of subprocesses that enables more resource-efficient performance. None of these changes is inherently more important than the others, and all must be included in a coherent and complete account of development. The second difference concerns the generality of postulated stages or states. In organismic-developmental theories, stages are viewed as relatively stable and pervasive. They are stable in that they characterize cognition over long periods of time; they are pervasive in that they describe cognition as it is manifested on a wide variety of tasks. For example, in Piaget’s theory a “stage” of concrete operational thought spans several years and represents a child’s cognitive skills in a variety of contexts. Both stability and pervasiveness can be accommodated in information-processing theories and they enhance the generality of such models. But stable and pervasive stages are not a necessary part of an informationprocessing approach to development. Some aspects of performance may show stability and pervasiveness while other aspects may not. Indeed, a major criticism of Piaget’s theory, from the point of view of information processing, is that

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the influence of task-specific factors is often ignored (e.g., Case, 1978b; Pascual-Leone, 1970). In information processing, general aspects of performance (those that are stable and pervasive) and task-specific characteristics of performance are both of importance. Despite these differences, an information-processing account of cognitive development, like organismic theories, will necessarily include specification of ( a ) successive states of the system (formal cause) and ( b )constraints on the course of development (final cause), such as those outlined in Section IV. In full detail, it will also describe how environmental events (efficient cause) and physiological substrates (material cause) contribute to developmental change. 3 . Conclusions We have used metatheoretical distinctions between the mechanistic and organismic world views to clarify presuppositions and possible ambiguities of the information-processing framework. On the whole, information-processing concepts seem more compatible with the concept of an active organism. However, the active-passive distinction corresponds to the theoretical distinction between controlled (resource-demanding) and automatic processes, and in this sense the information-processing framework may be viewed as “eclectic,” in that concepts related to both world views are used. The framework also includes a commitment to states or stages, but qualitative change is not the sine qua non of development. Although Pepper (1942) claimed that eclecticism is confusing and should be avoided, Reese (1973; Reese & Overton, 1970) suggested that eclecticism can be useful if the components are theoretically distinct. White’s ( 1 965) temporal stacking theory of learning, which contains both associative and cognitive components, is a well-known instance of such an eclectic theory. In the informationprocessing framework, similarly, the “organismic” and “mechanistic” components are distinct but together form a coherent and powerful cognitive system. B. TASK SPECIFICITY

Some developmental psychologists (e.g., Breslow, 1981; Youniss & Furth, 1973) argce that, in information processing, performance on specific tasks is emphasized while aspects of cognition that transcend these tasks are ignored. If such a bias were inherent to information processing, it would be a severe shortcoming that precludes the approach as a general developmental framework. To concretize our discussion of the task-specificity criticism, consider how one might seek to understand a certain cognitive ability, such as inductive reasoning. At the most general level we would want a theory that explains induction in a way that is independent of the context of any particular task. At a more specific level we would want a theory that would account for performance on a

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particular kind of inductive task, such as the series completion problems described in Section 11. At an even greater level of specificity, we would want a theory to explain performance on a particular variant of series completion, such as series of numbers, letters, or geometric figures. Information-processing psychologists who try to understand inductive reasoning would, as the critics have noted, be more likely to begin their research by studying one of the specific variants of the task. This decision would reflect two related beliefs. First, information-processing psychologists believe that a precise model of performance on a single, complex task is more likely to reveal important features of thought than is a general but ill-specified model. That is, most information-processing psychologists believe that cognitive psychology can progress most rapidly with “a series of experimental and theoretical studies around a single complex task, the aim being to demonstrate that one has a sufficient theory of a genuine slab of human behavior. All of the studies would be designed to fit together and add up to a total picture in detail” (Newell, 1973, p. 303, emphasis added). The second reason for the task-specific emphasis stems from the relationship between general knowledge structures and more specific processes or representations. To begin, critics often suggest that the task-specific emphasis within information processing neglects the phenomena of greatest importance to developmental theory and reifies the trivial. Breslow (1981) notes that while information processing has a strong task-specific component, “Structural developmental theory, in contrast, has been concerned with pervasive, abstract structures that apply to a broad range of tasks . . . and to long-term temporal phenomena in the form of structural change. . . . A task i s only of interest insofar as it does require a certain concept for its solution” (p. 348, emphasis added). The information-processing response to this criticism is that the cognitive structures of interest to structural developmental theory cannot be studied independently of performance on specific tasks. Cognitive structures cannot be measured per se; they can only be measured by activating them and recording their behavioral consequences. Consequently, our understanding of these structures can only be as deep as our understanding of the procedures by which those structures become active. That is, models of performance on specific tasks are seen as necessary precursors for more general theories of intellectual competence. For this reason information-processing has had and will continue to have a strong task-specific component.6 6As noted in Section V,A,2, a frequent countercriticismof structural developmental theories is that they are too vague to generate specific predictions regarding performance on particular tasks, and hence that the theories are untestable. Klahr and Wallace (1972) remarked that

On the one hand, we have Inhelder and Piaget’s theoretical account and, on the other, the complex set of results obtained from the experimental studies. A gap exists between the

Finally, nothing in the information-processing commitment to task-specificity is antagonistic to the creation of general cognitive theories. In fact, developing such broad theories is an important part of the scientific agenda for informationprocessing psychology. General information-processing theories have perhaps been less conspicuous than task-specific models, but they exist in sufficient numbers (Anderson, 1976; Case, 1978a; Klahr & Wallace, 1976; Newell & Simon, 1972; Sternberg, 1977) to dispel any notion that information-processing psychology is inherently “limited to the characterization of surface manifestations, that is, of real-time performance on particular tasks” (Breslow, 1981, p. 348).

VI.

Concluding Remarks

We began by describing a generic information-processing system that incorporates characteristics of many information-processing theories. This system also provided a basis for a selective review of research on cognitive development. We then outlined a transitional system that would result in continued, orderly development. Finally, the information-processing framework was considered with respect to several key developmental issues to clarify its relation to other theoretical perspectives. Now that the information-processing framework has been described in some detail, questions and issues that are central to the perspective can be distinguished from those that are not. Speed of processing, for example, is a central topic because it is related to automatization and may reflect the level of resources available for activation of knowledge-modification processes. The distinction between qualitative and quantitative change, so critical in some theories, is of less importance in information-processing theories: Any particular developmental change may have both qualitative and quantitative components; moreover, whether the change is characterized as qualitative or quantitative often depends hypothetical structures and processes which form the basis of the theory and the level of perforniance as represented by the experiniental data. This arises from the fact that the theoretical account is presented at a level of generality which makes it uncertain as to whether it is sufficient to account for the complex and varied behavior i t purports toexplain. Indeed, there is n o way at all of determining what would be i t 5 consequences on the level of performance. A much inore detailed account of the functioning of specific processes is necessary before these uncertainties can be dispelled. (p. 154) Similarly, Brainerd (1981 1, in discussing the structural-developmental literature on concept acquisition, wrote “If we aim to discover when some concept appears and what its developmental ordering is relative to other concepts. it is self-evident that we shall first have to know what it means ‘to have the concept’ in the sense of what processes are responsible for performance. But as a rule, this is precisely what we do not know” (p. 465).

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on the level of analysis employed. Distinguishing central issues from peripheral issues is an important task for optimizing the interaction between traditional developmental approaches and information-processing theories. Identifying the issues and characteristics of the framework also helps to establish an agenda for research on cognitive development. For example, research on the development of procedures and representation is required that specifies principles of processing that apply across tasks. Related to this concern, research is needed that identifies the boundary conditions under which different developmental patterns are found. In addition, a number of questions need to be answered that concern operation of the proposed transitional system. In particular, the growth and automatization hypotheses need to be distinguished, as well as the conditions under which each may contribute to development. Similarly, the interaction between automatization and knowledge-modification processes needs to be clarified, as does the role of regularity and inconsistency detectors in processing feedback. Research addressed to questions like these will transform the general framework into more specific theories of cognitive development. ACKNOWLEDGMENTS Preparation of this article was made possible, in part by grants to the first author from NIMH (MH-34137), NlNCDS (NS-17663). and the Purdue Research Foundation, and to the second author from the Natural Sciences and Engineering Research Council of Canada. We wish to thank Gay Bisanz, Fred Morrison, and Hayne Reese for helpful comments on an earlier draft of this article.

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RESEARCH BETWEEN 1950 AND 1980 ON URBAN-RURAL DIFFERENCES IN BODY SIZE AND GROWTH RATE OF CHILDREN AND YOUTHS

Howard V. Meredith BLATT PHYSICAL EDUCATION CENTER UNIVERSITY OF SOUTH CAROLINA COLUMBIA. SOUTH CAROLINA

I . INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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11. RETROSPECT: 1870-1915 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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111. DIFFERENCES IN STANDING HEIGHT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A . COMPARISONS FOR LATE CHILDHOOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . B . COMPARlSONS FOR EARLY ADOLESCENCE. . . . . . . . . . . . . . . . . . . . . . . . C . COMPARISONS FOR LATE ADOLESCENCE . . . . . . . . . . . . . . . . . . . . . . . . .

86 86 94 99

D . GROWTH RATE COMPARISONS FOR AGES 8-13 YEARS (PEMALES) AND 10-15 YEARS (MALES). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

102 105 105 109 112

IV . DIFFERENCES IN BODY WEIGHT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A . COMPARISONS FOR LATE CHILDHOOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . B . COMPARISONS FOR EARLY ADOLESCENCE. . . . . . . . . . . . . . . . . . . . . . . . C . COMPARISONS FOR LATE ADOLESCENCE . . . . . . . . . . . . . . . . . . . . . . . . . D . GROWTH RATE COMPARISONS FOR AGES 8-13 YEARS (FEMALES) AND 10-15 YEARS (MALES). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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V . DIFFERENCES IN CHEST GIRTH., . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A . COMPARISONS FOR LATE CHILDHOOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . B . COMPARISONS FOR ADOLESCENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

117 117 119

VI . DIFFERENCES IN OTHER SOMATIC VARIABLES . . . . . . . . . . . . . . . . . . . . . . . . A . COMPARISONS FOR SIZE OF HEAD. TRUNK. AND LIMBS IN CHILDHOOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B . COMPARISONS FOR SIZE OF HEAD, TRUNK, AND LIMBS IN ADOLESCENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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VII . SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

130 134

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Copyright 0 1982 by Academic Press Inc All rights of rcproduclion in any form reserved. ISBN 0- 12-009717-6

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I. Introduction Two recent reports (Meredith, 1978b, 1979) dealt with somatic differences among groups of racially similar children and youths residing at paired urban and rural locations in various parts of the world. The former was focused on urban-rural differences in stdnding height during early childhood, and the latter on urban-rural differences--n?ainly in standing height and body weight-across the age span from mid-childhood ;o mid-adolescence. This article contains no repetition of analyses or outcomes from the foregoing reports: the contribution is designed to provide separate treatments for late childhood, early adolescent, and late adolescent segments of human ontogeny . Within each segment, drawing upon somatic data collected between 1950 and 1980, knowledge is systematized for urban-rural differences in standing height, body weight, and dimensions of the head, trunk, and limbs. The objectives are as follows: 1 . To bring together a substantial array of statistics pertaining to urban-rural differences in human body size presently scattered in biological journals, scholarly monographs, anthropometric-survey reports, and unpublished manuscripts. 2. To determine the direction and magnitude of urban-rural differences in body size for a wide assortment of human groups during late childhood, early adolescence, and late adolescence. Included are Ghanaian Black, South African White, Transvaal Black, and Tunisian groups in Africa; Chinese, Hindu, Japanese, Kirghiz, Russian, and South Korean groups in Asia; Australian and New Zealand White groups in Australasia; Austrian, Bulgarian, Chuvash, Finnish, French, German, Greek, Hungarian, Italian, Lithuanian, Moldavian, Polish, and Spanish groups in Europe; and Amerindian, Costa Rican, Mexican mestizo, Peruvian, Surinam Creole, Surinam Indonesian, and United States White groups in North, Central, and South America. 3. To discover whether urban-rural differences in body size increase or decrease with age in the period between 7 and 17 years. For instance, to ascertain whether urban-rural differences in standing height are smaller prior to adolescence than during adolescence, or vice versa. 4. To reveal whether urban-rural differences in body size increase or decrease from decade to decade. In particular, to show whether urban-rural differences in body weight of children and youths are greater for the 1950s than the 1970s, or vice versa. 5. To investigate rates of growth in standing height and body weight during selected age intervals. For example: Are increments in height and weight between late childhood and mid-adolescence systematically slower (or faster) for city residents than their village peers?

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All of the urban-rural differences presented in Sections 111 through VI of this article are secured from group averages based on age to the nearest birthday. In some of the studies drawn upon, averages were reported at 7.5 years, 8.5 years, and so forth; from these studies averages at 8 years, and successive annual ages, were derived for the present article by rectilinear interpolation. At various places in this contribution the word “group” is used in one of two ways. On occasions it denotes a racial, national, or tribal division of mankind; at other times it denotes an urban or a rural subdivision of a race, nation, or tribe. Usually the context in which it appears is adequate to carry the correct connotation. Where necessary for clarity, urban and rural subdivisions are referred to as ‘ ‘subgroups.’ ’

11. Retrospect: 1870-1915 Had a synthesis of urban-rural findings on human body size been written near the end of World War I , the author of the synthesis would have reviewed studies made between 1870 and 1915, and would have concluded: On the whole, children and youths of European ancestry residing at urban centers are shorter and lighter than rural coevals. In the early 1870s, Roberts (1876) measured the standing height and body weight of English children living in towns and agricultural districts of Cheshire, Lancashire, and Yorkshire. At ages between 9 and 1 1 years, the averages on about 4000 urban children were lower than those on about 1700 rural children by I . 2 cm and 0 . 9 kg for height and weight, respectively. Erismann (1888) reported statistics obtained from “Dr. Michailoff“ at ages 8.5 to I I .5 years on European children (2000 urban and 3800 rural) measured in 1887 at Russian city and village schools. Compared with the urban averages, corresponding rural averages were higher for girls and lower for boys in height, similar for girls and lower for boys in weight, and higher for both sexes in chest girth. From data amassed during 1889 on German children and youths representing ages from 6.5 to 13.5 years, Schmidt (1892) found 4300 urban residents smaller than 5000 rural peers by 1.8 cm in average standing height and 0.7 kg in average body weight. Records for height and weight were collected about 1907 in New South Wales on 25,700 residents of Sydney and 9000 rural residents between ages 6.5 and 15.5 years (Roth & Harris, 1908). Compared with the rural children and youths, those living at Sydney averaged 0.8 cm shorter and 0.2 kg lighter. Tuxford and Glegg (191 I ) , using measures accumulated during 1909-1910 on country-wide samples of English children and youths between ages 6.5 and 14.5 years, obtained averages lower on 21 3,000 urban inhabitants than 178,000 village inhabitants by 1.2 cm and 0.6 kg for height and weight, respectively.

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Standing height and body weight data were gathered in Pomerania during 1911 on 14,200 urban males and 28,300 rural males between ages 6.5 and 12.5 years (Peiper, 1912). Compared with the rural residents, urban residents were shorter by 0.2 cm and lighter by 0.4 kg. At semiannual ages from 7 to 15 years, Mecham (19 18- 1919) reported averages for standing height and body weight on children and youths of Australian-born parents living in New South Wales: measures were taken during 1913-1915 on 33,200 persons at a metropolitan center, 27,000 at “country towns,” and 43,200 in rural districts. On average, persons living in the city were smaller than those living (a) at country towns by 0.2 cm and 0.3 kg, and (b) in rural districts by 1.9 cm and 0.9 kg. Juxtaposition of these findings and those to be presented in Sections 111 and IV will show: The direction of urban-rural differences in standing height and body weight usually found in the period 1870-1915 is the opposite of that typifying the period 1950-1980.

111. Differences in Standing Height A. COMPARISONS FOR LATE CHILDHOOD

I . Female Children Urban-rural differences in average standing height of girls in the triennium between ages 7 and 10 years are displayed in Table I. Each row on the table carries an identification tag, names an ethnic group, specifies when data were collected, records how many urban and rural girls were measured, and gives an obtained urban-rural difference in average standing height. Additional particulars for Table I are as follows: Tag I - I . Samples drawn from cities with more than 50,000 inhabitants, and villages with fewer than 2000 inhabitants (Aubenque, 1952) Tag I-2. Data accumulated at Klagenfurt, and in the Kamtner rural region (Routil, 1955) Tag I-3. Measures taken in 1950 at Modena (Rezza & Soragni, 1953), and during the late 1950s in rural regions of Modena province (Galli, 1960) Tag I-4. Materials gathered in 1958 at Budapest (Dezso, 1959), and during 1951-1954 at 30 villages in eastern Hungary (Maliin, 1961) Tag I-5.Records amassed at five major cities (Auckland, Christchurch, Dunedin, Hutt Valley, Wellington) and 10 rural regions (New Zealand Department of Health, 1971) Tag I-6. Data from the “urban districts of Napier, Hastings, Palmerston North, Hamilton, New Plymouth, and Gisborne” and the same rural areas as in 1-5 (New Zealand Department of Health, 1971)

TABLE 1 Female Standing Height (Centimeters) in Late Childhood: Average Difference for the Age Period from 7 to 10 Years between Urban and Rural Girls Studied during 195C-1980 Sample size Tag

I- 1

Ethnic group French

1-2 Austrian 1-3 Italian 1-4 Hungarian 1-5

1-6 1-7 1-8 1-9 1-10 1-1 1

1-12 1-13 1-14 1-15

1-16 1-17 1-18 1-19 1-20 1-21 1-22 1-23 1-24 1-25 1-26 1-21 1-28 1-29 I-3c 1-31 1-32 1-33 1-34 1-35 1-36

New Zealand White New Zealand White French Australian White Finnish Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Chuvash Russian Japanese United States White Greek Surinam Creole Surinam Hindu Surinam Indonesian Spanish Costa Rican Ghanaian Black Indian (India) Amerindian Polish Australian White Polish Chinese South Korean Malayan Malaysian Chinese Malaysian Tamil ~~~

Time

Urban

1950 I95C-I954 1950-1958 1951-1958 1954 1954 I955 I955 1955-I961 1957-1960 1957-1960 19591960 1958-1962 I96C-I96I 196C-I961 I96C-I961 1961- 1963 I963 196% 1965 1963-1966 1964- I965 1964-I965 1964-I965 I 963-1968 1963-1969 19661968 I 964-1970 1965-1975 1968-1972 l97C-I971 1973-I974 1975 1976 1976 I976 I976

Rural

>20,000

>20,000

ca. 4700

ca. 12,000

796 225 3230 978 >4000 589 3065 3156 1739 446 470 903 910 487 613 ca. 8600 ca. 740 ca. 500 201 I

713 230 400 790 ca. 260 1278 103 >700 2583 ca. 385 8394 194 323 223 I33

>450 2348 2218 2218 >5000

I006 666 1497 I497 ca. 250 419 794 194 484 674 ca. 13,000 ca. 250 ca. 1200 1288 4259 1525 27,394 442 ca. 350 1655 I43 345 191 ca. 420 8361 229 285 556 313

Urban minus rural (7-10 years)O .5

2.0 I .3h

2.I C .2 - .I .2 -1.6 3.0 2.0 I .8 5.6 2.9 3.3 1.5

3.3 .8 I .o

.7 5.7 .9 I .5

1.7 5.9c 2.6 2.0 4.4c 2.6< 2.9 .3 1.8 5.2 3.I 4.2 6.1 8.0

~~

"Each value in this column is the average of four differences, that is, urban mean minus rural mean at successive annual ages from 7 to 10 years. bThe obtained difference was increased by .6cm as estimated adjustment for earlier collection of urban than rural data. 20,000 ca. 4600 25 1 3272 1014 >4000 522 2373 2366 3263 I768 466 469 798 84 1 640 494 394 935 1043 679 ca. 13,200 42 1 403 1304 1901 744 162 345 400 819 ca. 260 398 638 ca. 380 >700 112 3948 ca. 385 8475 264 587

>20,000 ca. 10,700 2643 2203 2203

11-1 1

1-29. 11-13 1-30 1-31 1-32 1-33 11-14

>5000

880 195 532 1305 1305 ca. 250 559 758 758 591 478 43 1 417 1189 762 ca. 15,000 539 836 ca. 3250 998 3506 I249 181 2 1,974 687 ca. 350 1593 299 239 346 222 238 ca. 420 8083 270 758

Urban minus rural (10-13 yearsp .7 2.5 4.56 .7 .9 .5 -1.9 3.5 3. I 3.7 2.3 7.2 4.8 4.5 2.7 4.3 3.6 .8 2.9 .4 1.1

I .o 3.0 3.9 - .I 1.6 3.0 2.8 2.8 7.56 3.2 2.9 1.4 4.3 6.5 3.2 - .7 - .6 I .8 5.8 4.2 5.1

aEach value in this column is the average of four differences, that is, urban mean minus rural mean at successive annual ages from 10 to 13 years. bThe obtained difference was lowered by .8cm as approximate adjustment for earlier collection of rural than urhan data.

96

Howard V . Meredith

sured between 1950 and 1980 was greater for urban than rural residents. In five instances the null hypothesis could not be rejected, and for one ethnic group (1-8) urban females were significantly shorter than rural peers. These findings were obtained from tests at p = .01, using 7.0 cm as population standard deviations (Bulgarian Academy of Sciences, 1965; New Zealand Department of Health, 1971). Table 111 showed that urban female youths surpassed rural coevals by 2.5 cm or more in 26 (62%) of 42 comparisons: Austrian, Bulgarian (1-14, 1-15), Chinese, Chuvash, Costa Rican, Finnish, Ghanaian Black, Hungarian, Indian (I-lo), Jamaican Black, Kirghiz, Lithuanian, Mexican, Polish (1-12, 1-29, 11-2, 11-3), Russian (11-5, 11-7), South Korean, Spanish, Surinam Hindu, Surinam Indonesian, Transvaal Black, and Turkish. Tables I and I11 included 28 corresponding rows for late childhood and early adolescence. Assembling the differences from these rows in two series, and computing the mean of each series, revealed: During 1950-1980, average standing height of females at urban centers exceeded that at rural villages by 2.0 cm in late childhood, and 2.8 cm in early adolescence. Compared with values from Table 1, matching values in Table I11 were larger by 1.0 cm or more for Bulgarian, Hungarian, Lithuanian, South Korean, Spanish, Surinam Hindu, and Surinam Indonesian groups; by .5 to .9 cm for Austrian, Chinese, Costa Rican, Ghanaian Black, New Zealand White, and Surinam Creole groups; and by .1 to .4cm for Chuvash, Finnish, French, Polish (I-29), and Russian (1-17) groups. Differences were zero for Japanese and Polish (1-31) groups, and negative for Australian White groups (1-8, 1-30). Longitudinal data for standing height of 54 Polish females “with Turner’s syndrome” were analyzed by Krawozynski (1980). Twenty-nine of the females lived at urban centers, and 25 in rural districts. The urban inhabitants “definitely” were taller than their rural peers; average differences were 2.3 cm in late childhood (ages 8 to 10 years) and 3.4 cm in early adolescence (1 1 to 13 years). 2 . Males Age 12-15 Years Two rows in Table IV were not documented previously: Tag IV-J. Residents of 8 cities and 44 rural communities; described as random samples of ‘‘healthy boys, whatever their socioeconomic background” (Grobbelaar, 1963) Tag ZV-2. Data amassed at Romanian urban and rural locations (Cristescu, 1969)

For 33 (92%) of the 36 comparisons in Table IV, significance tests at p = .01, using 7.8 cm as population standard deviations (see Section III,B, 1), allowed the inference that urban male youths age 12- 15 years measured during 1950- 1980

TABLE IV Male Standing Height (Centimeters) in Early Adolescence: Average Difference in the Age Triennium 12-15 years between Urban and Rural Subgroups Studied 1950-1980 Sample size Tag

Ethnic group

I- 1 French IV- 1 South African White 1-4 Hungarian 1-5 New Zealand White 1-6 New Zealand White 1-7 French East German 11- I 11-2 Polish 1-9 Finnish 1-10 Indian (India) 1-1 I Indian (India) 1-12 Polish 1-13 Lithuanian 1-14 Bulgarian 1-15 Bulgarian 1-16 Chuvash 11-4 Russian 11-5 Russian 11-6 Moldavian 1-17 Russian 1-18 Japanese 11-7 Russian IV-2 Romanian 1-21 Surinam Creole 1-22 Surinam Hindu 1-23 Surinam Indonesian 1-25 Costa Rican 1-26 Ghanaian Black 11-10 Polish 11-1 I Kirghiz 1-29 Polish 1-30 Australian White 1-3I Polish 1-32 Chinese 1-33 South Korean 11-14 Transvaal Black

Time

Urban

Rural

I950 1952- 1955 195 I- 1958 1954 1954 1955 1956-1958 1957- I958 1955- 1961 1957- I960 1957- 1960 I 959- I960 1958-1962 1960-1961 1960-1961 1960-1961 196% 196I 1961- 1962 1961-1963 1961-1963 I965 1962- I964 1963- I966 1964- 1965 1964- I965 1964-1965 1963- 1966 1966-1968 1967 1967- I970 1968-1972 1970-197 I 1973-1974 I975 1976 1976-1978

>20,000 567 402 3316 1101 >4000 >4000 1894 2132 3270 1937 403 440 742 846 420 405 I I28 843 747 ca. 18,000 538 >3200 1781 738 226 709 ca. 350 350 ca. 380 >650 4939 ca. 380 8705 330 59 I

>20,000 463 1734 2124 2124 >5000 >5000 I29 302 I576 I576 ca. 250 617 778 778 483 432 44 I 1306 837 ca. 16,000 680 >3600 82 1 2674 1010 44 1 ca. 275 1197 243 295 354 ca. 400 8264 258 405

Urban minus rural ( 12- I5 years).

.8 3.1 7.8h 1.1

1.1 .6 2.0 6. I 4.8 I .7 1.6 5.4 5.1 3.7 2.4 3.9 1.3 3.4 .7 .4 I .3 2.0 6.0 2.2 3.0 3 .O 6.3 2.4 2.0 7.9 4.4 .4 3.2 5.8 3.0 4.8

"Each value in this column is the average of four differences, that is, urban mean minus rural mean at successive annual ages from 12 to 15 years. "The obtained difference (8.6 cm) was reduced by .8 cm as estimated adjustment for earlier collection of rural than urban data.

97

Howard V. Meredith

98

exceeded their rural peers in average standing height. Rejection of the null hypothesis statistically was untenable in three instances, that is, for Australian White, Moldavian, and Russian urban-rural comparisons (I- 17, 1-30, 11-6). The early adolescent differences in Table IV showed that in 16 of 36 pairings, urban male youths studied during 1950-1980 were taller than rural coevals by 2.5 cm or more. Listed alphabetically, these pairings represented Bulgarian (1-14), Chinese, Chuvash, Costa Rican, Finnish, Hungarian, Kirghiz, Lithuanian, Romanian, Russian (1-3 1, 11-5), South African White, South Korean, Surinam Hindu, Surinam Indonesian, and Transvaal Black ethnic groups. The difference of 4.8 cm on Transvaal Black youths was similar to that of 4.5 cm obtained by Walker and Walker (1977) from data collected during 1975-1976 at two ages (12 and 14 years) on 134 Black males at Soweto and 86 peers in a rural region 22 km west of Rustenburg. On Turkic Tatar males age 15 years, Goldfeld et al. (1965) reported averages for standing height higher by 4.1 cm on 253 TABLE V Female Standing Height (Centimeters) in Late Adolescence: Average Difference for Ages from 15 to 17 Years between Urban and Rural Subgroups Studied 1950-1980 Sample size Tag

Ethnic group

Time

1-10 1-11 v-1 1-13 1-14 1-15 v-2 1-17 1-18 11-7 IV-2 1-2I 1-22 1-23 1-25 11-10 v-3 11-11 1-30 1-32 1-33 11-14

Indian (India) Indian (India) Jamaican Black Lithuanian Bulgarian Bulgarian Hungarian Russian Japanese Russian Romanian Surinam Creole Surinam Hindu Surinam Indonesian Costa Rican Polish Polish Kirghiz Australian White Chinese South Korean Transvaal Black

1957-1960 1957- I 960 1959 1958-1962 1960-1 961 1960- 1961 1961-1963 1961- 1963 1963 1962-1964 1963- 1966 1964- 1965 1964- 1965 1964-1965 1963- 1969 1967 1 9 6 61971 1967-1970 1970- 1971 1975 I976 1 9 7 61978

Urban

Rural 600 600 73 173 597 597 476 37 1 >3500 497 >2200 260 399 123 I24 448 3052 180 127 6016 24 1 500

2226 963 55

253 693 655

469 333 >6500 388 >2000 1140 333 106 505 164 672 ca. 285 1888 6063 330 55 1 ~

~~

UAverage of differences for ages 15 and 16 years; age 17 years not sampled.

Urban minus rural (15-17 years) 3.6 1.1 .5

3.9u 2.6 1.3 I .6 - .6a 1.1

.I 2.2u .2 1.8 2.2 5.0 .8 2.1 3.3 - 1 .ou 2.7 .5

1.2

Urban-Rural Diferences in Human Body Growth

99

youths at Kazan than on 100 coevals at villages in northeast Tatar Autonomous Soviet Socialist Republic. Differences in Table IV on Polish youths varied from 6.1 cm comparing Warsaw and the Makowskiego rural district (11-2), through 4.4 and 3.2 cm at and near Lublin and Wielkopoiski (1-29, 1-31), to 2.0 cm at Nowa Huta on offspring of immigrants from Polish urban and rural habitats (11-10). On Russian youths, obtained differences were 3.4, 2.0, 1.3, and .4 cm for groups at and near Kalinin, Ryazan, and Stavropol in Europe (11-4, 11-5, 11-7) and, in Asia, at Barnaul and rural areas of the Altai Territory (1-17). Tables 11 and IV had 34 corresponding rows for males in late childhood and early adolescence. The difference values in these rows yielded averages for the period between 1950 and 1980 showing that standing height was greater at urban than rural locations by 2.1 cm during late childhood, and 3.1 cm during early adolescence. Compared with Table I1 values, those of Table IV were higher by 1 .O cm or more for Chuvash, Costa Rican, Finnish, Ghanaian Black, Hungarian, Kirghiz, Lithuanian, New Zealand White (I-6), Polish (1-29, 11-2), Surinam Creole, Surinam Hindu, Surinam Indonesian, and Transvaal Black groups; by .5 to .9 cm for Australian White (I-30), Bulgarian (1-15), Chinese, East German, Indian (1-1 l), New Zealand White (1-5), and Polish (1-31) groups; and by . l to .4 cm for Bulgarian (1-14), French, Japanese, and Moldavian groups. Differences were negative by .3 or .4 cm for Indian (I-lo), Russian (I-17,11-7), and South Korean groups; and by 1 .O cm from a Russian comparison (11-4). Among the 78 comparisons for early adolescence in Tables I11 and IV, there were 24 in the 195Os, 41 in the 1960s, and 13 in the 1970s. Taking these decades in succession, the average amounts by which urban youths were taller than rural coevals were 2.7, 3.1, and 3.1 cm. C . COMPARISONS FOR LATE ADOLESCENCE

Averages for standing height of urban and rural late adolescent youths were accessible from some studies at ages 15, 16, and 17 years, and from other studies at ages 15 and 16 years only. For males, all of the urban-rural differences computed were based on averages extending to age 17 years. With few exceptions, the studies drawn upon in constructing Tables V and V1 were cited earlier. Exceptions were:

Tug V-1. Data gathered at Kingston and in a rural area surrounding the village of Lawrence Tavern on late adolescent females “of predominantly African origin” and “mostly poor” economic status (Ashcroft, Ling, Lovell, & Miall, 1966) Tag V-2. Measures taken on “Pommeranian and Kujawy youths” residing at metroptropolitan centers and in rural districts (Kriesel, 1977)

Howard V . Meredirh

100

TABLE V1 Male Standing Height (Centimeters) in Late Adolescence: Average Difference for the Age Period from 15 to 17 Years between Urban and Rural Subgroups Measured 1950-1980 ~~~

~

Sample size Tag

Ethnic group

Time

Urban

Rural

Urban minus rural (15-17 years)

IV- 1 Vl-1 1-10 1-1 1 1-14 1-15

South African White Hungarian Indian (India) Indian (India) Bulgarian Bulgarian Hungarian Russian Japanese Surinam Creole Surinam Hindu Surinam Indonesian Costa Rican Polish Kirghiz Chinese South Korean Transvaal Black

1952-1 955 1953-1954 1957- I960 1957-1960 1960- I961 1960-196 1 1961- 1 963 1962- 1964 1963 19641965 I 9 6 4 1965 1964- 1965 1963- 1969 1967 1967-1970 1975 1976 1976- 1978

360 I32 2198 1477 540 610 67 1 362 >7000 1015 382 I04 357 156 ca. 285 606 1 39 1 457

489 143 888 888 562 562 650 435 >4000 268 692 269 112 386 I74 6149 208 25 1

.2 2.0 4.3 3.5 3.9 1.5 3.3 4.2 1.3 1.8 2.0 2.1 1.3 I .8 5.8 5.1 2.3 3.3

v-2 11-7 1-18 1-21 1-22 1-23 1-25 11-10 11-1I 1-32 1-33 11-14

Tug V-3. Polish females measured at Warsaw (Charzewska, 1973) and at villages in “8 regions and 5 viovodships of Poland” (Laska-Mierzejewska, 1970) Tug VI-1. Urban and rural records accumulated on late adolescent males living at and near Debrecen (Eiben, 1956) For 30 (75%) of the 40 comparisons in Tables V and V1, statistical tests allowed the inference that average standing height of late adolescent females and males measured during 1950-1980 was greater for urban than rural youths. In 10 instances, rejection of the null hypothesis was untenable. These findings were obtained from tests at p = .01, using as population standard deviations 6.0 and 7.5 cm for females and males, respectively (Bulgarian Academy of Sciences, 1965; O’Brien, Girshick, & Hunt, 1941). At late adolescent ages (Tables V and VI) urban youths were taller than rural peers by 2.5 cm or more in comparisons for Bulgarian (1-14), Chinese, Costa Rican, Indian (I-lo), and Kirghiz groups of each sex; for Lithuanian females; and for Hungarian (V-2), Russian (11-7), and Transvaal Black males.

Urban-Rural Differences in Human Body Growth

101

On average, taking separately the 22 differences in Table V and the I8 in Table VI, late adolescent youths living at urban centers were taller than coevals living at rural habitats by 1.7 cm for females and 3.1 cm for males. Standing height differences obtained on each sex in late adolescence were greater for Indian inhabitants of large cities compared with those of rural areas (1-10) than for lndian inhabitants of smaller urban centers compared with those of the same rural areas (1-1 1). Similarly, Bulgarian differences between residents of Sofia and Bulgarian villages (1-14) were greater than those between residents of urban communities other than Sofia and the same village peers (1-15). For females of 14 ethnic groups, urban-rural differences were available at late childhood, early adolescent, and late adolescent ages. These were identified in Tables I, 111, and V as Tags “I” followed by 10, 11, 13, 14, 15, 17, 18, 21, 22, 23, 25, 30, 32, and 33. Averages from the three series of differences showed females taller at urban than rural habitats by 2.0, 2.9, and 1.8 cm in late childhood, early adolescence, and late adolescence, respectively. Were the socalled “adolescent spurt” in standing height timed earlier, on average, for urban than for rural females (see Fuchs, 1979), this pattern of rising and falling values would be expected. On males, Tables 11, IV, and VI had urban-rural comparisons in common for 15 groups (1-10, 1-11, 1-14, 1-15, 1-18, 1-21, 1-22, 1-23, 1-25, 1-32, 1-33, 11-7, 11-10, 11-1 1, 11-14); these gave average standing height differences of 2.6, 3.4, and 3.3 cm. Since the “adolescent spurt” occurs, on average, later for males than females, it is reasonable to envision a smaller urban-rural difference in average standing height from male comparisons at ages 17 to 19 years than the 3.3 cm obtained at ages 15 to 17 years. Joint examination of the urban-rural differences in Tables I through V1 revealed: 1 . On average, children and youths living at urban centers were taller than rural coevals by 2.1 cm in the 1950s, 2.7 cm in the 1960s, and 3.2 cm in the 1970s. These values were derived from 57, 105, and 41 comparisons for the three successive decades. 2 . Among 203 statistical tests at the .01 level, 167 (82%) allowed the inference that urban children and youths were taller than rural coevals, two allowed the reverse inference. and 34 (17%) did not allow rejection of the null hypothesis. The two sets indicating shorter average standing height at “metropolitan” than at “country” locations were obtained on girls and female youths measured during 1955 in Western Australia. 3. For nine ethnic groups, corresponding urban-rural differences in all six tables showed that average amounts by which urban children and youths surpassed rural coevals in standing height were 1 . 1 cm, Japanese (1-18); I .3 cm,

102

Howard V . Meredith

Surinam Creole; 2.1 cm, Surinam Hindu and Indonesian; 2.7 cm, South Korean; 2.9 cm, Indian (1-10); 3.6 cm, Bulgarian (1-14);and 4.9 cm, Chinese and Costa Rican . D. GROWTH RATE COMPARISONS FOR AGES 8-13 YEARS (FEMALES) AND 10-15 YEARS (MALES)

Table VII was constructed to display, for groups of urban and rural females measured between 1950 and 1980, standing height means at age 8 years, and increments in mean standing height during the quinquennium from ages 8 to 13 years. In relation to average age of adolescent peak velocity for standing height of females, this period extends from about 4 years before the peak to 1 year beyond (Faust, 1977; Roche & Davila, 1972). In order to deal with reasonably valid increments, Table VII was restricted to studies in which sample size exceeded 150 for each subgroup, that is, sample size was over 150 for urban females age 8 years, rural females age 8 years, urban females age 13 years, and rural females age 13 years. Both centimeter and percentage increments were obtained for table presentations. For a given urban or rural subgroup, centimeter increase was mean standing height at age 13 years minus mean standing height at age 8 years, and percentage gain was 100 X centimeter increase divided by mean at age 8 years. Table VII showed:

1. Urban means for standing height of females age 8 years were higher than corresponding rural means by amounts varying from .4 to 5.0 cm, and averaging near 1.7 cm. Taking urban and rural subgroups together, Indian females were shortest, French females intermediate, and New Zealand White females tallest. 2. In three instances (Bulgarian, Indian in India, Surinam Hindu) centimeter gain for urban females exceeded that for rural peers by more than 1 .O cm, and in three instances (French, Japanese, Moldavian) centimeter gains of urban and rural females showed little or no difference. Typical increments for the quinquennium between ages 8 and 13 years were near 27.5 and 26.5 cm for urban and rural females, respectively. 3. Expressed in relation to means for standing height at age 8 years, typical increases in standing height between ages 8 and 13 years were near 22.5% for urban females and 22.0% for rural females. From French, Japanese, and Moldavian comparisons, urban and rural percentage gains were similar; and from Bulgarian, New Zealand White, Surinam Creole, and Surinam Hindu comparisons, percentage gains were between 22.5% and 23.0% for urban females, and near 22.0% for rural females.

TABLE VII Means and Gains in Female Standing Height: Sample Size at Age 8 Years, Mean at Age 8 Years, Centimeter Gain from 8 to 13 Years, and Percentage Gain from 8 to 13 Years for Urban and Rural Females Measured between 1950 and 1980

Sample size: age 8 years Tag -

-

W 0

1-1

1-5 1-10 1-14

11-6 1-17 1-18

1-21 1-22 1-32

Ethnic group

French New Zealand White Indian (India) Bulgarian Moldavian Russian Japanese Surinam Creole Surinam Hindu Chinese

Urban

>2000" 809 785" 208c 227c 168c >2000" 508 18OC

20930

Mean: age 8 years

Gain (centimeters): &13 years

Gain (%): &I3 years

Rural

Urban

Rural

Urban

Rural

Urban

Rural

>2000" 553b 369' 177' 236
3000a 325< 1119b 2101"

"'Sample size at age 13 years more than 1900. "Sample size at age 13 years between 500 and 900. CSample size at age 13 years between 170 and 340.

TABLE VIII Means and Gains in Male Standing Height: Sample Size at Age 10 Years, Mean at Age 10 Years, Centimeter Increase from 10 to 15 Years, and Percentage Increase from 10 to 15 Years for Urban and Rural Males Measured between 1950 and 1980

Sample size: age I0 years

-

P 0

Tag

Ethnic group

1-1

French New Zealand White East German Indian (India) Bulgarian Russian Japanese Surinam Hindu Chinese

1-5 11-1 1-10 1-14 1-17 1-18 1-22 1-32

Urban

Mean: age 10 years

Gain (centimeters): 10-15 years

Gain (96):10-15 years

Rural

Urban

Rural

Urban

Rural

Urban

Rural

>2000a 5636 >1200a 3246

132.3 138.6 136.7 126.7 136.4 133.8 130.7 131.7 132.5

131.9 138.0 135.1 124.7 132.6 132.6 129.6 129.4 127.5

23.8 27.9 27.0 26.1 28.5 28.6 29.5 27.4 26.8

22.7 27.3 27.1 24.2 28.5 29.4 29.2 26.4 25.9

18.0 20.1 19.8 20.6 20.9 21.4 22.6 20.8 20.2

17.2 19.8 20.1 19.4 21.5 22.2 22.5 20.4 20.3

188C

193c >3500a 1026~ 2093a

asample size at age 15 years more than 1200. bSample size at age 15 years between 450 and 900. <Sample size at age 15 years between 150 and 400.

Urban-Rural Dixerences in Human Body Growth

105

Tables VII and VIII were prepared as complementary for females and males. Table VIII spanned the period of ontogeny from about 4 years preceding to 1 year following average age of adolescent peak velocity in standing height of males. Examination of Table VIII revealed: 1 . Means for standing height of urban males age 10 years were higher than comparable means on rural males by amounts varying from .4 (French) to 5.0 cm (Chinese). In 78% of the comparisons the urban advantage exceeded 1.0 cm. 2. In three instances (French, Indian in India, Surinam Hindu) the centimeter gain for urban males surpassed that for their rural peers by 1 .O cm or more, and in three instances (Bulgarian, East German, Japanese) little or no difference was found between the amounts of centimeter gain for urban and rural males. Typical centimeter increases in standing height during the quinquennium following age 10 years were slightly above 27 cm for urban males, and slightly below 27 cm for rural males. 3 . From Chinese, East German, Japanese, and New Zealand White comparisons, urban and rural percentage gains were similar. From other comparisons, urban-rural differences were both positive (French, Indian) and negative (Bulgarian, Russian). Overall, the average percentage increases in male standing height from age 10 to age 15 years was near 20.5%.

In summary, from large samples studied between 1950 and 1980, rates of growth in standing height from late childhood to middle adolescence were in most instances slightly higher for urban than rural females and males.

IV. Differences in Body Weight A.

COMPARISONS FOR LATE CHILDHOOD

Tables IX and X were constructed to exhibit, for girls and boys, respectively, urban-rural differences in average body weight during late childhood. The procedure in table construction matched that for Tables I and 11 on standing height, and the sources drawn upon were the same as specified in connection with Tables I and 11. The values in the right-hand column of Tables IX and X gave 1.5 kg ( 3 . 3 Ib) for each sex as the average amount by which children studied between 1950 and 1980 at late childhood ages were heavier at urban centers than in rural districts. As noted earlier (Section Ill), urban samples in several instances were selected from the upper part of the socioeconomic continuum (1-12, 1-24, 1-34, 1-35,

TABLE IX Female Body Weight (Kilograms) in Late Childhood: Average Difference in the Triennium 7-10 Years between Urban and Rural Girls Studied during 1950-1980 Sample size Tag

Ethnic group

Time

I- 1 1-2 1-3 1-4

French Austrian Italian Hungarian New Zealand White New Zealand White French Australian White Finnish Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Chuvash Russian Japanese United States White Greek Surinam Creole Surinam Hindu Surinam Indonesian Spanish Costa Rican Ghanaian Black Indian (India) Amerindian Polish Australian White Polish Chinese South Korean Malayan Malaysian Chinese Malaysian Tamil

1950 1950-1954 1950-1958 1951-1955 1954 1954 1955 1955 1953- 1961 1957-1960 1957-1960 1959- 1960 1958-1962 1960-1961 1960-1961 1960-1961 1961-1963 1963 1963-1965 1963- 1966 1964- 1965 1964- 1965 1964- 1965 1963-1968 1963- 1969 1966- 1968 1964-1970 1965- 1975 1968- I972 1970-1971 1973- 1974 1975 1976 1976 1976 1976

1-5

1-6 1-7 1-8 1-9 1-10 1-11 1-12 1-13 1-14 1-15 1-16 1-17 1-18 1-19 1-20 1-21 1-22 1-23 1-24 1-25 1-26 1-27 1-28 1-29 1-30 1-31 1-32 1-33 1-34 1-35 1-36

Urban >20,000 ca. 4700 796 225 3230 978 >4000 589 30114 3156 1739 446 470 895 802 487 613 ca. 8600 ca. 740 ca. 500 201 I 712 230 400 790 ca. 260 1278 103 >700 2583 ca. 385 8394 194 323 223 133

Rural >20,000 ca. 12,000 >450 2180 2218 2218 >S o 0 0 1006 666 1497 1497 ca. 250 419 782 782 484 674 ca. 13,000 ca. 250 ca. 1200 1288 4258 1531 27,394 442 ca. 350 1657 143 345 191 ca. 420 8361 230 285 556 313

Urban minus rural (7-10 yearsp .7 1.o 1.96 1.4= - .I - .4 .6 .2 1.4 1.2 I .o 3.2 .9 1.6 .7 .8 1.1

.7 .4 2.4 .4 1.1 1.1 4.0c 1.2 1.9 1.1‘ 2.7~ 2.1 .I .6 I .4 I .2 3.2 5.6 6.3

“Each value in this column is the average of four differences, that is, urban mean minus rural mean at successive annual ages from 7 to 10 years. bThe obtained difference was increased .4 kg as estimated adjustment for earlier collection of urban than rural data. cThe obtained difference was reduced .4 kg as approximate adjustment for earlier collection of rural than urban data.

106

TABLE X Male Body Weight (Kilograms) in Late Childhood: Average Difference in the Triennium 8-1 I Years between Urban and Rural Boys Studied during 1950-80 Sample size Tag

Ethnic group

Time

Urban

Rural

I-1

French Austrian Italian Hungarian New Zealand White New Zealand White Australian White Finnish Polish Indian (India) Indian (India) Polish Lithuanian Polish Bulgarian Bulgarian Chuvash Russian Russian Moldavian Russian Japanese Russian United States White Jamaican Black Greek Surinam Creole Surinam Hindu Surinam Indonesian Spanish Costa Rican Ghanaian Black Polish Amerindian Polish Tunisian Mexican mestizo Australian White Polish Chinese South Korean Malayan

I950 1950- 1954 1950- I958 195 1- 1958 1954 1954 1955 1955- 196I 1957-1958 1957- 1960 1957- I960 195% I960 1958- I962 1956- 1966 1960-1961 1960-1961 1960- I96 I 1960- I961 1961- 1962 196 I- I963 1961-1963 I963 1962- 1964 1963- I965 1964 1963- I966 1964- I965 1964- 1965 1 9 6 4 I965 1963- 1968 1963-1969 I 9 6 6 1968 I967 1965- I975 1968- I972 1968- I972 1968- I972 1970- 197 I 1973-1974 1975 1976 1976

>20,000 ca. 3500 1425 249 3259 976 75 I 3030 2753 3395 1841 444 497 476 802 808 497 44 I 878 1021 672 ca. 10,000 45 1 ca. 790 403 ca. 680 1679 787 214 400

>20,000 ca. 12,000 >450 2663 2245 2245 832 604 267 1759 1759 ca. 250 528 689 725 725 487 430 400 968 748 ca. 14.000 532 ca. 240 777 ca. 1350 1 I65 4028 1557 26,980 488 ca. 275 1503 I69 345 I32 250 229 ca. 400 8379 246 269

1-2 1-3 1-4 1-5 1-6 1-8 1-9

11-2 1-10 1-1 I 1-12 1-13 11-3 1-14 1-15 1-16 11-4 11-5 11-6 1-17 1-18 11-7 1-19 11-8 1-20 1-21 1-22 1-23 1-24 1-25 1-26 11-10 1-28 1-29 11-12 11- I3 1-30 1-31 1-32 1-33 1-34

101 1

ca. 275 31 1 95 >650 33 I 131 2993 ca. 380 8268 187 325

Urban minus rural (8- I I years)"

.8 .9 1.9h 2.1' - .I - .4 .4 2. I 2.3 1 .o

.I 3.1 1.1

1.6 I .8 .7 .2 1.2

.o .2 .9 .6 1.1

.3 I .6 2.8

.s

.7 .4 3.2~ 2.7 2.2 .9

I .9( 2.3 3.8 .3" - .2 1.2 I .4 I .9 2. I (continued)

I07

Howard V. Meredith

108

TABLE X (Continued) Sample size Tag 1-35 1-36 11-14

Ethnic group Malaysian Chinese Malaysian Tamil Transvaal Black

Time 1976 I976 1976- 1978

Urban

Urban minus rural (8-1 1 years)O

Rural 38 I 177 226

517 268 315

5.9 4.6 2.6

OEach value in this column is the average of four differences, that is, urban mean minus rural mean at successive annual ages from 8 to I 1 years. bSee Table IX, footnote b. See Table IX, footnote c . . dThe average for urban boys exceeded that for a rural subgroup of 119 “Zapotec-speaking” boys by 1.3 kg (Malina et a l . , 1981).

1-36); with these studies eliminated urban children were found, on average, to weigh more than rural peers by 1.1 kg (2.5 lb). Taken together, Tables IX and X afforded 24 comparisons for the 1950s, 36 for the 1960s, and 21 for the 1970s. Decadal averages showed urban children surpassed rural coevals in body weight by 1.1, 1.2, and 2.4 kg, respectively. On exclusion of the studies cited in the preceding paragraph, averages were 0.9 kg for the 1950s, 1.1 kg for the 1960s, and 1.5 kg for the 1970s. Among the 81 comparisons in Tables IX and X, 63 (78%) allowed the inference that urban children were heavier than rural coevals, and 18 (22%) fell short of allowing rejection of the null hypothesis. In no instance were urban girls or boys significantly lighter in body weight than rural coevals. These results were obtained through statistical tests at p = .01, using 4.5 kg as population standard deviations (O’Brien et al., 1941). Body weight averages were similar (differed less than .5 kg) for urban and rural samples of the following ethnic groups: Surinam Creole girls; Chuvash, Indian (1-1 l), Mexican mestizo, Moldavian, Russian (11-6), and Surinam Indonesian boys; and Australian White (1-8,1-30), New Zealand White (1-5, I-6), and United States White children of both sexes. Averages for body weight in late childhood were greater at urban than rural locations by 1.8 kg (4.0 lb) or more for Bulgarian (I-14), Costa Rican, Finnish, Hungarian, South Korean, Transvaal Black, and Tunisian boys; and Amerindian, Greek, Italian, Ghanaian Black, Malayan, Malaysian Chinese, Malaysian Tamil, Polish (1-12, 1-29), and Spanish children of both sexes. On African Black children 8 and 10 years of age measured during 1975-1976 at Soweto and in a rural area 22 km west of Rustenburg, Walker and Walker (1977) obtained body weight averages 4.5 kg higher for 172 urban girls than 92 rural peers, and 3.5 kg higher for 165 urban boys than 87 rural peers.


109

B . COMPARISONS FOR EARLY ADOLESCENCE

Tables XI and XI1 pertain to the same segments of human ontogeny as Tables 111 and IV. They display urban-rural differences in average body weight of young adolescent females (Table XI) and males (Table XII) studied between 1950 and 1980. For both sexes together, the following findings were obtained: 1. On average, early adolescent youths living at urban communities weighed more than those inhabiting rural regions by no less than 2.0 kg (4.4 lb). The 75 urban-rural differences in Tables XI and XI1 gave an average of 2.2 kg; exclusion of the Polish and Spanish differences from comparison of urban youths favorably selected socioeconomically with rural peers (I-12,1-24) gave 2.0 kg as the average amount by which body weight was greater in early adolescence for urban than rural youths. 2. From statistical tests at the .01 level, 61 (81%) of the 75 comparisons in Tables XI and XI1 allowed the inference that in early adolescence urban youths exceeded rural coevals in average body weight. In 14 instances (19%), rejection of the null hypothesis was untenable. The population standard deviations used in making these tests were 6.7 kg for females and 7.7 kg for males (O’Brien et al., 1941). 3. Averages for body weight of urban and rural young adolescents were similar (differed less than .5 kg) on Moldavian and New Zealand White (1-5) ethnic groups of each sex, and on female youths of Australian White (1-8, 1-30), Mexican mestizo, Polish (1-31, 11-10), and Russian (11-4, 11-9) ethnic groups. 4. Urban young adolescents, on average, were heavier than rural peers by 2.0 kg or more for Indian (I-lo), Jamaican Black, Mexican, Polish (11-3,11-7), South Korean, Spanish, Surinam Indonesian, and Turkish females; Chuvash and Romanian males; and Bulgarian (I-14), Chinese, Costa Rican, Finnish, Ghanaian Black, Hungarian, Lithuanian, Polish (1-1 2, 1-29, 11-7), Surinam Hindu, and Transvaal Black youths of both sexes. 5. Young adolescents of each sex living at Bulgarian urban centers other than Sofia are intermediate in average body weight to residents at Sofia and inhabitants of Bulgarian villages (1-14, 1-15). 6. Compared with urban-rural differences for Polish female and male youths living at and near Lublin (1-29), differences were smaller for those living at and near Wielkopolski (I-31), and larger for well-nourished youths at Warsaw in relation to peers at impoverished Polish villages (1- 12). Urban-rural comparisons at biennial ages for South African Black youths were accessible from the study by Walker and Walker (1977) cited in Section IV,A. Averages at ages 12 and 14 years showed that 134 urban males were heavier than 86 rural peers by 4.5 kg. From the study on Turkic Tatar males age 15 years (Goldfeld et al., 1965) cited in Section IlI,B, average body weight for

TABLE XI Female Body Weight (Kilograms) in Early Adolescence: Average Difference in the Age Triennium 10-13 Years between Urban and Rural Subgroups Weighed between 1950 and 1980 Sample size Tag

Ethnic group

Time

Urban

Rural

Urban minus rural (10-13 yearsp

1-1

French Austrian Hungarian New Zealand White New Zealand White French Australian White Polish Finnish Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Polish Chuvash Russian Russian Moldavian Russian Japanese Russian Jamaican Black Russian Surinam Creole Surinam Hindu Surinam Indonesian Mexican Spanish Costa Rican Ghanaian Black Polish Turkish Polish Mexican mestizo Australian White Polish Chinese South Korean Transvaal Black

1950 1950- 1954 1951- 1958 1954 1954 1955 1955 1957-1958 1955- 1961 1957- I960 1957- 1960 1959-1960 1958- 1962 196&196 I 1960- 1961 1 9 5 61966 1960- 1961 1960- 1961 1961-1962 1961- 1963 1961- 1963 1963 1962- 1964 I964 1964-1965 1964-1 965 1964-1965 1964- 1965 1965 1963-1968 1963- I969 I 966- 1968 1967 1967- I 969 1968-1972 1968-1972 1970-197 1 1973- 1974 1975 1976 1976-1978

>20,000 ca. 4600 25 1 3272 1014 >4000 522 2373 2313 3263 1768 466 469 784 817 640 494 394 935 1043 679 ca. 13,200 42 1 403 1304 1903

>20.000 ca. 10,700 2402 2203 2203 >SO00 880 195 532 I305 1305 ca. 250 557 756 756 599 478 43 1 417 1189 762 ca. 15,000 539 836 ca. 3250 997 355 1 1254 181 2 1,974 687 c. 350 1585 299 346 222 238 ca. 420 8083 270 755

I .o 1.5 3.0b - .I .6 1.4 .3 3.0 2.7 2.7 1.6 6. I 3.4 3.2 1.2 2.8 1.8 - .1 I .7 .I I .7 .8 2.2 2.5 - .2 I .o 2.5 2.4 2.5 6.2b 2.7 3.2 .4 5.2 3.4 - .4 - .2 .2 2.3 2.3 6.4

1-2 1-4 1-5 1-6 1-7 1-8 11-2 1-9 1-10 1-1 I 1-12 1-13 1-14 1-15 11-3 1-16 11-4 11-15 11-6 1-17 1-18 11-7 11-8 11-9 1-21 1-22 1-23 111-1

1-24 1-25 1-26 11-10 111-2 1-29 11- I3 1-30 1-31 1-32 1-33 11-14

744 162 345 400 819 c. 260 389 638

>700 112 3949 ca. 385 8475 264 589

eEach value in this column is the average of four differences, that is, urban mean minus rural mean at successive annual ages from 10 to 13 years. 'The obtained difference was lowered .6 kg as approximate adjustment for earlier collection of rural than urban data. 110

TABLE XI1 Male Body Weight (Kilograms) in Early Adolescence: Average Difference in the Age Triennium 12-15 Years between Urban and Rural Subgroups Weighed during 195s-1980 Sample size Tag

Ethnic group

Time

Urban

Rural

I- 1 IV- I 1-4

French South African White Hungarian New Zealand White New Zealand White French Polish Finnish Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Chuvash Russian Russian Moldavian Russian Japanese Russian Romanian Surinam Creole Surinam Hindu Surinam Indonesian Costa Rican Ghanaian Black Polish Polish Australian White Polish Chinese South Korean Transvaal Black

I950 1952-1 955 1951- 1958 1954 1954 1955 1957-1958 1955-1961 1957- I960 1957-1960 1959- 1960 1958- 1962 1960-1961 1960- I96 I 1960- 1961 1961961 1961-1962 I96 1-1 963 1961- I963 I963 1962- I964 1963-1966 1964-1965 1964- I965 1964- 1965 1 9 6 s I966 1966- I968 I967 1968-1972 197cL1971 1973-1 974 1975 1976 1976- I978

>20,000 567 402 3316 I101 >4000 1894 2132 3270 1937 403 440 802 83 1 420 405 I I28 843 747 ca. 18,000 538 >3200 1781 737 226 709 ca. 350 354 >650 4939 ca. 380 8705 329 591

>20,000 463 1583 2124 2124 >5000 129 302 I576 1576 ca. 250 617 725 725 483 432 41 I I306 837 ca. 16,000 680 >3600 82 I 2674 101 I 441 ca. 275 I I99 295 354 ca. 400 8264 258 405

1-5

1-6 1-7 11-2 1-9 1-10 1-1 I 1-12 1-13 1-14 1-15. 1-16 11-4 11-5 11-6 1-17 1-18 11-7 IV-2 1-2I 1-22 1-23 1-25 1-26 11-10 1-29 1-30 1-31 1-32 1-33 11-14

Urban minus rural ( 12- I5 years)a 1.2 2.0 6.86 .I .3 1.1

4.5 3.8 1.1 1.1

6.7 3.4 2.5 I .4 2.3 I .3 .9 .4 I .2 .9 I .6 4.5 I .6 3.1 1.8 5.0 2.6 1.8 3.6 .8 1.7 2.8 I .8 5.3

aEach value in this column is the average of four differences, that is, urban mean minus rural mean at successive annual ages from 12 to 15 years. obtained difference (7.5 kg) was lowered .7 kg as estimated adjustment for earlier collection of rural than urban data.

112

Howard V . Meredith

253 Kazan residents surpassed that for 100 village residents by 4.2 kg. Graham, MacLean, Kallman, Rabold, and Mellits (1980), from body weight data collected during 1961-1979 on “poor Peruvians” at Lima and in four northern villages, found urban inhabitants were heavier than rural peers throughout late childhood and early adolescence. The urban sample, compared with the rural sample, was more heterogeneous genetically. Corresponding rows for late childhood and early adolescence sum to 28 for females in Tables IX and XI, and 31 for males in Tables X and XII. From the differences in these rows it was found: During 1950-1980, averages for body weight at urban centers typically exceeded those at rural villages by 1 . 1 kg on each sex in late childhood and, in early adolescence, by 2.1 and 2.3 kg for females and males, respectively. Urban-rural differences were larger by 1 .O kg or more during early adolescence than during late childhood for Chuvash, Costa Rican, Finnish, Hungarian, Lithuanian, Polish (1-12, 1-29), Surinam Hindu, and Surinam Indonesian ethnic groups of both sexes; Bulgarian (I-14),Ghanaian Black, Indian (1-101, South Korean, and Spanish female groups; and Chinese, Indian (1-1 1). Polish (11-2), Surinam Creole, and Transvaal Black male groups. C . COMPARISONS FOR LATE ADOLESCENCE

Findings for the period 1950-1980 on urban rural differences in average body weight at late adolescent ages were assembled in Tables XI11 and XIV. In 19 (50%) of the 38 rows, urban youths exceeded rural peers by 1.5 kg or more. These compared Bulgarian (I-14), Indian (I-lo), Surinam Hindu, and Transvaal Black groups of each sex; Lithuanian, Polish (11-lo), and Romanian females; and Bulgarian (I-15), Chinese, Costa Rican, Hungarian, Indian (1-1 l ) , Russian (11-7), South African White, and South Korean males. Results from significance tests at p = .01 indicated 14 (37%) of the comparisons did not allow rejection of the null hypothesis, and 24 (63%)allowed the inference that during late adolescence urban youths weighed more than rural coevals. The population standard deviations used were 6.9 and 8.9 kg for females and males, respectively (O’Brien et al., 1941). Obtained urban-rural differences in average body weight at late adolescent ages were twice as large from comparisons of samples drawn at Sofia and Bulgarian villages (1-14)than from comparison of urban samples excluding Sofia with village samples (1-15). For each sex, this relationship was similar in late childhood (Section IV,A) and early adolescence (Section IV,B). Body weight findings from urban-rural comparisons in common for late childhood, early adolescence, and late adolescence were obtained from Tables IX through XIV to parallel those for standing height from Tables I through VI. For females, row identifications were “1” followed by 10, 1 1 , 13, 14, 15, 17, 18, 21, 22, 23, 25, 30, 32, and 33; and, for males, “I” followed by 10, 1 1 , 14, 15,

TABLE XIII Female Body Weight (Kilograms) in Late Adolescence: Average Difference in the Age Biennium 15-17 Years between Urban and Rural Subgroups Weighed during 1950-1980 Sample size Tag

Ethnic group

Time

Urban

Rural

1957- 1960 1957-1960 1959 1958-1962 196Gl96I 196% I961 1961-1 963 1961-1963 1963 1962- I964 1963- 1966 I 9 6 6 I965 1 9 6 4 I965 I 9 6 6 I965 1963- I969 I967 1966-1971 l 9 7 G I97 1 I975 I976 I 976- I 978

2226 963 55 253 693 655 469 333 >6500 388 >2000 1137 333 106 505 154 672 I 888 6063 321 55 1

600 600 73 I70 597 597 476 37 1 >3500 497 >2200 264 399 I23 124 445 2326 I27 6016 24 1 500

Urban minus rural ( 15- I7 years) ~~

1-10 1-11 v- I 1-13 1-14 1-15 v-2 1-17 1-18 11-7 IV-2 1-21 1-22 1-23 1-25 11-10 v-3 1-30 1-32 1-33 11-14

Indian (India) Indian (India) Jamaican Black Lithuanian Bulgarian Bulgarian Hungarian Russian Japanese Russian Romanian Surinam Creole Surinam Hindu Surinam Indonesian Costa Rican Polish Polish Australian White Chinese South Korean Transvaal Black

2.7 1.1 .3 2.5" 3.1 1.3 .5 - .90

.o .4 1.6" - .5 I .9 .7 .9 1.9 I .3 -

-

.n'l

I .4 - 9 4.0

"Average of differences for ages 15 and 16 years; age 17 years was not sampled TABLE XIV Male Body Weight (Kilograms) in Late Adolescence: Average Difference in the Age Biennium 15-17 Years between Urban and Rural Subgroups Weighed during 1950-1980 Sample size Tag

Ethnic group

Time

Urban

Rural

IV-l

South African White Hungarian Indian (India) Indian (India) Bulgarian Bulgarian Hungarian Russian Japanese Surinam Creole Surinam Hindu Surinam Indonesian Costa Rican Polish Chinese South Korean Transvaal Black

1952- I955 1953-1954 1957- 1960 1957-1 960 1960-1 961 1960-1 961 1961-1 963 1962- I964 1963 1964-1965 1964-1965 1 9 6 4 I965 1963-1 969 1967 I975 I976 19761978

360 135 2198 1477 537 610 67 1 362 >7000 1015 38 1 I04 357 I52 606 1 39 1 456

489 I43 888 888 562 562 650 428 >4000 268 692 269 112 388 6149 206 25 1

VI- I 1-10

(-I 1 1-14 1-15 v-2 11-7 1-18 1-21 1-22 1-23 1-25 11-10 1-32 1-33 11-14

Urban minus rural ( 15- I7 years) i .n 1

.o

3.0 2. I 3. I 1.5 1.5 2.4

.a .5 2.4 .6 5.8 .4 3.1 3.0 3.6

114

Howard V. Meredith

18, 21, 22, 23, 25, 32, and 33, also 11-7, 11-10, and 11-14. Average amounts by which urban residents weighed more than rural peers in the successive ontogenetic periods were 1 .O, 2.0, and .9 kg for females, and 1.2, 2.3, and 2.3 kg for males. Taking each sex in turn, the pattern for body weight corresponded with that for standing height. Consequently, the explanatory suggestion made for standing height (Section III,C> becomes plausible for both variables. With rows 1-12, 1-24, 1-34, 1-35, and 1-36 excluded (see Section IV,A), average amounts by which urban inhabitants surpassed rural peers in body weight were 1.4 kg from 50 comparisons for the 1950s, 1.4 kg from 96 comparisons for the 1960s, and 1.9 kg from 35 comparisons for the 1970s. In the aggregate, averages for body weight of urban residents studied during 1950 1980 were greater than those for rural coevals by between 1 . 1 and 1.5 kg in late childhood, near 2.0 kg in early adolescence, and about 1.5 kg in late adolescence. For the three ontogenetic segments together, from 194 tests at the .01 level of significance it was tenable for 148 (76%)to infer that urban residents were heavier than rural coevals. In no instance was it statistically reasonable to infer a lower average body weight for urban than rural residents. D. GROWTH RATE COMPARISONS FOR AGES 8-13 YEARS (FEMALES) AND 10-15 YEARS (MALES)

Tables XV and XVI were constructed to yield statistics for body weight corresponding to those for standing height in Tables VII and VIII. Kilogram and percentage increments were derived using body weight means from urban and rural samples at ages 8 and 13 years for females, 10 and 15 years for males. Each mean was computed from data on more than 150 individuals. As noted in Section IILD, the quinquennial periods dealt with extended similar distances into female and male adolescence. Tables XV and XVI indicated: 1. Means for body weight at age 8 years were lowest for Indian girls, intermediate for Moldavian girls, and highest for New Zealand White girls. At age 10 years, means were distributed from below 23 kg for Indian boys, through about 21 kg for Japanese boys, to near 33 kg for New Zealand White boys. 2. Means for body weight of urban girls age 8 years were higher than those of rural age-sex peers by amounts varying from .1 to 1.4 kg, and averaging .9 kg. Corresponding differences for boys age 10 years averaged .9 kg, and varied from - . I to 2.0 kg. 3. Typical increases in body weight between ages 8 and 13 years were near 18 kg for urban females, and 17 kg for rural females. Between ages 10 and 15 years, typical increases were near 20 and 19 kg for urban and rural males, respectively. 4. On Bulgarian, Chinese, Indian, and Surinam Hindu ethnic groups, kilo-

TABLE XV Means and Gains in Female Body Weight: Sample Size at Age 8 Years, Mean at Age 8 Years, Kilogram Gain from 8 to 13 Years, and Percentage Gain from 8 to 13 Years for Urban and Rural Females Weighed during 1950-1980 ~~

~

Sample size: age 8 years

wl

Gain (%): 8-13 years

Tag

Ethnic group

Urban

Rural

Urban

Rural

Urban

Rural

Urban

Rural

I- 1

French New Zealand White Indian (India) Bulgarian Moldavian Russian Japanese Surinam Creole Surinam Hindu Chinese

>20000 809 7856 207" 227" 168< >2000" 5106 179r 2093

>2000a

23.4 26.5 18.7 25.0 23.1 23.9 22. I 23.4 20.9 21.4

22.8 26.4 17.6 23.9 22.2 22.5 21.4 22.8 19.8 20.1

17.0 20.0 13.8 20.8 13.9 18.4 18.4 19.5 17.5 15.2

16.4 20.1 11.2 18.2 14.7 17.8 18.1 18.7 15.0 13.7

72.6 75.5 73.8 83.2 60.2 77.0 83.3 83.3 83.7 71 .O

71.9 76.1 63.6 76.2 66.2 79.1 84.6 82.0 75.8 68.2

1-5 I I

Mean: age 8 years

Gain (kilograms): 8-1 3 years

1-10 1-14 11-6 1-17 1-18 1-21 1-22 1-32

5536 369c 173' 236~ 183r >30ma 325< 11 196 21010

asample size at age 13 years more than 2000. bSample size at age 13 years between 500 and 900. CSamplesize at age 13 years between 170 and 340.

TABLE XVI Means and Gains in Male Body Weight: Sample Size at Age 10 Years, Mean at Age 10 Years, Kilogram Gain from 10 to 15 Years, and Percentage Gain from 10 to 15 Years for Urban and Rural Males Weighed during 195G1980

Sample size: age 10 years

Q\

Mean: age 10 years

Gain (kilograms): 10-15 years

Gain (%): 1G15 years

Tag

Ethnic group

Urban

Rural

Urban

Rural

Urban

Rural

Urban

Rural

1-1 1-5 1-10 1-14 1-17 1-18 1-22 1-32

French New Zealand White Indian (India) Bulgarian Russian Japanese Surinam Hindu Chinese

2Q00'7 8186 7596 210'-

2000~ 5636 3246 186c 193c 35000 1026c 2093a

28.6 32.7 22.7 31.0 29.2 27.3 25.3 26.2

27.8 32.8 21.7 29.0 28.5 26.6 24.2 24.9

17.1 22.8 14.6 22.2 22.5 22.3 18.4 18.2

16.7 23.0 13.3 20.8 22.1 22.1 15.6 16.2

61.9 69.7 64.3 71.6 77.1 81.7 12.7 69.5

60.1 70.1 61.3 71.7 77.5 83.1 64.5 65.1

l5gC 2500a 206c 2026* ~

4arnple size at age 15 years more than 1200. %irnple size at age 15 years between 450 and 900. '-Sample size at age 15 years between 150 and 400.


I17

gram gains for urban females and males exceeded those for rural peers of like sex by more than 1 .O kg. Japanese, New Zealand White, and Russian ethnic groups of each sex showed little or no difference between the kilogram gains for urban and rural subgroups. In each of the quinquennia studied, the average amount by which urban residents gained more than rural peers was 1. I kg. 5. Expressed in relation to means for body weight at age 8 years, increments in body weight between ages 8 and 13 years varied from about 60 (Moldavian urban females) to 85% (Japanese rural females). Average increments between ages 10 and 15 years were spread from 60 (French rural males) to 83% (Japanese rural males). 6. Average percentage increases were greater for urban than rural residents of the following ethnic groups: Chinese, Indian, and Surinam Hindu females and males; Bulgarian females; and French males. Similar percentage increases were obtained for New Zealand White residents of both sexes; and percentage increases were less for urban than rural Japanese females and males, and Moldavian and Russian females. Overall, large samples studied between 1950 and 1980 showed that in many ethnic groups, but with some exceptions, kilogram and percentage growth rates from late childhood to middle adolescence were higher among urban than rural females and males. From data for body weight and standing height accumulated on Japanese children at ages from 6 to 14 years, Yoshimura (1979) found that 757 urban residents “slightly surpassed” 321 rural peers in “average growth rate.”

V. A.

Differences in Chest Girth COMPARISONS FOR LATE CHILDHOOD

Tables XVII and XVIII were constructed to show average amounts by which, in late childhood, urban girls and boys measured between 1950 and 1980 were larger or smaller than rural coevals in chest girth (thoracic circumference). Sample descriptions and sources were provided in Section III,A. These tables revealed no predominant direction of urban-rural differences in chest size during late childhood. Average chest girth of urban children, compared with that of rural peers, was larger by 1 .O cm or more in five instances, and smaller by 1 .O cm or more in four instances. Differences fell within the limits of - 0.7 and 0.7 cm for 67% of the female comparisons, and 75% of the male comparisons. The 35 sex-specific differences gave a composite average near zero.

Howard V . Meredith

118

Inferences allowable from significance tests at p = .01, using 3.8 cm as population standard deviations (Goldfeld et al., 1965), were as follows: 1. Average chest girth for each sex was larger at Budapest than in Hungarian rural areas (I-4), at Sofia than in Bulgarian villages (1-14), and at Wielkopolski than in a nearby Polish rural district (1-31). 2. At Lublin, compared with Polish villages, average chest girth was larger for girls, but not for boys (1-29). 3. At major Indian cities, compared with Indian rural districts, average chest girth was larger for girls and smaller for boys (1-10). 4. For each sex, average chest girth was smaller at Modena than in an Italian rural region (1-3). 5 . In Chinese, Russian (I-17), and South Korean comparisons, for neither sex was rejection of the null hypothesis warranted. Overall, 16 differences were not statistically significant, 10 were in the urban-larger-than-rural direction, and 9 were in the urban-smaller-than-rural direction,

Although tenable on statistical grounds, several of the foregoing inferences (e.g., inferences 2 and 3, or 1 and 4) appear unlikely biologically. Discussion on

TABLE XVII Female Chest Girth (Centimeters) in Late CHildhood: Average Difference in the Age Triennium from 7 to 10 Years between Urban and Rural Girls Measured during 1950-1980 Sample size Tag 1-3 1-4 1-10 1-11 1-12 1-13 I- 14 1-15 1-16 1-17 1-27 1-29 1-31 1-32 1-33

Ethnic group Italian Hungarian Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Chuvash Russian Indian (India) Polish Polish Chinese South Korean

Time

Urban

1950-1958 195 1- 1958 1957-1960 1957- 1960 1959-1960 1958- 1962 1960-196 1 1960-1961 1960-1961 196 1- 1963 1964-1970 1968- I972 1973-1974 I975 I976

796 225 1309 999 446 470 807 81 1 487 613 1278 >700 ca. 385 8394 I94

Rural

Urban minus rural (7-10 yearsp

>450

-2.0

2257 188 188 ca. 250 419 717 717 484 674 1654 345 ca. 420 8361 230

.I 1.5 .8 .7 - .6 I .3 .2 - .6 .2 - .6 1.1

.7

.o .6

"Each value in this column is the average of four differences (see Table 1, footnote a ) .


119

TABLE XVIII Male Chest Girth (Centimeters) in Late Childhood: Average Difference in the Age Triennium from 8 to I 1 Years between Urban and Rural Boys Measured during 195&1980 Sample size Tag

Ethnic group Italian Hungarian Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Chuvash Russian Russian Moldavian Russian Russian Russian Kirghiz Polish Polish Chinese South Korean

1-3 1-4 1-10 1-1 1 1-12 1-13 1-14 1-15 1-16 11-4 11-5 11-6 1-17 11-7 11-9 11-1 I 1-29 1-31 1-32 1-33

Time

Urban

Rural

195Q- 1958 1951-1958 1957-1 960 1957- 1960 1959- I960 1958- 1962 196&1961 I 9 6 C I96 I 1960-196 1 196Q-I961 1961- 1962 I 96 1- 1963 1961- 1963 1962- 1964 1 9 6 4 1965 1967-1970 1968- I 972 1973-1974 1975 1976

1425 249 1469 1432 443 497 819 833 497 44 I 878 1021 672 45 1 1078 ca. 380 >650 ca. 380 8268 I87

>450 2592 840 840 ca. 250 528 735 735 487 430 400 968 748 532 (:a. 3100 242 345 ca. 400 8379 246

Urban minus rural (8-1 1 yearsp -2.0 1.5 -1.0 - .5

.o - .7 .6 .2 - .7 - .5 -2.8 - .4 - .2 .4 - .4 1.3 .5 .6 - .1 .I

~~

OEach value in this column is the average of four differences (see Table 11, footnote a)

shortcomings of chest girth comparisons will be postponed pending presentation of findings on urban-rural differences in chest girth during adolescence. B.

COMPARISONS FOR ADOLESCENCE

Statistics were brought together in Tables XIX and XX on urban-rural differences in average chest girth during early adolescence. From significance tests at p = .01, using population standard deviations of 4.6 cm for females and 5.0 cm for males (Goldfeld et al., 1965), consistencies and discrepancies were as follows: 1. Chest girth was larger for each sex at Budapest than in Hungarian villages (I-4), at Frunze than in the Kirov rural district (11-1 l), at Lublin than in nearby rural areas (I-29), at Chinese cities than in adjacent rural regions (I-32), and at Seoul than in rural areas of Naju-Gun (1-33). Chest girth was smaller for each sex at Kalinin than in nearby rural locations (11-5).

120

Howard V . Meredith

TABLE XIX Female Chest Girth (Centimeters) in Early Adolescence: Average Difference in the Age Period 10-13 Years between Urban and Rural Subgroups Measured during 1950-1980 Sample size Tag

Ethnic group

Time

Urban

Rural

1-4

Hungarian Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Chuvash Russian Russian Moldavian Russian Russian Russian Kirghiz Polish Polish Chinese South Korean

1951-1958 1957-1960 1957-1960 1959-1 960 1958- 1962 1960- 1961 196C-1961 1960-1961 1960-1 96 1 1961-1 962 1961- 1963 1961- 1963 1962- 1964 1964- 1965 1967- 1970 1968- 1972 1973- 1974 1975 1976

25 1 1092 920 466 469 794 819 494 393 935 1043 679 42 1 1304 ca. 380 >700 ca. 385 8475 265

2568 183 183 ca. 250 555 764 764 478 43 1 417 1083 762 539 (:a. 3250 239 346 ca. 420 8083 270

1-10 1-11 1-12 I- 13 1-14 1- 15 1-16 11-4 11-5 11-6 1-17 11-7 11-9 11-11 1-29 1-31 1-32 1-33

Urban minus rural (1C-13 years)O 2.5 3.4 2.7 1.4

.5 1.3 .4 .1

- .5 -3.4 1.1 .3 .8 - .2 3.7 1 .o

- .5 .7 1 .o

"Same method as in Table 111, footnote a.

2. For Polish (1-12), Bulgarian (1-14, 1-15), and Moldavian (11-6) ethnic groups, chest girth was larger on urban than rural residents of one sex, but not the other. 3. Urban-rural differences were not statistically significant for either sex in Lithuanian (I- 13), Chuvash (I- 16), and Polish (1-3 I) comparisons. 4. In one Indian comparison (I-lo), chest girth was larger for urban than rural females, but smaller for urban than rural males; in another (1-1 I), chest girth was larger for urban than rural females, but not different for urban and rural males. Varying outcomes from Polish comparisons were obtained in 1-12, 1-29, and 1-31.

The average of the 39 differences in Tables XIX and XX was .7 cm. Sixteen differences were not significant statistically, 18 were in the direction of urban chest girth larger than rural, and 5 in the direction of urban chest girth smaller than rural. The number of urban-rural comparisons in common for late childhood and early adolescence was 13 for females, and 18 for males. Row identifications in

Urban-Rural Dixerences in Human Body Growth

121

Tables XVII and XIX were “I” followed by 4 , 10, 11, 12, 13, 14, 15, 16, 17, 29, 31, 32, and 33; and in Tables XVIII and XX, the same “I” tags plus 11-4, 11-5, 11-6, 11-7, and 11-1 1. Pooling for sex, and averaging the two series of 31 differences gave .2 and .8 cm as amounts by which chest girth of urban residents exceeded that of rural peers late childhood and early adolescence, respectively. After insertion of Table XXI, joint examination of Tables XIX, XX, and XXI showed:

I . During early and late adolescence, Bulgarian, Kirghiz, and Polish females surpassed rural coevals in average chest girth by 1.0 cm or more (1-12, 1-14, 11-1 1). Similarly, throughout adolescence urban males in China and South Korea were larger than rural coevals in average chest girth by 1 .O cm or more. 2 . Urban-rural differences in early and late adolescence were near zero for Indian, Russian, and South African White males (1-1 1, 1-17, 11-7, IV-I). Lithuanian urban and rural females had similar average chest girths in early adolescence, but in late adolescence average chest girth was smaller for urban than TABLE XX Male Chest Girth (Centimeters) in Early Adolescence: Average Difference in the Age Period 12-15 Years between Urban and Rural Subgroups Measured during 1950-1980 Sample size Tag

Ethnic group

Time

Urban

Rural

1-4 IV- I 1-10 1-1 I 1-12 1-13 1-14 1-15 1-16 11-4 11-5 11-6 1-17 11-7 IV-2 11-1 I 1-29 1-31 1-32 1-33

Hungarian South African White Indian (India) Indian (India) Polish Lithuanian Bulgarian Bulgarian Chuvash Russian Russian Moldavian Russian Russian Romanian Kirghiz Polish Polish Chinese South Korean

I95 1- 1958 1952- 1955 1957- 1960 1957- I960 195% 1960 1958- 1962 1960- 1961 1960- I961 1960-1961 196C-1961 1961- I962 1961-1 963 1961-1963 1962- I964 1963- 1966 1967- I970 1968-1972 1973-1974 1975 1976

402 305 I I59 1450 403 440 748 848 420 405 1128 849 747 538 >3200 ca. 380 >650 ca. 380 8705 329

I583 400 736 736 ca. 250 617 77 I 77 1 483 432 41 I 1395 837 681 >3600 243 295 ca. 400 8264 258

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