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THE NEUROLOGICAL BASIS OF LEARNING, DEVELOPMENT AND

DISCOVERY


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Science & Technology Education Library

VOLUME 18

SERIES EDITOR

William W. Cobern, Western Michigan University, Kalamazoo, USA

FOUNDING EDITOR

Ken Tobin, University of Pennsylvania, Philadelphia, USA

EDITORIAL BOARD

Henry Brown-Acquay, University College of Education of Winneba, Ghana

Mariona Espinet, Universitat Autonoma de Barcelona, Spain

Gurol Irzik, Bogazici University, Istanbul, Turkey

Olugbemiro Jegede, The Open University, Hong Kong

Reuven Lazarowitz, Technion, Haifa, Israel

Lilia Reyes Herrera, Universidad Autónoma de Columbia, Bogota, Colombia

Marrisa Rollnick, College of Science, Johannesburg, South Africa

Svein Sjøberg, University of Oslo, Norway

Hsiao-lin Tuan, National Changhua University of Education, Taiwan

SCOPE

The book series Science & Technology Education Library provides a publication forum

for scholarship in science and technology education. It aims to publish innovative books

which are at the forefront of the field. Monographs as well as collections of papers will

be published.

The titles published in this series are listed at the end of this volume.


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The Neurological Basis of

Learning, Development andDiscovery

Implications for Science and Mathematics Instruction

by

ANTON E. LAWSON

School of Life Sciences, Arizona State University, U.S.A.

KLUWER ACADEMIC PUBLISHERSNEW YORK, BOSTON, DORDRECHT, LONDON, MOSCOW


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eBook ISBN: 0-306-48206-1Print ISBN: 1-4020-1180-6

©2003 Kluwer Academic PublishersNew York, Boston, Dordrecht, London, Moscow

Print ©2003 Kluwer Academic Publishers

All rights reserved

No part of this eBook may be reproduced or transmitted in any form or by any means, electronic,mechanical, recording, or otherwise, without written consent from the Publisher

Created in the United States of America

Visit Kluwer Online at: http://kluweronline.comand Kluwer's eBookstore at: http://ebooks.kluweronline.com

Dordrecht


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TO

MATT, BOB, BETSY and KRISTINA


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TABLE OF CONTENTS

Preface

Acknowledgements

CHAPTER 1

CHAPTER 2

CHAPTER 3

CHAPTER 4

CHAPTER 5

CHAPTER 6

CHAPTER 7

CHAPTER 8

CHAPTER 9

CHAPTER 10

CHAPTER 11

References

Index

How Do People Learn?

The Neurological Basis of Self-Regulation

Brain Maturation, Intellectual Development andDescriptive Concept Construction

Brain Maturation, Intellectual Development and

Theoretical Concept Construction

Creative Thinking, Analogy and a Neural Model of

Analogical Reasoning

The Role of Analogies and Reasoning Skill in TheoreticalConcept Construction and Change

Intellectual Development During the College Years:

Is There a Fifth Stage?

What Kinds of Scientific Concepts Exist?

Psychological and Neurological Models of ScientificDiscovery

Rejecting Nature of Science Misconceptions By Preservice

Teachers

Implications for The Nature of Knowledge and Instruction

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PREFACE

A goal of mine ever since becoming an educational researcher has been to help

construct a sound theory to guide instructional practice. For far too long, educational

practice has suffered because we have lacked firm instructional guidelines, which in my

view should be based on sound psychological theory, which in turn should be based on

sound neurological theory. In other words, teachers need to know how to teach and that

"how-to-teach" should be based solidly on how people learn and how their brains

function. As you will see in this book, my answer to the question of how people learn is

that we all learn by spontaneously generating and testing ideas. Idea generating

involves analogies and testing requires comparing predicted consequences with actual

consequences. We learn this way because the brain is essentially an idea generating and

testing machine. But there is more to it than this. The very process of generating and

testing ideas results not only in the construction of ideas that work (i.e., the learning of

useful declarative knowledge), but also in improved skill in learning (i.e., the

development of improved procedural knowledge). Thus, to teach most effectively,

teachers should allow their students to participate in the idea generation and testingprocess because doing so allows them to not only construct "connected" and useful

declarative knowledge (where "connected" refers specifically to organized neuron

hierarchies called outstars), but also to develop "learning-to-learn" skills (where

"learning-to-learn" skills refer to general rules/guidelines that are likely located in the

prefrontal cortex).

My interest in the neurological basis of instruction can be traced to a 1967 book

written by my biologist father, the late Chester Lawson, titled Brain Mechanisms and

Human Learning published by Houghton Mifflin. Although the book was written while

I was still in high school, in subsequent years my father and I had many longconversations about brain structure and function, learning and development, and what it

all meant for education. In fact, in that book, my father briefly outlined a theory of

instruction that has subsequently been called the learning cycle. That instructional

theory was put into practice by my father, by Robert Karplus and by others who worked

on the Science Curriculum Improvement Study during the 1970s. My mathematician

brother David Lawson has also boosted my interest in such issues. David worked on

NASA's Space Station Program and is an expert in neural modeling. His help has been

invaluable in sorting out the nuances of neural models and their educational

implications.Given this background, Chapter 1 begins by briefly exploring empiricism,

innatism and constructivism as alternative explanations of learning. Empiricism claims

learning results from the internalization of patterns that exist in the external world.

Innatism claims that such patterns are internal in origin. Constructivism views learning

as a process in which spontaneously generated ideas are tested through the derivation of


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expectations. The initial ideas are retained or rejected depending upon the extent that

their expectations match future observations in an assumed-to-exist external world.

Piaget's brand of constructivism with its theory of self-regulation is discussed as an

explanation for development and learning. Piaget's self-regulation theory is based onbiological analogies, largely on Waddington's theory of genetic assimilation. Genetic

assimilation is described and used to explain psychological-level phenomena,

specifically the development of proportional reasoning skill during adolescence. In

spite of the value of self-regulation theory, an important theoretical weakness exists as

the theory is based on biological analogies rather than on brain structure and function.

Brain structure and function are discussed in Chapter 2 to hopefully eliminate this

weakness.

Chapter 2 explains visual and auditory information processing in terms of basic

brain structure and function. In brief, a hypothetico-predictive pattern is identified in

both visual and auditory processing. Steven Grossberg's neural modeling principles of

learning, perception, cognition, and motor control are presented as the basis for

construction of a neurological model of sensory-motor problem solving. The pattern of

problem solving is assumed to be universal, thus is sought in the higher-order shift from

the child's use of an additive strategy to the adolescent's use of a proportions strategy to

solve Suarez and Rhonheimer's Pouring Water Task. Neurological principles involved

in this shift and in the psychological process of self-regulation are discussed, as are

educational implications. The conclusion is drawn that reasoning is hypothetico-predictive in form because that is the way the brain works.

Many adolescents fail when attempting to solve descriptive concept

construction tasks that include exemplars and non-exemplars of the concepts to be

constructed. Chapter 3 describes an experiment that tested the hypothesis that failure is

caused by lack of developmentally derived, hypothetico-predictive reasoning skill. To

test this developmental hypothesis, individually administered training sessions

presented a series of seven descriptive concept construction tasks to students (ages five

to fourteen years). The sessions introduced the hypothetico-predictive reasoning pattern

presumably needed to test task features. If the developmental hypothesis is correct, thenthe brief training should not be successful because developmental deficiencies in

reasoning presumably cannot be remedied by brief training. Results revealed that none

of the five and six-year-olds, approximately half of the seven-year-olds, and virtually all

of the students eight years and older responded successfully to the brief training.

Therefore, the results contradicted the developmental hypothesis, at least for students

older than seven years. Previous research indicates that the brain's frontal lobes undergo

a pronounced growth spurt from about four to seven years of age. In fact, performance

of normal six-year-olds and adults with frontal lobe damage on tasks such as the

Wisconsin Card Sorting Task, a task similar to the present descriptive concept

construction tasks, has been found to be identical. Consequently, the present results

support the hypothesis that the striking improvement in task performance found at age

seven is linked to maturation of the frontal lobes. A neural network of the role the

frontal lobes play in task performance is presented. The advance in reasoning that


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presumably results from effective operation of the frontal lobes is seen as a fundamental

advance in intellectual development because it enables children to employ hypothetico-

predictive reasoning to change their "minds" when confronted with contradictory

evidence regarding features of perceptible objects, a reasoning pattern necessary for

descriptive concept construction. Presumably, a further qualitative advance inintellectual development occurs when some students derive an analogous, but more

advanced pattern of reasoning, and apply it to derive an effective problem-solving

strategy to solve the descriptive concept construction tasks when training is not

provided.

Chapter 4 describes an experiment testing the hypothesis that an early

adolescent brain growth plateau and spurt influences the development of higher-level

hypothetico-predictive reasoning skill and that the development of such reasoning skill

influences one's ability to construct theoretical concepts. In theory, frontal lobe

maturation during early adolescence allows for improvements in one's abilities to

coordinate task-relevant information and inhibit task-irrelevant information, which

along with both physical and social experience, influence the development of reasoning

skill and one's ability to reject misconceptions and accept scientific conceptions. A

sample of 210 students ages 13 to 16 years enrolled in four Korean secondary schools

were administered four measures of frontal lobe activity, a test of reasoning skill, and a

test of air-pressure concepts derived from kinetic-molecular theory. Fourteen lessons

designed to teach the theoretical concepts were then taught. The concepts test was re-

administered following instruction. As predicted, among the 13 and 14-year-olds,performance on the frontal lobe measures remained similar, or decreased. Performance

then improved considerably among the 15 and 16-year-olds. Also as predicted, the

measures of frontal lobe activity correlated highly with reasoning skill. In turn,

prefrontal lobe function and reasoning skill predicted concept gains and posttest

concept performance. A principal components analysis found two main components,

which were interpreted as representing and inhibiting components. Theoretical concept

construction was interpreted as a process involving both the representation of task-

relevant information (i.e., constructing mental representations of new scientific

concepts) and the inhibition of task-irrelevant information (i.e., the rejection of previously-acquired misconceptions).

Chapter 5 presents a model of creative and critical thinking in which people use

analogical reasoning to link planes of thought and generate new ideas that are then

tested by employing hypothetico-predictive reasoning. The chapter then extends the

basic neural modeling principles introduced in Chapter 2 to provide a neural level

explanation of why analogies play such a crucial role in science and why they greatly

increase the rate of learning and can, in fact, make classroom learning and retention

possible. In terms of memory, the key point is that lasting learning results when a match

occurs between sensory input from new objects, events, or situations and past memoryrecords of similar objects, events, or situations. When such a match occurs, an adaptive

resonance is set up in which the synaptic strengths of neurons increase), thus a record of

the new input is formed in longterm memory. Neuron systems called outstars and

instars presumably enable this to occur. Analogies greatly facilitate learning and


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retention because they activate outstars (i.e., the cells that are sampling the to-be-

learned pattern) and cause the neural activity to grow exponentially by forming

feedback loops. This increased activity boosts synaptic strengths, thus causes storage

and retention in long -term memory.In Chapter 6, two hypotheses about theoretical concept construction, conceptual

change and application are tested. College biology students classified at different levels

of reasoning skill were first taught two theoretical concepts (molecular polarity and

bonding) to explain the mixing of dye with water, but not with oil, when all three were

shaken in a container. The students were then tested in a context in which they applied

the concepts in an attempt to explain the gradual spread of blue dye in standing water.

Next students were taught another theoretical concept (diffusion), with and without the

use of physical analogies. They were retested to see which students acquired the

concept of diffusion and which students changed from exclusive use of the polarity and

bonding concepts (i.e., misconceptions) to the scientifically more appropriate use of the

diffusion concept to explain the dye's gradual spread. As predicted, the

experimental/analogy group scored significantly higher than the control group on a

posttest question that required the definition of diffusion. Also as predicted, reasoning

skill level was significantly related to a change from the application of the polarity and

bonding concepts to the application of the diffusion concept to explain the dye's gradual

spread. Thus, the results support the hypotheses that physical analogies are helpful in

theoretical concept construction and that higher-order, hypothetico-predictive reasoning

skill facilitates conceptual change and successful concept application.

Chapter 7 describes research aimed at testing the hypothesis that two general

developmentally based levels of causal hypothesis-testing skill exist. The first

hypothesized level (i.e., Level 4, which corresponds generally to Piaget's formal

operational stage) presumably involves skill associated with testing causal hypotheses

involving observable causal agents, while the second level (i.e., Level 5, which

corresponds to a fifth, post-formal stage) presumably involves skill associated with

testing causal hypotheses involving unobservable entities. To test this fifth-stage

hypothesis, a hypothesis-testing skill test was developed and administered to a largesample of college students both at the start and at the end of a biology course in which

several hypotheses at both causal levels were generated and tested. The predicted

positive relationship between causal hypothesis-testing skill and performance on a

transfer problem involving the test of a causal hypothesis involving unobservable

entities was found. The predicted positive relationship between causal hypothesis-

testing skill and course performance was also found.

Scientific concepts can be classified as descriptive (e.g., concepts such as

predator and organism with directly observable exemplars) or theoretical (e.g., concepts

such as atom and gene without directly observable exemplars). Understandingdescriptive and theoretical concepts has been linked to students' developmental stages,

presumably because the procedural knowledge structures (i.e., reasoning patterns) that

define developmental stages are needed for concept construction. Chapter 8 describes

research that extends prior theory and research by postulating the existence of an


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intermediate class of concepts called hypothetical (e.g., concepts such as subduction

and evolution with exemplars that can not in practice be observed due to limits on the

normal observational time frame). To test the hypothesis that three kinds of scientific

concepts exist, we constructed and administered a test of the concepts introduced in a

college biology course. As predicted, descriptive concept questions were significantly

easier than hypothetical concept questions, than were theoretical concept questions.

Further, because concept construction presumably depends in part on reasoning skill,

students at differing reasoning skill levels (Levels 3, 4 and 5, where Level 5 is

conceptualized as 'post-formal' in which hypotheses involving unseen entities can be

tested) were predicted to vary in the extent to which they succeeded on the concepts

test. As predicted, a significant relationship (p < 0.001) was found between conceptual

knowledge and reasoning skill level. This result replicates previous research, therefore

provides additional support for the hypothesis that procedural knowledge skillsassociated with intellectual development play an important role in declarative

knowledge acquisition, i.e., in concept construction. The result also supports the

hypothesis that intellectual development continues beyond the 'formal' stage during the

college years, at least for some students.

Chapter 9 considers the nature of scientific discovery. In 1610, Galileo Galilei

discovered Jupiter's moons with the aid of a new more powerful telescope of his

invention. Analysis of his report reveals that his discovery involved the use of at leastthree cycles of hypothetico-predictive reasoning. Galileo first used hypothetico-

predictive reasoning to generate and reject a fixed-star hypothesis. He then generatedand rejected an ad hoc astronomers-made-a-mistake hypothesis. Finally, he generated,

tested, and accepted a moon hypothesis. Galileo's reasoning is modeled in terms of

Piaget's self-regulation theory, Grossberg's theory of neurological activity, Levine &

Prueitt's neural network model and Kosslyn & Koenig's model of visual processing.

Given that hypothetico-predictive reasoning has played a role in other important

scientific discoveries, the question is asked whether it plays a role in all scientific

discoveries. In other words, is hypothetico-predictive reasoning the essence of the

scientific method? Possible alternative scientific methods, such as Baconian induction

and combinatorial analysis, are explored and rejected as viable alternatives. The "logic"of scientific discovery and educational implications are discussed.

Instructional attempts to provoke preservice science teachers to reject nature-of-

science (NOS) misconceptions and construct more appropriate NOS conceptions have

been successful only for some. Chapter 10 describes a study that asked, why do some

preservice teachers make substantial NOS gains, while others do not? Support was

found for the hypothesis that making NOS gains as a consequence of instruction

requires prior development of Stage 5 reasoning skill, which some preservice teachers

lack. In theory, science is an enterprise in which scientists often use Stage 5 reasoning

to test alternative hypotheses regarding unobservable theoretical entities. Thus, anyone

lacking Stage 5 reasoning skill should be unable to assimilate this aspect of the nature

of science and should be unable to reject previously constructed NOS misconceptions

as a consequence of relatively brief instruction. As predicted, the study found the

predicted positive relationship between reasoning skill (Levels 3, 4 and 5) and NOS


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gains as a consequence of instruction. Preservice teachers who lack Stage 5 reasoning

skill can be expected to find it difficult to teach science as a process of inquiry when

they become teachers.

Chapter 11 begins with a brief summary of the neurological principles andresearch introduced in the previous chapters and with their key instructional

implications. The chapter then offers a resolution to the current debate between

constructivists and realists regarding the epistemological status of human knowledge.

As we have seen, knowledge acquisition follows a hypothetico-predictive form in

which self-generated ideas/representations are tested by comparing expected and

observed outcomes. Ideas may be retained or rejected, but cannot be proved or

disproved. Therefore, absolute Truth about any and all ideas, including the idea that the

external world exists, is unattainable. Yet learning at all levels above the sensory-motor

requires that one assume the independent existence of the external world because only

then can the behavior of the objects in that world be used to test subsequent higher-

order ideas. In the final analysis, ideas - including scientific hypotheses and theories -

stand or fall, not due to social negotiation, but due to their ability to predict future

events. Although this knowledge construction process has limitations, its use

nevertheless results in increasingly useful mental representations about an assumed to

exist external world as evidenced by technological progress that is undeniably based on

sound scientific theory. An important instructional implication is that instruction should

become committed to helping students understand the crucial role that hypotheses,predictions and evidence play in learning. Further, instruction that allows, indeed

demands, that students participate in this knowledge construction process enables them

to undergo self-regulation and develop both general procedural knowledge structures

(i.e., reasoning skills) and domain-specific concepts and conceptual systems. Examples

of effective instruction are provided.

As you will see, this book includes fairly detailed accounts of specific research

studies. The studies provide examples of how hypothetico-predictive research can be

conducted and reported in science and mathematics education. In my view, too few

such studies are designed and written in this hypothetico-predictive manner, and sufferas a consequence. In fact, in my view the entire field suffers as a consequence. Thus, a

secondary goal of this book is to encourage other researchers to adopt the hypothetico-

predictive approach to their research and writing.


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Acknowledgements

I would like to thank William Cobern, Series Editor, for asking me to write this

book, Michel Lokhorst, Publishing Editor of Kluwer Academic Publishers, for his

expert help in seeing the project to completion, Irene van den Reydt of Kluwer's Social

Sciences Unit for helping with the review process, Chula Eslamieh for her help in

preparing the final manuscript, and two anonomous reviewers for their many helpful

comments. Thanks also to Anne Rowsey, Laural Casler and Cameo Hill of the Arizona

State University Life Sciences Visualization Laboratory for their graphic illustration

work that appears in the book and to several colleagues who have contributed to the

ideas and research presented. These include John Alcock, Souheir Alkoury, William

Baker, Russell Benford, Margaret Burton, Brian Clark, Erin Cramer-Meldrum, Lisa

DiDonato, Roy Doyle, Kathleen Falconer, Bart James, Margaret Johnson, LawrenceKellerman, Yong-Ju Kwon, David Lawson, Christine McElrath, Birgit Musheno,

Ronald Rutowski, Jeffery Sequist, Jan Snyder, Michael Verdi, Warren Wollman and

Steven Woodward.

An additional thank you is due to the National Science Foundation (USA) under

grant No. DUE 0084434 and to the editors and publishers of the articles appearing

below as several of the chapters contain material based on those articles:

Lawson, A.E. & Wollman, W.T. (1976). Encouraging the transition from concrete to formal cognitive

functioning - an experiment. Journal of Research in Science Teaching, 13(5), 413-430.

Lawson, A.E. (1982). Evolution, equilibration, and instruction. The American Biology Teacher, 44(7), 394-

405.

Lawson, A.E. (1986). A neurological model of problem solving and intellectual development. Journal of

Research in Science Teaching, 23(6), 503-522.

Lawson, A.E., McElrath, C.B., Burton, M.S., James, B.D., Doyle, R.P., Woodward, S.L., Kellerman, L. &

Snyder, J.D. (1991). Hypothetico-deductive reasoning and concept acquisition: Testing a constructivist

hypothesis. Journal of Research in Science Teaching, 28(10), 953-970.

Lawson, A.E. (1993). Deductive reasoning, brain maturation, and science concept acquisition: Are they

linked? Journal of Research in Science Teaching, 30(9), 1029-1052.

Lawson, D.I. & Lawson, A.E. (1993). Neural principles of memory and a neural theory of analogical insight.

Journal of Research in Science Teaching, 30(10), 1327-1348.

Lawson, A.E., Baker, W.P., DiDonato, L., Verdi, M.P. & Johnson, M.A. (1993). The role of physical

analogues of molecular interactions and hypothetico-deductive reasoning in conceptual change.

Journal of Research in Science Teaching, 30(9), 1073-1086.

Lawson, A.E. (1999). What should students learn about the nature of science and how should we teach it?

Journal of College Science Teaching, 28(6), 401-411.

Musheno, B.V., & Lawson, A.E. (1999). Effects of learning cycle and traditional text on comprehension of science concepts by students at differing reasoning levels. Journal of Research in Science Teaching,

36(1), 23-37.

Kwon, Yong-Ju & Lawson, A.E. (2000). Linking brain growth with scientific reasoning ability and

conceptual change during adolescence. Journal of Research in Science Teaching, 37(1), 44-62.


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Lawson, A.E. (2000). The generality of hypothetico-deductive reasoning: Making scientific thinking explicit.

The American Biology Teacher, 62(7), 482-495.

Lawson, A.E., Clark, B., Cramer-Meldrum, E., Falconer, K.A., Kwon, Y.J., & Sequist, J.M. (2000). The

development of reasoning skills in college biology: Do two levels of general hypothesis-testing skills

exist? Journal of Research in Science Teaching, 37(1), 81-101.

Lawson, A.E., Alkhoury, S., Benford, R., Clark, B. & Falconer, K.A. (2000). What kinds of scientific

concepts exist? Concept construction and intellectual development in college biology. Journal of

Research in Science Teaching, 37(9), 996-1018.

Lawson, A.E. (2000). How do humans acquire knowledge? And what does that imply about the nature of

knowledge? Science & Education, 9(6), 577-598.

Lawson, A.E. (2001). Promoting creative and critical thinking in college biology. Bioscene: Journal of

College Biology Teaching, 27(1), 13-24.

Lawson, A.E. (2002). What does Galileo's discovery of Jupiter's moons tell us about the process of scientific

discovery? Science & Education, 11, 1-24.

Anton E. Lawson

Department of Biology

Arizona State University

Tempe, AZ, USA 85287-1501

September, [email protected]


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CHAPTER 1

HOW DO PEOPLE LEARN?

1. INTRODUCTION

Years ago while teaching junior high school math and science, two events

occurred that made a lasting impression. The first occurred during an eighth grade

math class. We had just completed a chapter on equivalent fractions and the students

did extremely well on the chapter test. As I recall, the test average was close to 90%.

The next chapter introduced proportions. Due to the students' considerable success

on the previous chapter and due to the similarity oftopics, I was dumbfounded when

on this chapter test, the test average dropped below 50%. What could have caused

such a huge drop in achievement? The second event occurred during a seventh grade

science class. I cannot recall the exact topic, but I will never forget the student. I was

asking the class a question about something that we had discussed only the day

before. When I called on a red-haired boy named Tim, he was initially at a loss for

words. So I rephrased the question and asked again. Again Tim was at a loss for

words. This surprised me because the question and its answer seemed, to me at least,

rather straightforward, and Tim was a bright student. So I pressed on. Again I

rephrased the question. Surely, I thought, Tim would respond correctly. Tim did

respond. But his response was not correct. So I gave him some additional hints and

tried again. But this time before he could answer, tears welled up in his eyes and he

started crying uncontrollably. I was shocked by his tears and needless to say, have

never again been so persistent in putting a student on the spot. However, in my

defence, I was so certain that I could get Tim to understand and respond correctly

that it did not dawn on me that I would fail. What could have gone wrong?

Perhaps you, like me, have often been amazed when alert and reasonably brightstudents repeatedly do not understand what we tell them, in spite of having told them

over and over again, often using what we believe to the most articulate and clear

presentations possible, sometimes even with the best technological aids. If this sounds

familiar, then this book is for you. The central pedagogical questions raised are these:

Why does telling not work? Given that telling does not work, what does work? And

given that we can find something that does work, why, in both psychological and

neurological terms, does that something work? In short, the primary goal is to explicate

a theory of development, learning and scientific discovery with implications for

teaching mathematics and science. The theory will be grounded in what is currentlyknown about brain structure and function. In a sense, the intent is to help teachers better

1


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2 CHAPTER 1

understand effective teaching methods as well as provide both psychological and

neurological level explanations for why those methods work.

We begin with a brief look at three alternative views of how people learn. This will

be followed by a discussion of initial implications for higher-order cognition and formath and science instruction. Chapter 2 will introduce neural network theory with the

intent of explaining learning in neurological terms. Subsequent chapters will expand on

these and related ideas in the context of math and science instruction and in the context

of scientific discovery.

2. EMPIRICISM, INNATISM AND CONSTRUCTIVISM

An early answer to the question of how people learn, known as empiricism, claims

that knowledge is derived directly from sensory experience. Although there are

alternative forms of empiricism espoused by philosophers such as Aristotle, Berkeley,

Hume and Locke of Great Britain, and by Ernst Mach and the logical positivists of

Austria, the critical point of the empiricist doctrine is that the ultimate source of

knowledge is the external world. Thus, the essence of learning is the internalization of

representations of the external world gained primarily through keen observation.

Innatism in its various forms stands in stark opposition to empiricism. Innatism's basic

claim is that knowledge comes from within. Plato, for example, argued for the existence

of innate ideas that "unfold" with the passage of time. For a more modern innatist viewsee, for example, Chomsky and Foder (in Piattelli-Palerini, 1980). A third alternative,

sometimes referred to as constructivism, argues that learning involves a complex

interaction of the learner and the environment in which contradicted self-generated

behaviors play a key role (cf., Piaget, 1971a; Von Glasersfeld, 1995; Fosnot, 1996).1

What are we to make of these widely divergent positions? Consider the following

examples.

Van Senden (in Hebb, 1949) reported research with congenitally blind adolescents

who had gained sight following surgery. Initially these newly sighted adolescents could

not visually distinguish a key from a book when both lay on a table in front of them.They were also unable to report seeing any difference between a square and a circle.

Only after considerable experience with the objects, including touching and holding

them, were they able to "see" the differences. In a related experiment, microelectrodes

were inserted into a cat's brain (Von Foerster, 1984). The cat was then placed in a cage

with a lever that dispensed food when pressed, but only when a tone of 1000 h2 was

produced. In other words, to obtain food the cat had to press the lever while the tone

was sounding. Initially the electrodes indicated no neural activity due to the tone.

However, the cat eventually learned to press the lever at the correct time. And from that

point on, the microelectrodes showed significant neural activity when the tone sounded.

1 A philosophical examination of alternative forms of constructivism can be found in Matthews (1998). Discussion of some of these

alternatives will be saved for Chapter 11. For now it suffices to say that the present account rejects extreme forms of constructivism that

in turn reject or downplay the importance of the external world in knowledge acquisition.


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HOW DO PEOPLE LEARN? 3

In other words, the cat was "deaf" to the tone until the tone was of some consequence to

the cat! In more general terms, it appears that a stimulus is not a stimulus unless some

prior "mental structure" exists that allows its assimilation.

What about the innatist position? Consider another experiment with cats. In thisexperiment one group was reared in a normal environment. Not surprisingly, cells in the

cats' brains became electrically active when the cats were shown objects with vertical

lines. Another group was reared to the same age in an artificial environment that lacked

vertical lines. Amazingly, the corresponding cells of these cats showed no comparable

activity when they were shown identical objects. Thus, in this case at least, it would

seem that the mere passage of time is not sufficient for the cat's brain cells to become

"operational," i.e., for their mental structures to "unfold."

Next, consider a human infant learning to orient his bottle to suck milk. Jean Piaget

made several observations ofhis son Laurent from seven to nine months of age. Piaget

(1954, p. 31) reports as follows:

From 0:7 (0) until 0:9 (4) Laurent is subjected to a series of tests, either before the meal

or at any other time, to see if he can turn the bottle over and find the nipple when he

does not see it. The experiment yields absolutely constant results; if Laurent sees the

nipple he brings it to his mouth, but if he does not see it he makes no attempt to turn

the bottle over. The object, therefore, has no reverse side or, to put it differently, it is not

three-dimensional. Nevertheless Laurent expects to see the nipple appear and evidently

in this hope he assiduously sucks the wrong end of the bottle.

Laurent's initial behavior consists of lifting and sucking whether the nipple is

properly oriented or not. Apparently Laurent does not notice the difference between the

bottom ofthe bottle and the top and/or he does not know how to modify his behaviour

to account for presentation of the bottom. Thanks to his father, Laurent has a problem.

Let's return to Piaget's experiment to see how the problem was solved.

On the sixth day when the bottom ofthe bottle is given to Laurent".... he looks at it,

sucks it (hence tries to suck glass!), rejects it, examines it again, sucks it again, etc.,

four or five times in succession" (p.127). Piaget then holds the bottle out in front of

Laurent and allows him to simultaneously look at both ends. Laurent's glare oscillatesbetween the bottle top and bottom. Nevertheless, when the bottom is again presented,

he still tries to suck the wrong end. The bottom of the bottle is given to Laurent on the

11th, 17th, and 21st days of the experiment. Each time Laurent simply lifts and sucks

the wrong end. But on the 30th day, Laurent "...no longer tries to suck the glass as

before, but pushes the bottle away, crying" (p. 128). Interestingly, when the bottle is

moved a little farther away, "...he looks at both ends very attentively and stops crying"

(p. 128). Finally, two months and ten days after the start of the experiment when the

bottom of the bottle is presented, Laurent is successful in first flipping it over as he

"...immediately displaces the wrong end with a quick stroke ofthe hand, while lookingbeforehand in the direction of the nipple. He therefore obviously knows that the

extremity he seeks is at the reverse end ofthe object" (pp. 163-164).

Lastly, consider a problem faced by my younger son when he was a 14-month old

child playing with the toy shown in Figure 1. Typically he would pick up the cylinder


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sitting at the top left and hunt for a hole to drop it in. At first, he was unable to locate

the correct hole even though it was directly below where he had just picked up the

cylinder. Even, if by chance, he happened to find the correct hole, he was unable to

orient the cylinder to make it fit. Nevertheless, with my help, he achieved some success.When he placed the cylinder above the correct hole, I gently pushed the object so that it

would fit. Then, when he let go, the cylinder dropped out of sight. He was delighted.

Success! Next, he picked up the rectangular solid. Which hole do you think he tried to

drop it in? Should he drop it into the hole below the rectangular solid? He did not even

consider that hole even though (to us) it clearly is the correct choice. Instead, he tried

repeatedly to drop it into the round hole. Presumably this was because that behavior

(placing an object above the round hole and letting go) had previously led to success. In

other words, he responded to the new situation by using his previously successful

behavior. Of course when the rectangular object was placed over the round hole, it didnot fit. Hence, his previously successful behavior was no longer successful. Instead it

was "contradicted." Further, only after numerous contradictions was he willing to try

another hole. I tried showing him which holes the various objects would go into, but to

no avail. He had to try it himself - he had to act - to behave. In other words, the child

learned from his behaviours. Only after repeated incorrect behaviors and contradictions

did he find the correct holes.

The previous examples suggest that knowledge acquisition is not merely a matter of

direct recording of sensory impressions, nor is the mere passage of time sufficient for


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innate structures to become functional. Rather, acquiring new knowledge appears to

involve a complex "construction" process in which initially undifferentiated sensory

impressions, properties of the developing organism's brain and the organism's

unsuccessful (i.e., contradicted) behaviors interact in a dynamic and changingenvironment.

3. AN EXPLORATION INTO KNOWLEDGE CONSTRUCTION

To provide an additional insight into the knowledge construction process, take a

few minutes to try the task presented in Figure 2. You will need a mirror. Once you

have a mirror, place the figure down in front of it so that you can look into the mirror at

the reflected figure. Read and follow the figure's reflected directions. Look only in the

mirror - no fair peeking directly at your hand. When finished, read on.


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How did you do? If you are like most people, the task proved rather difficult and

frustrating. Of course, this should come as no surprise. After all, you have spent a

lifetime writing and drawing without a mirror. So what does this mirror-drawing task

reveal about learning?I think it reveals the basic knowledge construction pattern depicted in Figure 3 and

described as follows: First, the reflected images are "assimilated" by specific mental

structures that are currently part of your long-term memory. Assimilation is an

immediate, automatic and subconscious process. The activated mental structure then

drives behavior that, in the past, has been linked to a specific consequence (i.e., an

actual outcome of that behavior when used in the prior contexts). Thus, when the

structure is used to drive behavior in the present context, the behavior is linked to those

prior consequences. In this sense, the behavior carries with it an expectation, a

prediction, i.e., what you expect/predict you will see as a consequence of the behavior.All is well if the behavior is successful - that is if the actual outcome matches the

expected outcome. However, if unsuccessful, that is if the actual outcome does not

match the expectation/prediction (e.g., you move your hand down and to the right and

you expect to see a line drawn up and to the left, but instead you see one drawn up and

to the right), contradiction results. This contradiction then drives a subconscious search

for another mental structure and perhaps drives a closer inspection of the figure until

either another structure is found that works (in the sense that it drives successful, non-

contradicted behavior), or you become so frustrated that you quit. In which case, your

mental structures will not undergo the necessary change/accommodation. In other

words, you won't learn to draw successfully in a mirror.

The above process can be contrasted with one in which the learner first looks at a

reflected image. But not being certain how to draw the image, s/he looks again and

again. With each additional look, the learner gathers more and more information about

the image until s/he is confident that s/he can draw it successfully. Finally, at this point,

the learner acts and successfully draws the reflected image. In contrast with the trial-

and-error process depicted in Figure 2, this view of learning can be characterized as

inductive. Which process best characterizes your efforts at mirror drawing?Quite obviously, mirror drawing is a sensory-motor task that need not involve

language. Nevertheless, if we were try to verbalize the steps involved in one attempt to

draw a diagonal line, they may go something like this:

If...I have assimilated the present situation correctly, (initial idea)

and...I move my hand down and to the right, (behavior)

then...I should see a diagonal line go up and to the left. (expectation)

But...the actual line goes up and to the right! (actual outcome)

Therefore...I have not assimilated the situation correctly. I need to try something else.(conclusion)

The important point is that the mind does not seem to work the way you might

think. In other words, the mind does not prompt you to look, look again, and look still


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again until you somehow derive a successful behaviour from the environment in some

sort of inductivist manner. Rather, the mind seems to prompt you to look and as a

consequence of this initial look, the mind generates an initial idea that then drives

behavior. Hopefully the behavior is successful. But sometimes it is not. In other words,you tried something and found it in error. So the contradicted behavior then prompts the

mind to generate another idea and so on until eventually the resulting behavior is not

contracted. In short, we learn from our mistakes - from what some would call trail and

error.


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4. IS THE IF/THEN/THEREFORE PATTERN ALSO AT WORK

IN PRACTICAL PROBLEM SOLVING?

Can we find this pattern of If/then/Therefore thinking in cases of everyday problemsolving? Consider a personal example that we might call the case of the unlit barbecue.

Before I arrived home one evening, my wife had lit the gas barbecue in the backyard

and put some meat on for dinner. Upon arriving, she asked me to check the meat. When

doing so, I noticed that the barbecue was no longer lit. It was windy so I suspected that

the wind had blown out the flames - as it had a few times before. So I tried to relight the

barbecue by striking a match and inserting its flame into a small "lighting" hole just

above one of the unlit burners. But the barbecue did not relight. I tried a second, and

then a third match. But it still did not relight. At this point, I suspected that the tank

might be out of gas. So I lifted the tank and sure enough it lifted easily - as though it

were empty. I then checked the lever-like gas gauge and it was pointed at empty. So it

seemed that the barbecue was no longer lit, not because the wind had blown out its

flames, but because its tank was out of gas.

What pattern of thinking was guiding this learning? Retrospectively, it would seem

that thinking was initiated by a causal question, i.e., why was the barbecue no longer

lit? In response to this question, my reconstructed thinking goes like this:

If...the wind had blown out the flames, (wind hypothesis)

and...a match is used to relight the barbecue, (test condition)

then...the barbecue should relight. (expected result)

But ...when the first match was tried, the barbecue did not relight. (observed result)

Therefore...either the wind hypothesis is wrong or something is wrong with the test.

Perhaps the match flame went out before it could ignite the escaping gas. This seems

plausible as the wind had blown out several matches in the past. So retain the wind

hypothesis and try again. (conclusion)

Thus,if ...the wind had blown out the flames,

and...a second match is used to relight the barbecue,

then...the barbecue should relight.

But ...when the second match was used, the barbecue still did not relight.

Therefore...once again, either the wind hypothesis is wrong or something is wrong with

the test. Although it appeared as though the inserted match flame reached the unlit

burner, perhaps it nevertheless did get blown out. So again retain the wind hypothesis

and repeat the experiment. But this time closely watch the match flame to see if it does

in fact reach its destination.

Thus,

if...the wind had blown out the flames,

and...a third match is used to relight the barbecue while closely watching the flame,


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then...the flame should reach its destination and barbecue should relight.

But ...when the third match was used while closely watching the flame, the flame

appeared to reach its destination, but the barbecue still did not relight.

Therefore...apparently there was nothing wrong with the test. Instead the windhypothesis is probably wrong and another hypothesis is needed.

Perhaps the tank was out of gas. Thus,

if ...the tank is out of gas, (empty-tank hypothesis)

and ...the tank is lifted,

then...it should feel light and should lift easily.

And...when the tank was lifted, it did feel light and did lift easily.

Therefore...the empty tank hypothesis is supported.

Further,

if...the tank is out of gas,

and ...the gas gauge is checked,

then...it should be pointed at empty.

And...it was pointed at empty.

Therefore...the empty-tank hypothesis is supported once again.

5. THE ELEMENTS OF LEARNING

The introspective analysis suggests that learning (i.e., knowledge construction)

involves the generation and test of ideas and takes the form of several If/then/Therefore

arguments that can be called hypothetico-predictive (or hypothetico-deductive if you

prefer). However, notice that the attainment of evidence contradicting the initial wind

explanation (i.e., hypothesis) did not immediately lead to its rejection. This is because

the failure of an observed result to match an expected result can arise from one of two

sources - a faulty explanation or a f aulty test. Consequently, before a plausible

explanation is rejected, one has to be reasonably sure that the test was not faulty.In short, learning seems to involve the following elements:

Making an Initial Puzzling Observation - In this case, the puzzling observation

is that the barbecue is no longer lit. The observation is puzzling because it is

unexpected (i.e., I would not expect my wife to be trying to cook meat on an unlit

grill). Unexpected observations are cognitively motivating in the sense that they

require an explanation. Of course in this instance, motivation can also come from

one's hunger and/or a desire to keep one's wife happy.

Raising a Causal Question - Why is the barbecue no longer lit? In this case, the

causal follows more or less automatically from the puzzling observation.However, in other instances, generating a clear statement of the causal question

may be much more difficult.

Generating a Possible Cause (an explanation) - In this case the initial

explanation (i.e., hypothesis) was that the barbecue was no longer lit because the

1.

2.

3.


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wind had blown out its flames. The process ofexplanation/hypothesis generation

is seen as one involving analogies, analogical transfer, analogical reasoning i.e.,

borrowing ideas that have been found to "work" in one or more past related

contexts and using them as possible explanations/solutions/hypotheses in thepresent context (cf., Biela, 1993; Bruner, 1962; Dreistadt, 1968; Finke, Ward &

Smith, 1992; Gentner, 1989; Hestenes, 1992; Hoffman, 1980; Hofstadter, 1981;

Holland, Holyoak, Nisbett & Thagard, 1986; Johnson, 1987; Koestler, 1964;

Wong, 1993). Presumably the wind explanation was based on one or more

previous experiences in which the wind had blown out flames of one sort or

another including the barbecue's flames. Presumably the empty-tank explanation

was similarly generated. In other words, a similar experience was recalled (e.g., a

car's gas empty tank led to a failure of its engine to start) and used this as the

source of the empty-tank explanation used in the present context.Supposing that the Explanation Under Consideration is Correct and Generating

a Prediction - This supposition is necessary so that the tentative explanation can

be tested and perhaps be found incorrect. A test requires imagining relevant

condition(s) that along with the explanation allows the generation ofan

expected/predicted result (i.e., a prediction). This aspect of the learning process

is reminiscent ofAnderson's If/and/then production systems (e.g., Anderson,

1983). Importantly, the generation of a prediction (sometimes referred to as

deduction) is by no means always automatic. People often generate explanations

that they fail to test either because they do not want to or because they cannotderive/deduce a testable prediction.

Conducting the Imagined Test - The imagined test must be conducted so that its

expected/predicted result can be compared with the observed result of the actual

test.

Comparing Expected and ObservedResults - This comparison allows one to

draw a conclusion. A good match means that the tested explanation is supported,

but not proven. While a poor match means that something is wrong with the

explanation, the test, or with both. In the case ofa good match, the explanation

has not been "proven" correct with certainty because one or more un-stated andperhaps un-imagined alternative explanations may give rise to the same

prediction under this test condition (e.g., Hempel, 1966; Salmon, 1995).

Similarly, a poor match cannot "disprove" or falsify an explanation in any

ultimate sense. A poor match cannot be said to falsify with certainty because the

failure to achieve a good match may be the fault of the test condition(s) rather

than the fault of the explanation (e.g., Hempel, 1966; Salmon, 1995).

Recycling the Procedure - The procedure must be recycled until an explanation

is generated, which when tested, is supported on one or more occasions. In the

present example, the initial conclusion was that the test ofthe wind hypothesis

was faulty. Yet on repeated attempts and a closer inspection of the test, the wind

hypothesis was rejected, which allowed the generation, test, and support of the

empty-tank hypothesis.

4.

5.

6.

7.


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In this case at least, learning required feedback from the external world (albeit

filtered through sense receptors). Thus, the fact that the barbecue would not relight, in

spite of repeated attempts, was the key sensory evidence that eventually led to rejection

of the wind hypothesis. And only after the wind hypothesis was rejected, was thealternative empty-tank hypothesis generated and tested.

6. TWO TYPES OF KNOWLEDGE

Cognitive science distinguishes two types of knowledge that can be constructed,

declarative and procedural, also referred to as figurative and operative (e.g., Piaget,

1970). The distinction is essentially between knowing that (e.g., I know that London is

the capital of the United Kingdom, and animals inhale oxygen and expel carbon

dioxide) and knowing how (e.g., I know how to ride a bicycle, to count, to conduct a

controlled experiment). According to Anderson (1980): "Declarative knowledge

comprises the facts that we know; procedural knowledge comprises the skills we know

how to perform" (p. 222). Declarative knowledge is explicit in the sense that we

generally know that we have it and when it was acquired. The word "learning" is often

used in conjunction with the acquisition/construction of declarative knowledge (e.g., I

just learned that Joe and Diane got married last Thursday) and its conscious

recollection depends on the functional integrity of the medial temporal lobe (Squire &

Zola-Morgan, 1991). On the other hand, procedural knowledge, which is expressedthrough performance, is often implicit in the sense that we may not be conscious that

we have it or precisely when it was acquired. The word "development" is often used in

conjunction with the acquisition/construction of procedural knowledge (e.g., Ralph has

developed considerable golfing skill during the past few years; some students are better

at solving math problems than others). Importantly, storage and recollection of

procedural knowledge is independent of the medial temporal lobe, thus depends on

other brain systems such as the neostriatum (Squire & Zola-Morgan, 1991).

As we have seen, the acquisition/construction of declarative knowledge (e.g., the

cause of the unlit barbecue is a lack of gas) depends in part on one's ability to generateand test ideas and reject those that lead to contradicted predictions. Thus, as one gains

skill in generating and testing ideas, declarative knowledge acquisition/construction

becomes easier. This view is consistent with Piaget's when he claimed that "learning is

subordinated to development" (Piaget, 1964, p. 184), a view supported by numerous

studies that have found that, following instruction, students who lack reasoning skill do

more poorly on measures of conceptual understanding than their more skilled peers

(e.g., Cavallo, 1996; Lawson et al., 2000; Shayer & Adey, 1993). But all of this is

getting us somewhat ahead of the story. Let's first discuss Piaget's brand of

constructivism in some detail before we consider what might be taking place inside the

brain in neurological terms.


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7. PIAGET'S CONSTRUCTIVISM

Bringuier : In fact, there's a single word for the whole of your work - a word I once

heard you use; it's "constructivism."

Piaget : Yes, that's exactly right. Knowledge is neither a copy of the object nor taking

consciousness of a priori forms predetermined in the subject; it's a perpetual

construction made by exchanges between the organism and the environment, from the

biological point of view, and between thought and its object, from the cognitive point of

view. (Bringuier, 1980, p. 110)

Because Piaget was one ofthe first and foremost investigators attempting to answer

epistemological questions by scientific means, his brand of constructivism with its self-

regulation theory deserves special consideration. Piaget began his professional studiesas a biologist. So, not surprisingly, his psychological views were inspired by biological

theories, particularly those ofembryology, development, and evolution. In fact, Piaget's

thinking was firmly grounded in the assumption that intelligence is itself a biological

adaptation. Thus, he believed that the same principles apply to biological evolution and

to intellectual development. As Piaget put it: "Intelligence is an adaptation to the

external environment just like every other biological adaptation" (Bringuier 1980, p.

114). In other words, Piaget's basic assumption is that intellectual development can be

understood in the same, or analogous, terms as the evolutionary acquisition of a hard

protective shell, strong leg muscles, or keen vision.In Piaget's view there are at least two biological theories that should be considered

to explain the evolutionary development, hence, by analogy, there at least two

psychological theories that should be considered to explain intellectual development.

The biological theories are neo-Darwinism and genetic assimilation. Piaget (1952)

referred to the respective psychological theories as pragmatism and self-regulation

(sometimes equilibration).

Neo-Darwinism (neo because Darwin knew nothing ofthe mechanics ofgenetics or

mutations at the time he wrote Origin of Species) proposes that evolution occurs

through the natural selection of already-existing genetic variations initially produced by

spontaneous mutations. In other words, mutations in the genome cause changes in

observable characteristics that are then selectively evaluated by the environment

(Figure 4). Pragmatism, the psychological analogue to neo-Darwinism, claims that

random, non-directional changes in mental structures occur. A new mental structure

then drives a new behavior. The new behavior is either successful and retained or

unsuccessful and relinquished. Thus, new mental structures are internal in origin but the

environment plays an active role by selecting only the appropriate structures for

retention.


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7.1 How Do Limnaea Snails Adapt to Changing Environments?

The validity of neo-Darwinism as an explanation for organic evolution is

undisputed among modern biologists, yet many readily acknowledge that natural

selection is by no means the final word. There are a number of instances of biological


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adaptation that cannot be explained solely terms of natural selection. Piaget himself

investigated the adaptation of a variety of aquatic snails to wave-pounded and calm

environments in which changes in shell shape cannot be explained solely in terms of

after-the-fact natural selection (Piaget, 1929a;1929b). We will consider these data insome detail.

Snails of the genus Limnaea are found in almost all European lakes including those

in Switzerland where Piaget made his initial observations. The snails are famous for

their variability in shell shape. Those living in calm waters are elongated while those

living on wave-battered shorelines have a contracted, more globular, shape (Figure 5).

Piaget found that offspring of the elongated form, when reared in laboratory

conditions simulating the wave-battered shoreline, developed the contracted form. The

contracted form is due to a contraction of the columellar muscle that holds the snails

more firmly to the bottom whenever a wave threatens to dislodge them. As a

consequence of muscle contraction, the shell develops the contracted form as it grows.

Thus, in the lab the contracted shell form is a phenotypic change. However, when theeggs of the contracted form were taken to the laboratory and reared in calm conditions,

the offspring retained the contacted phenotype through several generations. This means

that the phenotypic change has become genetically fixed. Therefore, we have an


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excellent example of a characteristic that can be acquired in the course of a lifetime that

has become genetically fixed.

Can this phenomenon be explained by neo-Darwinian theory? Piaget argues that it

cannot because in the past when the elongated forms moved into wave-batteredenvironments there would have been no need for natural selection of the contracted

form to make it a genotypic trait (Piaget, 1952; 1975; 1978). In fact, natural selection

for snails having the contracted genotype would presumably be impossible because

there would have been nothing to select. In wave-battered environments, all of the

snails with either genotype would be contracted! How then could the contracted

phenotype have become incorporated into the genome?

7.2 Waddington's Theory of Genetic Assimilation

The generally accepted answer to this question among evolutionary biologists

draws heavily on the work of C. H. Waddington and his theory of genetic assimilation

(Waddington, 1966). Although Waddington's theory allows for the assimilation of genes

insuring the inheritance of initially acquired characteristics, it does so through natural

selection, but not of the relatively simple sort envisioned by Darwin. In this sense,

genetic assimilation represents a differentiation of neo-Darwinism rather than a

contradiction to it. Genetic assimilation involves the natural selection of individualswith a tendency to develop certain beneficial characteristics. As such, genetic

assimilation is a widely accepted theory of gene modification that appears as matter of

course in modern textbooks of evolutionary biology. To understand genetic

assimilation, we first need to consider embryological development and Waddington's

concept of canalization.

Canalization. The fertilized egg is a single cell. As egg cell divides, the resulting

cells differentiate into a myriad of cell types such as skin, brain, and muscle cells. The

developing embryo has a remarkable ability to buffer itself against environmental

disturbances to insure that "correct" cell types are produced. This is evidenced evenbefore the first cell divides. For example, the egg cell contains definite regions of

cytoplasm. When an egg cell is centrifuged, the cytoplasmic regions are displaced. But

if the egg is then left alone, the regions gradually move back to their original locations.

This self-righting (self-regulating) tendency is also found in eggs cut in half. Identical

human twins are produced by one egg cell that divides such that each twin arises from

what one might expect to produce only half of an individual.

The term Waddington gave to the developing organism's ability to withstand

perturbations to the normal course of development was canalization. As Waddington

(1966) described it:

The region of an early egg that develops into a brain or a limb or any other organ

follows some particular pathway of change. What we have found now is that these

pathways are 'canalized,' in the sense that the developing system has a built in tendency

to stick to the path, and is quite difficult to divert from it by any influence, whether an


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external one like an abnormal temperature or an internal one like the presence of a few

abnormal genes. Even if the developing system is forcibly made abnormal - for

instance, by cutting part of it away - it still tends to return to the canalized pathway and

finish up as a normal adult. (p. 48)

Waddington pointed out that canalization is not complete. The developing system

will not always end up as a properly formed adult. Yet the important point is that it has

the tendency toward self-regulation, toward a final end product, even in the face of

considerable variance in the paths taken. Waddington likened canalization to a ball

rolling downhill with several radiating canals (Figure 6). As the ball rolls, internal

(genetic) or external (environmental) factors can deflect it into one or another canal

with the ball ending up at the bottom of only one canal. Waddington called the system

of radiating canals the epigenetic landscape. To describe the development of an entire

organism, a large number of epigenetic landscapes would be required - one for eachcharacteristic.

Suppose, for example, an epigenetic landscape were constructed to represent the

development of an individual's sex. The landscape would contain two canals, thus

would dictate one of two end points - male or female. Genetic factors operate to deflect

the ball into one canal. Thus, the normal adult ends up male or female (but notsomewhere in between) despite intrusions at intermediate points that cause the ball to

roll part way up the side of one canal. The environment might also cause the ball to be

deflected into the other canal. Presumably this occurs in the marine worm Bonellia

where the environment determines the individual's sex, but canalization usually insures


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a male or female - not an intersex. Figure 7 shows the female and male Bonellia worms.

The larvae are free-swimming. If a larva settles down alone, it develops into a female.

If, however, it lands on the proboscis of a female, it develops into a dwarf male.

According to Waddington, organisms vary in their ability to respond to

environmental pressures due to differences in their epigenetic landscapes (e.g., the

degree of canalization, the heights of thresholds, the number of alternative canals).

Some individuals have well-canalized landscapes with few alternatives, hence are

relatively unresponsive to environmental pressures. Compare the two epigenetic

landscapes shown for the two first-generation individuals in Figures 8(A) and 8(B).

Both have well-canalized landscapes with two alternatives, yet the threshold in early

development of landscape H is higher than that in landscape L. Hence, an


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CHAPTER 118

environmental pressure, depicted by the non-shaded arrow, will most likely fail to force

the ball over the high threshold in H to produce the developmental modification (WA).

On the other hand, in landscape L with its lower threshold, the same environmental

pressure is more likely to push the ball over the threshold into another canal, thusproduce the modification.


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Because of such differences, individuals vary in their ability to respond to

environmental pressures. Some may acquire beneficial modifications, while others may

acquire non-beneficial modifications, and still others may not change. Of course,

individuals that acquire beneficial modifications have a better chance of survival and

will be more likely to leave offspring. On the other hand, the poor responders are likely

to die out. Hence landscape L with its ability to respond in a beneficial way is selected.

As shown, the population becomes one in which all members have landscape L. At this

point only the slightest genetic mutation (shaded arrow) will now push the ball over the

threshold into the new canal. Once this happens the organism will develop the well-

adapted phenotype WA with or without the environmental pressure. In a sense, the

selection for landscape L has put the developmental machine on hair trigger. Thus,

several gene mutations, which appear random in terms of molecular structure, are likely

to produce the well-adapted phenotype. Therefore, such mutations are not random in

their adaptive effect. Instead, they produce positive modifications in the genome. The

end result is that beneficial characteristics initially acquired in response to specific

environmental pressures become assimilated into the genome.

Although Waddington (1975) has stated that Piaget's studies of Limnaea represent

one of the most thorough and interesting examples of genetic assimilation in naturally

occurring populations, the biological literature is replete with additional natural and

experimental examples (e.g., Clausen, Keck, & Hiesey, 1948; Waddington, 1959;

Rendel, 1967; Futuyma, 1979).

8. PSYCHOLOGICAL SELF-REGULATION2

Figure 9 explicates psychological self-regulation as a process analogous to genetic

assimilation. The analogue of the changing genotype during evolution is one's

developing mental structures. The epigenetic landscape (itself shaped by the genes)

corresponds to one's predisposition to acquire new behaviors determined by what

Piaget (1971a, p. 22) has called "assimilation schemata." The phenotype corresponds to

2 The following discussion of psychological self-regulation differs in subtle ways from Piaget's conception. Piaget's conception of

self-regulation is based upon his theory of biological phenocopy (see Piaget 1975, pp. 216-217; Piaget 1978. pp. 78-83; and

Bringuier 1980, p. 113). As far as I am aware, phenocopy theory has not received favor among biologists. Therefore, the

present discussion will be confined to self-regulation's relationship to genetic assimilation.


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20 CHAPTER 1

overt behaviors. Thus, Figure 9(A) represents a situation in which the individual with

assimilation schemata H is unresponsive to pressures imposed by experience and does

not develop a new mental structure (WA). Interaction with the environment does not

produce "disequilibrium" or subsequent mental accommodation. The individual is not"developmentally ready" because the assimilation schemata available are inadequate to

assimilate the new experience. Presumably the available assimilation schemata are built

up by the interplay between the individual's powers of coordination and the data of

experience.


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Figure 9(B) on the other hand, represents an individual with assimilation schemataL able to respond to environmental pressures and acquire a new behavior. However, the

newly acquired behavior has not yet been assimilated into a mental structure (i.e., the

mental structure remains PA). The new behavior and the person's previous ways of

thinking have not yet been integrated. The result is mental disequilibrium. With

removal of environmental pressure, the individual is apt to revert to previous

inappropriate behaviors just as the offspring of genetically elongated but phenotypically

contracted snails will develop into the elongated form if reared in a calm environment.

In the classroom students may be able to correctly solve a proportions problem if

the teacher is there to suggest the procedure or if the problem is similar enough to ones

previously solved. But if left on their own, use of the proportions strategy may never

occur to the students because they have failed to comprehend why it was successful in

the first place (i.e., it has never been integrated with previous thinking). Thus, Figure

9(B) represents a state of disequilibrium because a mismatch exists between the poorly

adapted mental structure and the only occasionally successful behavior.

Finally Figure 9(C) represents the restoration of equilibrium through a spontaneous,

internal, yet directional, reorganization of a mental structure allowing the complete

assimilation of the new behavior pattern into an accommodated mental structure. Thus,psychological assimilation corresponds to the entire process of the incorporation of new

well-adapted behavior patterns (phenotypes) into one's mental structure (the genome)

by way of a spontaneous accommodation of mental structure (the mutation). Hence,

one does not have assimilation without accommodation. Piaget was fond of quoting the

child who, when asked about the number of checkers in two rows of unequal length,

responded correctly and reported, "Once you know, you know forever." Here is a child

with an accommodated mental structure who had completely assimilated the notion of

conservation of number.

9. INSTRUCTIONAL IMPLICATIONS

The instructional importance self-regulation theory can be stated simply. If one

adopts the pragmatic approach to education, then one is forced to wait until


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spontaneous and non-directional reorganizations of mental structures occur before

learning can take place. The process is internal and not amenable to environmental-

instructional shaping. The teacher is relegated to the relatively unimportant position of

simply telling a student when his ideas are right or wrong and cannot shape thedirection of the student's thinking.

But if one adopts self-regulation theory, then the teacher is not placed in a position

of sitting idly by waiting for change to occur. Rather, the teacher knowledgeable of

developmental pathways can produce the environmental pressures that place students

into positions in which they can spontaneously reorganize their thinking along the path

toward more complex and better-adapted thought processes. The teacher can be an

instigator of disequilibrium and can provide pieces of the intellectual puzzle for the

students to put together. Of course the ultimate mental reorganization will have to be

accomplished by the students but the teacher is far from passive. He or she can set theprocess on hair trigger just as the directional natural selection of Waddington sets the

genome on hair trigger.

The key point is that external knowledge (that presented by the teacher) can become

internalized if the teacher accepts the notion that self-regulation is the route to that

internalization. This means that students should 1) be prompted to engage their

previous ways of thinking about the situation to discover inadequacies, and 2) be given

ample opportunities to think through the situation to allow the appropriate mental

reorganization (accommodation), which in turn allows successful assimilation of the

new situation.

Let's consider how this might play out in the classroom. Many high school students

and even a significant fraction of college students employ an additive strategy to solve

the proportionality problem shown in Figure 10. As you can see, the problem involves

two plastic cylinders equal in height but unequal in diameter. The students note that

water from the wide cylinder at the fourth mark rises to the sixth mark when poured

into the narrow cylinder. When asked to predict how high water at the sixth mark in the

wide cylinder will rise when poured into the narrow cylinder, many students respond by

predicting mark 8, "Because it raised 2 marks last time so it will raise 2 marks again."


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How can these additive students learn to use a proportions strategy? According to

self-regulation theory, they must first discover the error of their previous thinking. In

this case this they can simply pour the water into the narrow cylinder and discover that

the water rises to mark 9 - instead of mark 8 as predicted. Even without pouring, theerror can be discovered through a thought experiment. Suppose the water is poured

from mark 6 in the narrow cylinder into the empty wide cylinder. Students using the

additive strategy will predict a rise to mark 4 (i.e., 6 - 2 = 4). Suppose water is now

poured from mark 4 in the narrow cylinder into the empty wide cylinder. Using the

additive strategy students now predict a rise to mark 2 (i.e., 4 - 2 = 2). Finally, suppose

that water is poured from mark 2 in the narrow cylinder into the empty wide cylinder.

Use of the additive strategy leads one to predict a rise to mark 0 (i.e., 2 - 2 = 0). The

water disappears! Of course additive students see the absurdity of the situation and are

forced into mental disequilibrium. A more formal explication of the students' reasoningmay look something like this:

If...the difference in waters levels is always 2 marks, (initial strategy)

and ...water at mark 2 in the narrow cylinder is poured into the wide cylinder,

then...it should rise to mark 0 (i.e., 2 - 2 = 0). In other words, the water should

disappear.

But ...water cannot disappear merely by pouring it from one cylinder to another.

Therefore...the difference in water levels must not always be 2 marks.

At this point, the students are prepared for step 2, introduction of the "correct" way

to think through the problem. Keep in mind, however, that according to the analogy, the

students themselves must undergo a mental reorganization to appreciate your

suggestions and assimilate the new strategy. This will not happen immediately. Rather,

experience suggests that this requires considerable time and a repeated experience with

the same strategy in a number of novel contexts (cf., Lawson & Lawson, 1979;

Wollman & Lawson, 1978). The fact that the use of a variety of novel contexts is

helpful (perhaps even necessary) is an argument in favor of breaking down traditionalsubject matter distinctions. For example, in a biology course one should not hesitate to

present problems that involve proportions in comparing prices at the supermarket,

altering recipes in cooking, comparing the rotations of coupled gears, balancing

weights on a balance beam, estimating the frog population size in a pond, comparing

the relative rates of diffusion of chemicals, and estimating gas mileage. If the range of

problems types were confined to traditional biology subject matter, many students

would fail to undergo the necessary mental reorganization and internalize the

proportions strategy, hence learning and transfer would be limited.

Although the previous example dealt with proportional reasoning (an aspect of logico-mathematical knowledge), self-regulation theory also deals with causal

relationships. As Piaget (1975, p. 212) points out, "Now it is essential to note that this

tendency to replace exogenous knowledge by endogenous reconstructions is not

confined to the logico-mathematical realm but is found throughout the development of


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physical causality." Minstrell (1980) provides a lovely classroom example of using the

theory to help students acquire physical understanding. Minstrell was trying to teach his

high school physics students about the forces that keep a book "at rest" on a table.

Before simply telling the students that the book remains at rest due to the presence of the equal and opposite forces of gravity (downward) and the table (upward), Minstrell

asked his students what forces they thought were acting on the book. Many of the

students believed that air pressing in from all sides kept the book from moving. Others

imagined a combination of gravity and air pressure pushing downward. A few students

also thought that wind or wind currents "probably from the side" could affect the book.

The most significant omission seemed to be the students' failure to anticipate the table's

upward force. Although some students did anticipate both downward and upward

forces, most believed that the downward force must total more than the upward force

"or the object would float away."After the crucial first step of identifying the students' initial misconceptions,

Minstrell then took the class through a carefully planned sequence of demonstrations

and discussions designed to provoke disequilibrium and initial mental reorganization,

stopping along the way to poll the students for their current views. The key

demonstrations included piling one book after another on a student's outstretched arm

and hanging a book from a spring. The student's obvious expenditure of energy to keep

the books up led some to admit the upward force. When students lifted the book

already supported by the spring, the initial response was surprise at the ease at which it

could be raised. "Oh my gosh! There is definitely a force by the spring." Although

Minstrell admits that the series of demonstrations was not convincing to all, in the end

about 90% of his students voiced the belief that there must be an upward force to keep

the book at rest. Of course, instruction did not stop there. Nevertheless, the majority of

Minstrell's students were well on the way to the appropriate mental accommodation.

In short, the teacher who takes self-regulation theory to heart becomes a poser of

questions, a provider of hints, a provider of materials,

[a.e. lawson] the neurological basis of learning

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