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Introduction to Learning Theories 1
INTRODUCTION
TO
LEARNING THEORIES
by
Denise R. Boyd, Ed.D.
University of Houston
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 2
Copyright © 2012 Denise R. Boyd, Ed.D., 6011 Bayonne, Spring, TX 77389. All rights
reserved. Published in the United States of America. This publication is protected by
copyright, and permission should be obtained from the author/publisher prior to any
prohibited reproduction, storage in a retrieval system, or transmission in any form or by any
means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission to
use material from this work, please submit a written request to Denise R. Boyd, Ed.D. at the
address given above.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 3
CONTENTS
CHAPTER 1: THE SCIENTIFIC STUDY OF LEARNING .............. 8
DEFINING AND STUDYING LEARNING ............................... 9
Defining Learning ........................................................................................ 9
The Goals of the Scientific Study of Learning ...................................................... 10
Describing Learning .................................................................................. 10
Explaining Learning .................................................................................. 11
Predicting Learning .................................................................................. 11
Influencing Learning ................................................................................. 12
METATHEORETICAL ISSUES......................................... 12
Epistemology ............................................................................................ 13
Learning Theory Families .............................................................................. 16
Behaviorism ........................................................................................... 16
Cognitivism............................................................................................ 16
Constructivism........................................................................................ 18
Evaluating the Usefulness of Learning Theories .................................................... 19
A PREVIEW OF LEARNING THEORIES .............................. 20
CHAPTER 2: CONDITIONING THEORIES ........................... 23
CLASSICAL CONDITIONING.......................................... 23
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 4
The Elements of Classical Conditioning ............................................................. 25
Influences on Classical Conditioning ................................................................. 27
EMOTIONAL CONDITIONING AND THE LAW OF EFFECT........ 28
John Watson and Emotional Conditioning ........................................................... 28
E. L. Thorndike and the Law of Effect ............................................................... 29
OPERANT CONDITIONING ........................................... 31
Reinforcers .............................................................................................. 31
Positive and Negative Reinforcers ................................................................. 31
Primary and Secondary Reinforcers ............................................................... 32
The Process of Reinforcement ........................................................................ 33
Operant Conditioning Outcomes ...................................................................... 35
Schedules of Reinforcement .......................................................................... 36
Punishment .............................................................................................. 37
Escape and Avoidance Learning ...................................................................... 39
EVALUATION OF CONDITIONING THEORIES ...................... 40
Systematic Desensitization ............................................................................ 41
Behavior Modification .................................................................................. 42
CHAPTER 3: INFORMATION PROCESSING THEORY .............. 46
THE ATKINSON-SHIFFRIN MODEL OF MEMORY .................. 47
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 5
Sensory Memory ......................................................................................... 48
Short-Term and Working Memory..................................................................... 48
Long-Term Memory ..................................................................................... 51
Explicit and Implicit Memory ....................................................................... 52
Episodic and Semantic Memory .................................................................... 52
REMEMBERING ........................................................ 53
Remembering as Retrieval............................................................................. 54
Remembering as Reconstruction ..................................................................... 54
Schemas ............................................................................................... 55
Networks .............................................................................................. 56
FORGETTING.......................................................... 57
Context Effects ......................................................................................... 58
The Curve of Forgetting ............................................................................... 59
Other Types of Forgetting ............................................................................. 60
EVALUATION OF INFORMATION PROCESSING THEORY ........ 62
CHAPTER 4: COGNITIVE DEVELOPMENT ......................... 65
THE DEVELOPING BRAIN ............................................ 66
PIAGET’S THEORY OF COGNITIVE DEVELOPMENT .............. 68
Piaget’s Great Discovery: Four Stages of Cognitive Development ............................... 68
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 6
The Sensorimotor Stage ............................................................................. 69
The Preoperational Stage ........................................................................... 69
The Concrete Operational Stage ................................................................... 70
The Formal Operational Stage ..................................................................... 72
Piaget’s Explanation of Cognitive Development ................................................... 74
Schemes. .............................................................................................. 74
Stages as Schemes ................................................................................... 75
Influences on Progression through the Stages ................................................... 76
THE INFORMATION-PROCESSING APPROACH TO COGNITIVE
DEVELOPMENT ....................................................... 78
Information Processing Efficiency .................................................................... 78
Processing Speed ..................................................................................... 78
Short-term Memory Capacity ....................................................................... 79
Working Memory Efficiency ......................................................................... 80
Automaticity .......................................................................................... 81
Expertise. ............................................................................................. 81
Metacognition ........................................................................................... 82
Selective Attention. ................................................................................. 82
Cognitive Monitoring................................................................................. 83
Metamemory. ......................................................................................... 83
Neo-Piagetian Theories of Cognitive Development ................................................ 84
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 7
VYGOTSKY’S SOCIOCULTURAL THEORY.......................... 85
EVALUATION OF COGNITIVE DEVELOPMENTAL THEORY ...... 86
CHAPTER 5: SOCIAL COGNITIVE THEORY ........................ 91
LEARNING THROUGH IMITATION .................................. 92
Early Theories of Imitative Learning ................................................................. 92
Bandura’s Approach to Learning through Imitation ................................................ 93
LEARNING THROUGH MODELING .................................. 95
What and How We Learn from Models ............................................................... 95
Influences on Modeling................................................................................. 96
EVALUATION OF SOCIAL COGNITIVE THEORY ................... 97
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 8
1
THE SCIENTIFIC STUDY OF
LEARNING
DEFINING AND STUDYING LEARNING
METATHEORETICAL ISSUES
A PREVIEW OF LEARNING THEORIES
SUMMARY
KEY TERMS, CONCEPTS, AND PEOPLE
You probably use the word learning fairly often, but have you ever taken the time to develop
a precise definition of it? If you did so, you would probably find that your general beliefs
about human nature play a large role in your definition. In this regard, you would have a great
deal in common with the approach that philosophers take to defining learning. That is, you
would use your general beliefs as premises from which to derive propositions about the nature
of learning. Educators typically approach the definition of learning in a similar manner, but
the beliefs they work from come from exposure to theories of instruction, their own teaching
experiences, as well as their general beliefs about human nature. In contrast to both
philosophers and educators, psychologists view learning as a natural, law-governed process
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 9
that researchers can describe, explain, test, and apply using the steps of the scientific
method. Nevertheless, the scientific study of learning has its roots in the philosophical
approach and, as you will see, cannot be separated from it. Whether we are interested in the
philosophy or the science of learning, a working definition of it must be our starting point.
DEFINING AND STUDYING LEARNING
Defining Learning
Simply put, learning is a relatively permanent change in behavior or knowledge that is caused
by experience. However, it is important to differentiate between learning and other kinds of
change. The distinguishing characteristic of learning is that it results exclusively from
experience. Think about a child learning to recite the alphabet. If she had never heard a
teacher, parent, or television character reciting the alphabet, she would never have learned
to do so herself.
In contrast to learning, maturation results from an inborn genetic plan. For example,
we often say that a baby "learns" to walk, but this statement violates the definition of
learning because it does not result exclusively from experience. All healthy human infants
living in reasonably supportive environments walk sooner or later, because humans are
genetically programmed to walk. Therefore, an infant’s change in status from non-walker to
walker is due to maturation rather than learning.
Development involves changes that arise from interactions between learning and
maturation. Consider the case of language. Some aspects of language development are built
in to the brain and depend on its maturation, but it also depends on the presence of language
in the environment. A child who grows up in a language-less environment will not develop
language. Likewise, a child who has an organic condition that interferes with brain
development may never develop language no matter how much of it is present in his
environment.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 10
The Goals of the Scientific Study of Learning
As you read earlier, psychologists apply the scientific method to the study of learning. Its
steps include observation, theory development, and hypothesis testing. In addition,
psychologists seek to apply scientific findings to problems outside the tightly controlled world
of the laboratory. Thus, psychologists who study learning seek to
Describe learning outcomes as accurately as possible (observation)
Explain why learning outcomes occur (theory development)
Predict learning outcomes and test their predictions (hypothesis testing)
Influence learning outcomes (application)
Educators are usually more concerned with the fourth goal, influencing learning
outcomes, than the other three. As a result, they sometimes studies of learning to be of no
value if they do not immediately lead to solutions to instructional problems. However,
effective instruction begins with reliable and valid ideas about how learning occurs, just as
the development of a vaccine begins with an understanding of the virus it is designed to
protect against. Thus, describing, explaining, and predicting learning outcomes are
indispensable to the development of instructional strategies that can influence learning.
Describing Learning: Some types of learning are easily observed and, as a result, can be
described in fairly straightforward ways. For instance, think about a child who is learning to
identify the letters of the alphabet. We can describe her proficiency in a number of ways
based on direct observation of her behavior. We could say that she can name 13 letters, 50%
of the alphabet, or list the specific letters she can name consistently.
However, there are some types of learning that are difficult or impossible to observe
directly. These types of learning are known as constructs, psychological variables that cannot
be directly observed but can be inferred from behavior. To describe a construct, a researcher
must develop an operational definition, an observable behavior that is a reasonable
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 11
representation if the construct. For example, student performance on standardized tests
often serves as an operational definition of the construct of reading achievement.
To be useful to learning theorists and researchers, operational definitions must have
reliability, that is, they must yield consistent scores. They must also have validity. In other
words, an operational definition must measure what it purports to measure. For example, a
reading test must be distinguishable from a general intelligence test to be valid.
Explaining Learning: Explaining learning requires the development of learning theories,
statements that account for relationships among observations of learning behavior. It is
important to note that learning theories, like other scientific theories, are not simply opinions
that one can accept or reject on the basis of consistency with one’s personal beliefs,
experiences with teaching and learning, or intuition about human nature. Instead, they are
statements that explain specific empirical observations. As such, they must be tested
empirically and determined to “have support” or “lack support” rather than judged to be
“true” or “false.” This is the essential difference between a philosophy of learning and a
learning theory. To be viable, a philosophy’s conclusions must flow logically from its
premises. For a theory to remain viable, the hypotheses derived from it must survive the
scrutiny of empirical testing.
Predicting Learning: Theories yield hypotheses, testable predictions of how changes in one
or more variables affect learning outcomes. Thus, the purpose of the prediction phase of the
scientific study of learning is to subject theory-based hypotheses to empirical testing so that
the theory can be accepted, rejected, or modified accordingly. For example, you will learn in
Chapter 2 that Ivan Pavlov’s research assistants observed hungry dogs in Pavlov’s laboratory
salivating when the assistants teased the dogs with food. Building on that observation, Pavlov
developed a theory of conditioning, a type of learning in which behaviors are thought of as
responses to stimuli. His theory generated a number of testable hypotheses, most of which
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 12
were confirmed by his research. These successful studies, in turn, led to the establishment of
principles of conditioning. Principles are predictions that have been supported by research so
many times that they are regarded as reliable nearly 100% of the time. Therefore, we can say
with confidence that Pavlov’s conditioning theory has empirical support due to its having
yielded testable hypotheses the examination of which has led to the establishment of reliable
principles of learning. We may not like conditioning theory. We may not think it explains
anything important about learning, but we must concede that it is supported by empirical
evidence and has led to the development of principles of learning. As you can see, it would be
inappropriate to say “I agree with Pavlov” or “I disagree with Pavlov” on the question of
empirical support for his theory.
Influencing Learning: Once reliable principles of learning have been established, we can use
them to develop theories of instruction. For example, classroom management theories that
emphasize the notion that the degree to which students feel emotionally comfortable in a
classroom depends on the nature of the stimuli that are present in the environment are
largely based on principles of classical condition. Such theories assume that cues such as
furniture that is more common to homes than to classrooms (e.g., a rocking chair, scatter
rugs, throw pillows) can help students feel welcome and metaphorically “at home.”
METATHEORETICAL ISSUES
Metatheoretical issues involve questions that apply to all learning theories. For example, as
you learned earlier, learning theories, like all psychological theories, have their roots in
philosophy. Thus, comparing and contrasting the philosophical bases of learning theories
addresses a metatheoretical issue. Grouping theories according to shared characteristics and
comparing their relative usefulness to the scientific study of learning address two other
important metatheoretical issues.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 13
Epistemology
Every learning theory makes assumptions about epistemology, the branch of philosophy that
addresses the question "Where does knowledge come from?" For this reason, psychologists
often compare learning theories in terms of their underlying epistemologies. Such
comparisons reveal why some theories focus on factors that are external to the learner, such
as instruction, and other focus on internal psychological variables, such as memory function.
Theories that are based on empiricist epistemologies assume that the causes of
learning are external to the learner. For example, the statement “Instruction causes
learning” is consistent with empiricist epistemology. Learning theories that are derived from
rationalist epistemologies assume that the causes of learning are internal to the learner. A
statement such as “Default learning structures that are genetically wired into the human
brain determine learning” reflects an underlying epistemology that is rationalist in nature.
Theories with an organismic epistemological foundation assume that interactions among
factors that are both external and internal to the learner cause learning. Thus, the organismic
approach gives rise to propositions that are more complex than those that are based on
empiricist or rationalist epistemology. For instance, an organismicist would probably agree
that “The combined effects of instruction, brain maturation, prior learning, stage of cognitive
development, and learners’ transitory emotional states cause learning.”
Epistemologies can be thought of as ways of answering three important questions
about learning:
Are learners active or passive in the learning process?
Which is more important to learning, nature or nurture?
Is learning quantitative are qualitative?
An epistemology’s answer to the first question determines its location on the
activity/passivity dimension. That is, some argue that learners are active and at least
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 14
somewhat in control of the learning process, while others depict learners as passive and at
the mercy of the environment. Epistemologies vary on the nature/nurture dimension in
accordance with their answers to the second question. This dimension focuses on the
interplay between factors that are biological, which include inherited individual and speciesspecific differences, and those that are environmental, such as instruction. Nature
collectively refers to biological factors, while nurture collectively refers to environmental
ones. Answers to the third question represent varying positions on the
quantitative/qualitative dimension, an epistemology’s assumptions about the degree to
which learning is quantitative (sometimes called continuous) or qualitative (sometimes called
qualitative). Epistemologies that take the quantitative approach assume that each learning
experience simply adds to those that can before. As a result, such theories characterize
movement from point A (no knowledge) to point B (knowledge) as happening in a straight
line, somewhat like walking up a ramp. Epistemologies that assume learning to be qualitative
assume that some instances of learning modify, rather than add to, previously learned
behaviors and ideas. Consequently, these epistemologies characterize movement from point A
(no knowledge) to Point B (knowledge) as happening in a series of leaps or a pattern of
apparent progressions and regressions, somewhat like walking up a staircase or hiking up a
switchback trail to get to the top of a mountain.
To better understand the quantitative/qualitative distinction, consider two examples
from child development. First, although children sometimes have growth spurts, the nature of
height itself never changes; children just get more of it as they get older. Thus, changes in
height are quantitative or continuous, because height is height no matter how tall the child
is. We can accurately describe it in terms of inches or centimeters in individuals of all ages
and heights.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 15
In contrast to height, puberty is a qualitative change, because it is a change in kind.
Prior to puberty, children are incapable of reproduction. During puberty, they acquire the
capacity to reproduce. When puberty is complete, children, who are now renamed
adolescents, are equal to adults in their capacity for reproduction. We do not say that an
adolescent or adult has more reproductive capacity than a child, because the difference is
not a quantitative one as the word more implies. The difference is qualitative in that
prepubescent children cannot reproduce, and post-pubescent adolescents and adults can
reproduce. Puberty results in changes in the nature of the body itself; organs and glands gain
capacities that they lacked before it occurred.
Some epistemologies imply that learning is akin to children’s height gains from year to
year. From this perspective, people accumulate learning experiences as time goes on, and
their knowledge at any given point in time can be thought of as the sum of all of their
learning experiences up until then. These epistemologies lie at the quantitative end of the
continuum. By contrast, other epistemologies are based on the idea that some learning events
are more like puberty than height gains. That is, some learning events actually change the
ways in which individuals learn and in how they think about what they already know. These
epistemologies lie at the qualitative end of the continuum. Moreover, stages are the hallmark
of theories that are associated with epistemologies that emphasize the qualitative aspects of
learning.
Another example, one drawn from language development, may help to clarify the
distinction between quantitative and qualitative change. Think about the various ways in
which children’s vocabularies change as they get older. One such change is that children learn
new words as they get older. This is a quantitative change. That is, ten-year-olds know more
words than four-year-olds do, and sixteen-year-olds know more words than ten-year-olds do.
But children’s understanding of the interconnections among word meanings also changes as
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 16
they get older. For instance, at some point, children observe that the words bicycle and
motorcycle have the root word cycle in common. When they realize that cycle refers to the
circular nature of wheels, they can use that knowledge to infer that there must be something
circular about the water cycle when they encounter it in science class. The construction of
meaningful links among bits of knowledge that can be used to acquire new knowledge through
inference is a qualitative change.
Learning Theory Families
Learning theories are grouped into three broad “families” according to their epistemological
assumptions.
Behaviorism. In a seminal article published in 1904, psychologist John B. Watson coined the
term behaviorism to denote an approach to studying learning that emphasizes observable
behavior. The epistemological assumptions of Watson’s behaviorism fit within the empiricist
tradition. According to the behaviorists, learning happens when passive learners are acted
upon by active agents in the environment. Consequently, theories in the behaviorist family
emphasize nurture over nature. Likewise, they view learning as comprised almost exclusively
of quantitative change. That is, a learner’s behavior at any given time is the sum of all prior
learning experiences. Similarly, behaviorists discount or deny the existence of thinking and
emotion, both of which they view as learned responses to environmental stimuli.
Cognitivism. In contrast to behaviorism, cognitivism emphasizes unobservable cognitive
processes. These processes include attention, perception, reasoning, memory, decision
making, and problem solving. For cognitivists, learning happens when the mind takes in
information and transforms it. As such, cognitivism fits within the rationalist epistemological
tradition. The information provided to the mind by the environment matters, but, for
cognitivists, it is what the mind does with the information that really counts. For instance,
early cognitivists such as Kurt Koffka, Wolfgang Kohler, and Max Wertheimer discovered a
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 17
number of important cognitive phenomena, the Gestalt principles of perception. The term
Gestalt implies that the mind tends to impose the best possible form on sensory input. Three
of these principles are illustrated in Figure 1.1. The principle of proximity causes us to
perceive the two sets of plus signs differently. We do not see them as two sets of 54 plus
signs. Instead, we view those on the left as two rows and those on the right as three columns.
We perceive the form of the plus signs, not their number. The principle of similarity causes
us to view the collection of plus signs and ampersands on the left as consisting of columns and
the ones on the right as consisting of rows. The principle of closure causes us to say that the
two figures in the third row are polygons when, in fact, they are not. Gestaltists would say
that we impose our mental template of a triangle on the lines in the left cell and that of a
rectangle on the one in the cell on the right because that is the best match our minds can
make between stored knowledge and incomplete sensory information. In effect, we mentally
close the lines in the two figures because we perceive that they should be closed rather than
open according to the standards of perceptual wholeness that Gestaltists believed are wired
in to our brains.
Figure 1.1
Gestalt Principles of Perception
Columns or Rows?
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Closure
© Denise R. Boyd, Ed.D.
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Introduction to Learning Theories 18
The Gestaltists further argued that insight, the “aha” experience that often happens
when we focus on a problem and suddenly arrive at a solution, works similarly. The senses
provide our minds with information about the elements of the problem. But it is our mind
that perceives these elements and the solution that satisfactorily unites them as a whole that
we can produce by acting on the elements in a certain way.
Clearly, the cognitivists’ learner is an active processor of information rather than a
passive recipient of environmental influence. Because both the information and the cognitive
processes applied to it contribute to learning, cognitivists tend to emphasize interactions
between nature (i.e., the brain’s hard-wired information processors) and nurture (i.e.,
information provided by the environment). They also view learning as involving both
quantitative and qualitative change.
Constructivism. Cognitivists typically argue that principles such as proximity, similarity,
closure, and insight are wired in to the brain. In philosophical language, they are a priori
constraints on information processing. In contrast, advocates for constructivism argue that
the learner’s mind builds most of the internal structures that it uses to interpret sensory
information on its own, although they acknowledge the existence of some a priori constraints.
As such, constructivism stands with cognitivism in opposition to behaviorism in that it views
the learner as an active agent in her own learning, emphasizes interactions between nature
and nurture, and sees both quantitative and qualitative change at work in the learning
process. Where the two orientations differ is in their relative emphasis on a priori constraints
and qualitative change. For constructivists, the number of experience-based, self-constructed
understandings far exceeds the number of a priori constraints. For this reason, as we noted
earlier, cognitivism fits within the rationalist epistemological tradition, while constructivism
is more consistent with the organismic approach. Moreover, constructivists place a great deal
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 19
more emphasis on qualitative than on quantitative change. Thus, theories within the
constructivist family typically describe change in terms of stages, while cognitivists do not.
Before moving on, you should be aware that the usage of the term constructivist in
theories of instruction varies considerably from its usage in learning theories. Typically,
constructivist instructional methods allow students experiment with learning materials, often
in collaboration with peers, in the hopes that such experimentation will lead them to
“construct” skills or knowledge (as opposed to objectivist models that emphasize acquisition
of information). In contrast, constructivist learning theories do not argue that learning
depends on opportunities for experimentation or on collaboration with others. It can happen
in such contexts or as a result of direct, objectivist-oriented instruction. The key distinction is
that, according to constructivist learning theory, learners construct their own knowledge no
matter what kind of instruction they are exposed to. Thus, research that supports
constructivist learning theories should not be construed as supportive of constructivist
teaching methods.
Evaluating the Usefulness of Learning Theories
In addition to comparing theories in terms of their epistemologies and grouping them into
theoretical families, we can evaluate learning theories in terms of their usefulness.
Generally, there are four questions that address a learning theory’s usefulness:
Does it have heuristic value, that is, does it generate debate and research?
Does it explain the basic facts of learning?
Does it generate testable predictions?
Does it lead to practical strategies that are relevant to real-world learning and
instruction?
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 20
You will see these four questions again after you take a brief look at the theories you will
read about in the coming chapters. For now, just keep them in mind as you read the next
section.
A PREVIEW OF LEARNING THEORIES
Table 1.1 summarizes the epistemological assumptions and theoretical families of each of the
theories you will read about in the remaining chapters of this text. The table also includes a
preview of the mechanisms posited by each theory to explain learning. For example, in
Piaget's cognitive-developmental theory, the primary mechanisms are assimilation,
accommodation, and equilibration of schemes. This means that Piaget's explanations of
learning focus on schemes (internal cognitive structures) and how they change with
experience (assimilation, accommodation, and equilibration).
Table 1.1
Overview of Learning Theories
Epistemological Questions
Active/Passive
Nature/Nurture
Quantitative/
Qualitative
Theory
Theoretical
Family
Behaviorism
Cognitivism
Constructivism
With which
theoretical
family is the
theory most
compatible?
Explanations of
Learning
Mechanisms of
Change
Is the learner
active or passive
in the learning
process?
Which is more
important to the
learning process,
nature or nurture?
Is learning
quantitative or
qualitative in
nature?
Classical
Conditioning
Passive
Nurture
Quantitative
Behaviorism
Association of stimuli
Operant
Conditioning
Passive
Nurture
Quantitative
Behaviorism
Reinforcement,
punishment
Information
Processing
Active
Both
Both
Cognitivism
Characteristics of the IP
system and to-belearned information
Active
Both
Qualitative
Constructivism
(with stages)
Assimilation,
accommodation,
equilibration of schemes
Active
Nurture
Quantitative
Behaviorism
Constructivism
(no stages)
Modeling; self-efficacy
Piaget’s
Cognitive
Developmental
Theory
Social-Cognitive
Theory
What causes learning?
As you might guess from looking at the table, the change mechanisms that each theory
proposes arise from its epistemological assumptions. For example, classical conditioning
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 21
theory views learners as passive, more influenced by nurture than nature, and emphasizes
quantitative change. As a result, the mechanisms of learning it proposes are located in the
environment.
In each of the chapters that follow, you will read an in-depth examination of each
theory’s assumptions and proposed learning mechanisms. You will also learn about the
relative usefulness of the theories this text covers. For now, the main thing you need to know
about these theories is that, for the most part, all of them get high ratings on the first
criterion listed above. They all have heuristic value in that they have led to a great deal of
debate and research. That is why these theories form the core of advanced learning theory
courses. It is also why they continue to exert a great deal of influence on the scientific study
of learning and on theories of instruction. Thus, in the chapters that follow, you will read
about how well each theory addresses the three remaining criteria of usefulness—
comprehensiveness, testability, and applicability to real-world learning and instruction.
SUMMARY
In this chapter, you have learned how psychologists define learning and the goals that guide
research that examines the learning process. You have also become familiar with the criteria
that psychologists use to compare and evaluate theories. These include the metatheoretical
issues of epistemology, theoretical family classification, and usefulness. In future chapters we
will return to these issues in the context of in-depth examinations of five major learning
theories: classical conditioning, operant conditioning, information processing, cognitivedevelopmental theory, and social-cognitive theory.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 22
KEY TERMS, CONCEPTS, AND PEOPLE
learning
maturation
development
construct
operational definition
reliability
validity
theories
hypotheses
Ivan Pavlov
principles
metatheoretical issues
epistemology
empiricist
rationalist
organismic
activity/passivity dimension
nature/nurture dimension
quantitative/qualitative dimension
John B. Watson
behaviorism
cognitivism
Kurt Koffka
Wolfgang Kohler
Max Wertheimer
Gestalt principles of perception
insight
constructivism
a priori constraints
heuristic value
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 23
2
CONDITIONING THEORIES
CLASSICAL CONDITIONING
EMOTIONAL CONDITIONING AND THE LAW OF EFFECT
OPERANT CONDITIONING
EVALUATION OF CONDITIONING THEORIES
SUMMARY
KEY TERMS, CONCEPTS, AND PEOPLE
You should recall from Chapter 1 that epistemological assumptions of the conditioning
theories flow from these theorists’ belief that learners are passive and that learning is caused
by the action of the environment on the learner. Thus, they place far more emphasis on
nurture than on nature. Likewise, conditioning theorists dismiss the notion that there are
stages, or any kind of qualitative change, in learning. They argue that stage-like behavior is
the result of an accumulation of learning experiences.
CLASSICAL CONDITIONING
Classical conditioning is a type of learning in which an organism acquires the tendency to
exhibit a natural behavior in response to an unnatural stimulus. It happens as a result of the
pairing of natural with neutral stimuli.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 24
Pavlov’s Classic Experiment
Russian physiologist Ivan Pavlov (1849-1936) is credited with discovering the principles of
classical conditioning. In the early years of a research career that would endure until just a
few days before Pavlov died at the age of 86, his primary research interests were in the
physiology of digestion and circulation. Pavlov developed a number of research techniques
and devices that won him the esteem of his colleagues. Moreover, in 1904, he received a
Nobel Prize in physiology and medicine for his studies examining the nerves of the heart.
It was in the context of Pavlov’s studies of the digestive system that he and his
research team accidentally discovered the existence of conditioned reflexes, automatic
behaviors that an organism exhibits in response to environmental signals, in 1901. Based on
what was known about digestion at the time, scientists believed that animals would salivate
only when the salivary glands in their mouths came into direct contact with food. However,
one of Pavlov’s assistants found that the dogs that were being used in the team’s experiments
salivated whenever they were “teased,” as he called it, with food. That is, the dogs would
drool at the sight and smell of food, although not as heavily as they did when food was
actually placed in their mouths. Pavlov hypothesized that the dogs’ drooling was a
conditioned reflex and, together with his research assistants, he devised a series of
experiments to test this hypothesis.
In the best known of these experiments, researchers rang a bell before they fed the
dogs. They repeated the pairing of the bell and the food several times. Next, they rang the
bell but withheld the food to see if the ringing of the bell alone would cause the dogs to
salivate. As Pavlov’s hypothesis predicted, the dogs salivated at the sound of the bell.
Pavlov’s team repeated the study many times with a number of variations. In addition, over
the next few decades, they conducted hundreds of experiments that enabled them to identify
the elements of and influences upon the process of classical conditioning.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 25
The Elements of Classical Conditioning
Classical conditioning begins with a reflex, an involuntary behavior that an organism exhibits
when it encounters the natural stimulus to which it is biologically linked. For instance, your
pancreas reflexively excretes insulin when your blood sugar rises, because the blood sugarinsulin stimulus-reflex bond is part of your body’s genetic programming. Likewise, you move
your head out of the way when a flying object, such as a ball, approaches it. The stimulus is
the approaching object, and the reflex is moving your head.
Pavlov’s research and thousands of studies by others that followed it revealed a key
principle about the relationship between natural stimuli and reflexes. When a neutral
stimulus, one that does not have a reflex connected to it, consistently precedes a natural
stimulus, it eventually acquires the power to elicit the reflex on its own. For instance, in
Pavlov’s classic experiment, the bell was the neutral stimulus. Because it was consistently
paired with the natural stimulus for the salivation reflex (food), the bell ultimately acquired
the same signal value as the natural stimulus. That is, it became a signal that prompted the
dogs’ salivary glands to produce saliva. As a result, the dogs developed two variations on the
salivation reflex. When they salivated in response to food being placed in their mouths,
salivation was classified as a natural reflex. But when they salivated in response to the bell,
the salivation reflex was classified as a learned reflex.
Learning researchers apply specific terms to the various components of the classical
conditioning process. The natural stimulus is called the unconditioned stimulus
(unconditioned = unlearned), and the natural reflex is called the unconditioned response.
The neutral stimulus, after it acquires the ability to elicit the reflex, is called the
conditioned stimulus (conditioned = learned), and the learned reflex is called the
conditioned response. The diagram below illustrates the application of these terms to
Pavlov’s experiment.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 26
Figure 2.1
Pavlov’s Classic Study
Unconditioned Stimulus
Unconditioned Response
Neutral Stimulus
Unconditioned Stimulus
Unconditioned Response
Conditioned Response
Conditioned Stimulus
Interestingly, once a stimulus functions independently as a trigger for a reflex, it can
play the same role in the conditioning process that the natural stimulus played in the original
series of events. This process is known as higher-order conditioning, the process through
which a new neutral stimulus acquires the capacity to elicit a conditioned response as a result
of being paired with a conditioned stimulus. For instance, after Pavlov’s dogs learned to
salivate in response to a bell, he was able to produce higher-order conditioning by pairing the
bell with a variety of neutral stimuli. If he sounded a buzzer prior to ringing the bell, the
dogs’ salivation reflex eventually became a conditioned response that they exhibited
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Introduction to Learning Theories 27
whenever the buzzer was sounded. Thus, the buzzer became a new conditioned stimulus with
the same capacity to elicit the salivation reflex as the bell and as the food with which the
bell was originally paired.
Influences on Classical Conditioning
Pairing a neutral stimulus with a natural stimulus doesn’t always lead to classical
conditioning. For one thing, a neutral stimulus must precede or occur at the same time as the
natural stimulus. Presenting the neutral stimulus after the natural stimulus doesn’t work. In
addition, the delay between the neutral stimulus and the natural stimulus must be so brief
that no other neutral stimulus can occur between them.
Furthermore, classical conditioning isn’t permanent. If a conditioned stimulus occurs
many times without being followed by the natural stimulus, it will eventually lose the ability
to elicit the reflex, a phenomenon known as extinction. Oddly, too, conditioned responses
sometimes disappear unexpectedly, and the organism reverts to its natural behavior. This
process is known as spontaneous recovery. Classical conditioning may also be contextspecific. That is, an organism may exhibit a conditioned response in connection with a
conditioned stimulus only in the setting in which the conditioned and natural stimulus were
originally paired.
A classically conditioned response may be exhibited in either a narrow or a broad way.
In discrimination, a conditioned response occurs only when the conditioned stimulus itself is
present. For example, a dog who learns to salivate in response to a bell that sounds the
musical tone C exhibits discrimination if it salivates only when a bell that sounds the tone C is
rung and does not respond to other tones. By contrast, in generalization, a conditioned
response occurs when the specific conditioned stimulus is present and when other stimuli that
are similar to it are present. For example, if a dog conditioned with a C-tone bell salivates
when a D-tone or B-tone bell is rung, generalization has occurred.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 28
Finally, biological predispositions, genetic tendencies to acquire or resist acquiring
classically conditioned responses to specific stimuli, influence classical conditioning.
Researchers have found that organisms rapidly acquire classically conditioned responses that
involve stimuli that are real threats to survival. For example, chimpanzees rapidly acquire
classically conditioned fear responses to any neutral stimulus that precedes the introduction
of a snake into the environment (Mineka & Oehlberg, 2008; World).
EMOTIONAL CONDITIONING AND THE LAW OF EFFECT
Two early conditioning theorists, John Watson and E. L. Thorndike, put forward ideas and
empirical evidence that advanced psychologists’ understanding of human and animal learning.
Both amplified and extended Pavlov’s findings to construct more comprehensive descriptions
of learning. Thus, Watson’s work on emotional conditioning and Thorndike’s studies of trialand-error learning served as the foundation of modern learning theories.
John Watson and Emotional Conditioning
As you learned in Chapter 1, John Watson (1878-1958) coined the term behaviorism, the
perspective that explains learning exclusively in terms of the influences of environmental
agents on organisms. Moreover, he argued that virtually all variations in human outcomes are
attributable to classical conditioning. He focused particularly on emotional outcomes such as
learned fears. In search of evidence for his arguments, Watson carried out one of the best
known and most controversial experiments in the history of psychology.
Watson and his assistant, Rosalie Raynor, placed an 11-month-old infant named Albert
in a room with a white rat. The child curiously observed the rat and attempted to play with
it. Importantly, he exhibited no fear whatsoever. As you probably know, infants are naturally
afraid of loud noises. Typically they startle and cry fearfully when they are exposed to sudden
loud noises such as gunfire. Watson and Raynor wanted to show that they could use Albert’s
natural fear of loud noises to condition him to fear the white rat. Consequently, they placed a
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Introduction to Learning Theories 29
suspended steel bar behind Albert’s back where he could not see them strike the bar with a
hammer. Following Pavlov’s procedures, they presented the rat and then banged the steel
bar. Predictably, Albert soon startled and cried whenever he saw the rat. Moreover, he
generalized the conditioned fear response to other white, furry objects. These included his
mother’s fur coat and a Santa Claus beard that Watson himself donned to test the extent of
Albert’s generalization of the conditioned fear response.
Watson argued that the “Little Albert” experiment, as his study came to be known,
and others like it proved his hypothesis that all human learning is attributable to classical
conditioning. Few psychologists today would agree, and virtually all of them would find fault
with the ethics of Watson’s experiment. However, Watson is credited with being the first
behavioral scientist to demonstrate that emotional responses are subject to classical
conditioning.
E. L. Thorndike and the Law of Effect
Like Watson, pioneering learning theorist E. L. Thorndike (1874-1949) argued that
psychologists should focus their studies on observable behavior rather than on internal mental
states. However, Thorndike deviated from behaviorist orthodoxy in his belief that hereditary
differences across individuals contributed to learning outcomes. Consequently, his research
career included two distinctive strands. First, he conducted rigorous laboratory studies of
both animal and human learning. Second, he developed standardized psychological tests to
measure human variations in traits such as personality and intelligence that enabled him to
compare similarities in these traits among twins, non-twin siblings, parents and children, and
unrelated individuals. Thus, Thorndike is often called “the father of educational psychology.”
Thorndike developed a distinctive learning theory based on studies in which he
confined cats in boxes like the one in Figure 2.2. He placed a fish outside the door of the box
and observed as the cats determined how to operate a lever, cord, or other device to open
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 30
the door to get hold of the fish. Thorndike observed the cats engaging in a process he called
trial and accidental success, rather than “trial and error,” as they attempted to figure out
how to operate the opening mechanism of each type of door he presented to them. As they
worked on opening the doors, the cats abandoned behaviors that did not produce results and
repeated those that moved them closer to solving the problem of getting the door open.
Figure 2.2
Thorndike’s Puzzle Box
Thorndike proposed that the cats’ repetition of successful actions and discontinuation
of unsuccessful ones constitute the law of exercise, the tendency of repeated behaviors to
strengthen bonds between sensory perceptions (such as seeing a cord attached to a puzzle
box door) and actions (such as pulling on the cord to see if it opens the door). Moreover, he
proposed a learning principle that he called the law of effect, the idea that the behavioral
repertoire of an organism consists of stamped-in and stamped-out responses. Stamped-in
responses are those that the organism acquires because they produce satisfying outcomes.
Stamped-out responses are those that the organism abandons because they produce annoying
outcomes.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 31
OPERANT CONDITIONING
The ideas and discoveries of Pavlov, Watson, and Thorndike were instrumental to the work of
B. F. Skinner. As you may recall, classical conditioning deals with reflexive responses, such as
the way that you move your head when you think an object is going to hit you in the face. By
contrast, Skinner’s work addressed changes in non-reflexive behaviors, which he called
operants, and the ways in which they change as a result of the consequences they produce, a
process he called operant conditioning. He referred to the relationship between an operant
and a consequence as a contingency, meaning that the consequence is contingent upon the
exhibition of the behavior. Operants typically occur randomly with no plan or purpose behind
them. However, Skinner argued that after operants are changed by the actions of
consequences upon them, operants become learned behaviors that organisms use to
manipulate their environments. In other words, organisms use operantly conditioned
behaviors to produce desirable consequences and avoid undesirable ones. For this reason,
operant conditioning is often characterized as instrumental learning, meaning that the
behaviors acquired through operant conditioning become purposeful once they are learned.
Reinforcers
The effects of consequences on operants, not their characteristics, determine how we classify
them. For instance, you often hear people refer to pleasant consequences as “reinforcers”
regardless of their influence on behavior. Technically, though, a reinforcer is any
consequence, pleasant or otherwise, that increases the frequency of the behavior it follows.
Thus, material rewards such as stickers are not reinforcers unless they increase the frequency
of the behavior on which they are contingent.
Positive and Negative Reinforcers. Skinner described two effects that operants have on the
environments in which they occur. He noted that operants can either cause a new stimulus to
appear in the environment, or they can cause an already present stimulus to disappear. He
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 32
referred to these effects as positive and negative effects on consequences. Many people
equate positive with good and negative with bad. However, in operant conditioning, the
mathematical connotations of these terms are the relevant ones. Thus, a positive reinforcer
is a consequence that involves the addition of a stimulus that increases behavior, while a
negative reinforcer is a consequence that involves the subtraction of a stimulus that
increases behavior.
Primary and Secondary Reinforcers. Before leaving the discussion of reinforcers, we should
also consider the distinction between primary or secondary reinforcers. Primary reinforcers
satisfy biological needs. Note that they can be used either as positive or negative reinforcers.
For example, Brussels sprouts satisfy the biological need for food, but many people find their
taste to be aversive. Imagine a parent saying, “Because you finished all of your green beans,
you don’t have to eat your Brussels sprouts.” If the behavior of green-bean-eating increases,
then the avoidance of Brussels sprouts has served as a negative reinforcer. By contrast,
imagine a parent saying, “Because your finished all of your green beans, I’m going to give you
some ice cream.” If the target behavior increases, the ice cream has served as a positive
reinforcer.
Secondary reinforcers are learned through association with primary reinforcers (e.g.,
money can buy food) or because they have social value. Again, these can function as either
positive or negative reinforcers. For instance, winning $100 positively reinforces the behavior
of buying lottery tickets in a winner who buys tickets more frequently afterward. The money
is a positive reinforcer because it was added and it increased behavior. By contrast, paying a
$100 late fee negatively reinforces the behavior of making mortgage payments on time in a
late-payer who pays on time more frequently afterward. The late fee is a negative reinforcer
because its removal led to an increase in behavior. By exhibiting the behavior of paying on
time, the person avoids the aversive outcome of having to pay late fees in the future.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 33
The Process of Reinforcement
As noted earlier, a reinforcer is a consequence that increases behavior. Typically, we use the
term reinforcement to refer to the entire series of events that leads to an increase in
behavior in response to a consequence. Positive reinforcement is a sequence of events in
which an added consequence, usually a pleasant one, increases the frequency of an operant.
Negative reinforcement is a sequence of events in which a subtracted consequence, typically
an aversive one, increases the frequency of an operant. To put it a bit differently, three
things happen in the process of reinforcement: (1) an operant (non-reflexive behavior)
happens; (2) a consequence is either added or subtracted (the positive or negative part,
respectively) by an agent in the environment or as a natural consequence of the operant’s
impact on the environment; and (3) the frequency of the operant increases (the
reinforcement part), decreases, or remains unchanged. Let’s consider a few examples.
Positive reinforcement: Johnny’s room is a mess. Johnny cleans his room.
Mom gives Johnny a Snickers bar. Johnny cleans his room more frequently
thereafter. (agent adds a consequence)
Negative reinforcement: Johnny’s room is a mess. Mom yells at Johnny.
Johnny cleans his room. Mom stops yelling at him. Johnny cleans his room more
frequently thereafter. (agent removes a consequence)
Positive reinforcement: Johnny’s room is a mess. Johnny cleans his room.
Johnny enjoys the feeling of accomplishment he experiences as a result.
Johnny cleans his room more frequently thereafter. (natural consequence
added)
Negative reinforcement: Johnny’s room is a mess. Johnny notices an
unpleasant order indicating the presence of spoiled food hidden somewhere in
the mess. Johnny cleans his room in order to locate the source of the odor. He
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Introduction to Learning Theories 34
finds a two-week old sandwich under his bed and throws it away. He notices
that the unpleasant odor has ceased. Johnny cleans his room more frequently
in the future. (natural consequence removed)
Let’s break down these examples in terms of the three things that happen in the
reinforcement process. First, the operant is the same in all four examples: Johnny cleans his
room. Second, consider the variations in the consequences across the four examples. In the
first example, the consequence, the Snickers bar, is added by Johnny’s mom, an agent. In the
second example, the consequence, Mom’s yelling, is removed by an agent, Mom herself. In
the third, the consequence, Johnny’s sense of accomplishment, is added but not by an agent.
It is the natural consequence of Johnny’s having worked to accomplish a goal. In the fourth,
the consequence, discontinuation of the spoiled food odor, is removed as a natural
consequence of Johnny’s having cleaned his room and found the old sandwich. Finally, notice
that the outcome is the same in all four examples: Johnny cleans his room more frequently in
the future. However, in the first and third examples, Johnny cleans his room more frequently
in order to get something pleasant. In the second and fourth, he cleans his room to avoid
something unpleasant.
You may have noticed that something else is the same across the four examples. The
state of Johnny’s room is the context in which the reinforcement process occurs in all of
them. In operant conditioning terms, Johnny’s messy room is an antecedent condition, a
feature of the environment that is present before the operant occurs. According to Skinner,
antecedent conditions trigger learned patterns of behavior based on a learner’s expectations
as to what will happen when a specific behavior is exhibited. As a result, antecedent
conditions become discriminative stimuli, signals that tell learners what kinds of
consequences are likely to ensue if they exhibit specific behaviors. In the first example,
whenever Johnny’s room is messy, he will clean it in anticipation of getting a Snickers bar or
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Introduction to Learning Theories 35
another treat. In the third, he will clean his room in anticipation of feeling good about the
fruits of his labor. But notice that the antecedent conditions in the second and fourth
examples are a bit more complex. In the second, the room is messy and Mom is yelling. As a
result, Johnny’s learned pattern of room cleaning behavior may depend on the presence of
both a messy room and a yelling mother. In the fourth example, the room is messy and there
is an odor of spoiled food present. Thus, Johnny’s learned pattern of room cleaning behavior
may depend on the presence of both a messy room and an unpleasant odor.
Operant Conditioning Outcomes
The tendency of human beings to apply previously learned operant-reinforcer connections to
new situations extends the influence of operant learning beyond the confines of a single
conditioning episode. For instance, students tend to generalize the reinforcers associated
with one assistant principal to all of them. Generalization is the process of responding to an
entire category of reinforcers rather than to a single reinforcer, or source of reinforcement,
within the category. Thanks to generalization, students don’t have to relearn how to behave
in the presence of an assistant principal every time they encounter a new one. Nevertheless,
students also learn the subtle differences in behavior across the people they know, including
assistant principals. As a result, they adapt their behavior accordingly, or discriminate, when
they are in the presence of an administrator whom they know. Discrimination is the tendency
to respond differently to individual reinforcers, or sources of reinforcement, within a
category.
The same principles of generalization and discrimination apply to students’
interactions with teachers. In addition, through generalization, some students learn to
respond to grades as a category of reinforcers. These students regard all grades as important,
put forth effort to obtain good grades and avoid bad ones, and are conscientious about graderelated details. By contrast, some students exhibit discrimination when they respond
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Introduction to Learning Theories 36
effortfully to some grades and carelessly to others based on the characteristics of the grades
themselves. For example, discriminating students respond in an effortful way only to
assignments that represent “major” grades.
Extinction happens when behaviors that are contingent on reinforcers disappear when
those reinforcers are no longer available. For example, students may read large numbers of
books when teachers offer tokens or other types of rewards. As soon as the reward scheme is
removed, however, their reading rates drop.
Because of the tendency of operantly conditioned behaviors to become extinct, some
psychologists argue that operant conditioning is not true learning. (Think back to the
definition of learning as a permanent change.) This assertion is supported by another
phenomenon known as instinctive drift, or the tendency of an organism to revert to
unlearned behavior. Still, both animals and humans sometimes show spontaneous recovery,
the sudden return of a long-absent conditioned behavior.
Schedules of Reinforcement
Contingencies between reinforces and operants are often described in terms of schedules of
reinforcement. Some schedules link behaviors directly to reinforcers, ratio schedules, while
others depend on the passage of time, interval schedules. Fixed ratio schedules and fixed
interval schedules apply reinforcers in the same way at all times. For instance, a fixed ratio
schedule might reinforce every other behavior the organisms exhibits, while a fixed interval
schedule might administer reinforcement every five minutes. Variable ratio schedules apply
reinforcers inconsistently. The goal of variable schedules is to prevent organisms from
predicting when they will receive a reinforcer.
Variations in schedules are strongly linked to variations in acquisition and extinction
rates. For instance, a continuous schedule of reinforcement, a special type of fixed ratio
schedule in which every behavior is reinforced, usually results in a rapid acquisition rate.
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Introduction to Learning Theories 37
However, it is also linked to a rapid rate of extinction. By contrast, variable ratio schedules
produce what is known as the partial reinforcement effect, the tendency of behaviors to be
learned slowly but to be extremely resistant to extinction. For example, when teachers grade
homework randomly, students typically display this effect. They do not know which
assignments will be graded, so they increase the overall amount of homework they do.
Fixed interval schedules produce the scalloping effect, or the tendency to exhibit high
rates of behavior just before reinforcers are administered. For instance, before direct
deposit, nobody was absent from work on payday. Alternatively, variable interval schedules
can induce superstitious behavior, because the organism does not know which behaviors are
linked to the reinforcers. Thus, the organism tends to repeat all of the behaviors that
occurred in the interval between reinforcers. For this reason, variable interval schedules are
useful for training complex sets of behaviors such as appropriate classroom behavior. This is
so because such schedules reinforce learners for exhibiting a number of specific behaviors
that collectively constitute a system of behavior that is appropriate to a specific situation.
Punishment
A punisher is any consequence that reduces the probability of an operant. So, it is the
opposite of a reinforcer. Remember, we don’t define consequences in terms of their
characteristics, but with regard to their effects on behavior. Thus, a pleasant or desirable
consequence can be a punishment if it causes a reduction in behavior. Like positive
reinforcement, positive punishment involves an added consequence, usually one that is
aversive, but unlike positive reinforcement, it involves a decrease in the frequency of a
behavior. Think about a situation in which a cat owner wants to teach his pet to stop clawing
the furniture. Following a friend’s suggestion, the cat owner zaps the cat with a squirt gun
every time he starts to claw. As a result, the cat stops clawing the furniture, and we can
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 38
conclude that positive punishment has occurred. The behavior, furniture clawing, decreased
because of an added stimulus, the water gun zap.
Similarly, negative punishment involves a subtracted consequence, usually one that is
pleasant, followed by a decrease in the frequency of a behavior. For instance, grounding or
taking privileges away from children is a form of negative punishment if it results in a
decrease in the behavior that caused the consequence. Likewise, “time out” is a negative
punishment because it removes the child from a setting in which reinforcers are available.
Table 2.1 summarizes the definitions of positive and negative reinforcement and positive and
negative punishment.
Table 2.1
Reinforcement and Punishment
Reinforcement
Punishment
Positive
Negative
Positive Reinforcement
Negative Reinforcement
Increased behavior
Added consequence
Increased behavior
Removed consequence
Positive Punishment
Negative Punishment
Decreased behavior
Added consequence
Decreased behavior
Removed consequence
Some people confuse positive punishment with negative reinforcement because both
usually involve aversive stimuli. Remember, punishment causes a decrease in behavior, while
reinforcement causes an increase in behavior. But here is what makes negative
reinforcement and positive punishment more complex than they might appear to be. A single
consequence can be a positive punisher and a negative reinforcer at the same time. Consider
this example. A child runs out the front door of his home, putting himself in danger. As a
result, the parent yells at him. The child then stops and returns to the safety of the house.
The parent’s yelling punished the behavior of running outside but also reinforced the
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 39
behavior of returning to the house. How? The yelling was added to the behavior of running out
(positive punishment) but it was removed when the child returned to the house (negative
reinforcement). Similarly, you read earlier that late fees negatively reinforce the behavior of
paying on time. They also positively punish the behavior of paying late. Timely payments
increase in frequency because they enable the payer to avoid late fees (negative
reinforcement). Late payments decrease in frequency because they elicit late fees (positive
punishment).
Escape and Avoidance Learning
Aversive stimuli are associated with escape learning and avoidance learning. Escape learning
involves, quite literally, escaping from an aversive stimulus that is already present. For
example, when you were a teenager, you probably learned to look remorseful whenever your
parents lectured you. Sad facial expressions, you learned, caused a lecture to be shorter than
it would be if you exhibited defensive behaviors such as talking back. Avoidance learning
involves exhibiting a behavior in order to avoid the administration of an aversive. Many
children learn to read their parents’ and teachers’ nonverbal cues, such as tone of voice, to
know when they have reached the point when some kind of aversive is about to happen (often
referred to colloquially as “pushing buttons”). When that point is reached, the child exhibits
a behavior she knows the parent or teacher wants her to, thus avoiding the aversive.
Escape and avoidance learning are adaptive in nature. However, another kind of
learning associated with aversives, learned helplessness, is not. Learned helplessness
develops when an organism has been exposed to aversives from which escape or avoidance
are impossible. The classic learned helplessness experiment involved dogs who were strapped
down and shocked. As a result, they allowed themselves to endure electric shocks even when
they weren’t strapped down. Some observers describe the passive behavior of many of
today’s students as the result of learned helpless. They have failed so many times, and
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Introduction to Learning Theories 40
believe their failure to be outside their own control, that they have given up trying. Those
who are resilient often turn to other sources of reinforcement. In some cases, these sources
of reinforcement only make matters worse (gangs, drugs, low-achieving peer groups),
although they may make the students feel better about their situation. Other students exhibit
learned helplessness by becoming depressed and withdrawn.
EVALUATION OF CONDITIONING THEORIES
Recall from Chapter 1 that learning theorists use four criteria to evaluate the usefulness of a
theory. As you read in Chapter 1, all of the theories discussed in this text have demonstrated
their heuristic value. Consequently, evaluation of each theory focuses on the remaining three
questions that are relevant to a learning theory’s usefulness:
Does it explain the basic facts of learning?
Does it generate testable predictions?
Does it lead to practical strategies that are relevant to real-world learning and
instruction?
Thus, the first question we need to ask about conditioning theories concerns the degree to
which they provide comprehensive explanations of real-world behaviors. Despite claims by
conditioning theorists to the contrary, most learning theorists believe that it is difficult, if not
impossible, to reduce every behavior to an inborn stimulus-response connection as would be
required if classical conditioning is to satisfactorily explain real-world learning. Likewise,
contrary to the predictions of operant conditioning theory, organisms often exhibit behavior
that has not been reinforced. Similarly, in the case of human learning, it is quite common for
people to display behaviors to which aversive stimuli have been repeatedly applied by
environmental agents. These shortcomings of the conditioning theories lead most learning
theorists to conclude that, while both classical and operant theory explain important aspects
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Introduction to Learning Theories 41
of learning, something more is required if we are to truly understand behavior change in the
real world.
Second, we must consider whether conditioning theories generate testable
hypotheses. The answer to this question is a clear “yes.” There is no doubt that both classical
and operant conditioning are capable of generating a wide variety of hypotheses. Moreover,
most hypotheses derive from the conditioning theories lend themselves to the experimental
method and, especially, to laboratory experimentation in which researchers can control all
relevant variables.
Finally, classical and operant conditioning theories provide us with a number of
techniques, such as systematic desensitization and behavior modification, that are useful for
changing behavior and enhancing people’s lives.
Systematic Desensitization
Systematic desensitization is a therapeutic technique that is used to reduce or eliminate
both natural and learned anxieties and fears through increasing levels of exposure to feared
stimuli (Wolpe, 1958, 1973). Such fears include workplace issues, such as zoo workers’ need
to gain control over the natural anxieties that are evoked by working with dangerous animals.
In addition, systematic desensitization helps individuals overcome life-limiting anxieties such
as fear of exposure to germs, fear of leaving home, and the fear of food that is experienced
by many individuals with eating disorders.
The first step that a therapist using systematic desensitization takes with a client is to
identify the stimuli that trigger the client’s fearful response. For instance, consider a client
who is so afraid of dogs that he won’t go outside his house. It might seem that the trigger
stimulus is dogs, and that the process of systematic desensitization would involve exposing
the client to dogs. However, in this client’s case, the trigger that is preventing him from
leaving home isn’t actually a dog. It is the thought of encountering a dog. Consequently, the
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Introduction to Learning Theories 42
first step in the therapist’s plan would be to help the client become comfortable with
thinking about encounters with dogs. Ultimately, the plan would involve exposing the client
to dogs, probably by gradually increasing amounts of exposure time and decreasing distances
from dogs, but there would be a number of intermediate steps between the thinking step and
the dog-exposure steps. The intermediate steps might include having the client watch videos
of dogs, for instance.
A complex fear and the sub-fears that contribute to it are usually collectively referred
to as a hierarchy of fears. A successful systematic desensitization plan typically identifies
and addresses all of the steps in the hierarchy. Everyday fears of this type for which
systematic desensitization can be effective include test anxiety, injection anxiety, and fear of
flying.
Behavior Modification
Behavior modification is an agent’s systematic application of the principles of operant
conditioning to the task of changing an organism’s behavior. It is especially helpful for
enabling individuals with autism, mental retardation, and psychological disorders to adapt to
their environments. Moreover, educators frequently use it to change the behavior of students
who do not have such disabilities. Businesses, too, employ the principles of operant
conditioning to address problems such as absenteeism. Health professionals also find that
behavior modification is useful in working with patients who need to stop smoking, lose
weight, exercise more, or make other health-related behavior changes. In addition, the
change agent in the behavior modification equation can be oneself, that is, individuals can
use behavior modification to change their own behavior.
Professionals who specialize in using behavior modification to alter individuals’
problematic behaviors are known as applied behavior analysts in educational settings and
behavioral therapists in clinical settings. The first step in a behavior modification plan is to
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Introduction to Learning Theories 43
identify the behavior that needs changing, or target behavior. The next step is to determine
the frequency of the target behavior through careful observation. Next, analysts or therapists
use their knowledge of schedules of reinforcement to devise a plan for using reinforcement
(and sometimes punishment) to change the behavior. The implementation phase comes next
when the plan is actually put into action. In the evaluation phase, the analyst or therapist
determines whether the plan has been successful.
When applied behavior analysts and behavior therapists need to induce a person to
learn behaviors that she is not exhibiting at all, they often use of the principles of shaping,
the process in which an agent breaks down a complex target behavior into intermediate steps
to which reinforcers are applied. For instance, suppose an applied behavior analyst is takes on
the challenge of helping a kindergartener who never sits down to remain seated and engaged
in class work. She would begin by reinforcing him for staying near his seat. Next, she would
reinforce him for actually sitting down, no matter how briefly. Next, she would gradually
extend the amount of time between the administration of the reinforcer and the time the
child first sat down. The next step would probably be to reinforce any evidence of
engagement with class work while he is seated, perhaps behaviors as simple as picking up a
pencil or looking at a book or worksheet. In this way, the analyst would help the child build a
repertoire of small behaviors that, together, constitute the complex behavior of engaging in
class work while seated.
Animal trainers also use shaping techniques. Moreover, because of their familiarity
with the principles of extinction and instinctive drift, experienced trainers know that there
really is no such thing as a “trained” wild animal that won’t eventually revert to its natural
behavior. Consequently, most behavior modification regimes for animals include a
maintenance reinforcement plan that is designed to reduce the chances of extinction and
instinctive drift. Behavior analysts and therapists who work with humans typically do likewise,
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Introduction to Learning Theories 44
especially those who work with clients who have autism, mental retardation, and/or serious
psychological disorders.
SUMMARY
In this chapter you have learned that the conditioning theories have given teachers,
therapists, and others a number of helpful techniques for changing behavior. Consequently,
psychologists view the conditioning theories as meeting the practical application criterion for
useful theories. Likewise, the conditioning theories are quite amenable to experimentation.
Thus, within the limited sphere of reflexive behavior and non-reflexive behavior that is
changed as a result of consequences, the conditioning theories do a good job of explaining
learning. However, as we will see in future chapters, we must look beyond the conditioning
theories if we want to construct a truly comprehensive account of learning.
KEY TERMS, CONCEPTS, AND PEOPLE
classical conditioning
Ivan Pavlov
conditioned reflexes
reflex
natural stimulus
neutral stimulus
unconditioned stimulus
unconditioned response
conditioned stimulus
conditioned response
higher-order conditioning
extinction (in classical conditioning)
spontaneous recovery (in classical conditioning)
discrimination (in classical conditioning)
generalization (in classical conditioning)
biological predispositions
behaviorism
John Watson
Rosalie Raynor
Little Albert
E. L. Thorndike
trial and accidental success
law of exercise
law of effect
operants
operant conditioning
instrumental learning
reinforcer
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primary reinforcer
secondary reinforce
positive reinforcer
negative reinforcer
reinforcement
positive reinforcement
negative reinforcement
antecedent condition
discriminative stimuli
schedules of reinforcement
ratio schedules
interval schedules
fixed ratio schedules
fixed interval schedules
continuous schedule
partial reinforcement effect
scalloping effect
superstitious behavior
generalization (in operant conditioning)
discrimination (in operant conditioning)
extinction (in operant conditioning)
instinctive drift
spontaneous recovery (in operant conditioning)
punisher
positive punishment
negative punishment
escape learning
avoidance learning
learned helplessness
systematic desensitization
hierarchy of fears
behavior modification
target behavior
implementation phase
shaping
maintenance reinforcement
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3
INFORMATION PROCESSING
THEORY
THE ATKINSON-SHIFFRIN MODEL OF MEMORY
REMEMBERING
FORGETTING
EVALUATION OF INFORMATION PROCESSING THEORY
SUMMARY
KEY TERMS, CONCEPTS, AND PEOPLE
In Chapter 1 you learned that information processing (IP) theory is consistent with
rationalist epistemology because it posits that an internal characteristic, the IP system,
constrains how input from the environment is interpreted and learned. For instance, the IP
system is limited in how much information it can take in at one time. Therefore, when
information exceeds that amount, some is lost. Furthermore, IP theorists assume that the
mind processes information in ways that are similar to those used by computers. Thus, these
theorists often talk about the roles of hardware (e.g., neural structures) and software (e.g.,
learning strategies) in memory and other cognitive processes. In addition, like all cognitive
theories, IP theory emphasizes the interactive effects of nature and nurture rather than
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Introduction to Learning Theories 47
attributing learning to one or the other. Moreover, it asserts that learning involves both
quantitative and qualitative change. While, in general, IP theorists do not propose that
learning occurs in stages, they recognize that there are some learning outcomes in which
change takes place in a stage-like fashion.
THE ATKINSON-SHIFFRIN MODEL OF MEMORY
Information processing theory is not a single grand theory that explains all learning.
Instead, it is a general perspective that informs micro-theories, theories that explain specific
learning outcomes. Thus, an IP micro-theory that explains the acquisition of decoding skills
might be quite different from one that explains the development of reading comprehension.
What the two micro-theories would have in common is that each would be framed in terms of
a general model of cognitive functioning that has its roots in a three-store model of human
memory proposed by Richard Atkinson and Richard Shiffrin (1968) that has informed IP
research for the past several decades (see Figure 3.1).
Figure 3.1
The Atkinson-Shiffrin Model of Memory
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 48
Sensory Memory
As you can see in Figure 3.1, incoming information enters the system through the sensory
memory, a temporary store that briefly holds visual, auditory, and other sensory information.
Sensory memory holds bits of visual information, icons, for only ¼ to ½ of a second. By
contrast, bits of auditory information, echoes, are held for 2 seconds or more. Thus, the
human IP system is somewhat biased in favor of auditory information. This lack of balance
may be due to the fact that language is such an important means of communication for
humans.
The capacity of sensory memory is quite large. However, most information is lost
unless it is selected for further processing. The information selection process is more
commonly known as attention. It is influenced by a number of variables. First, characteristics
of information such as its novelty, familiarity, intensity, and personal relevance increase the
chances that the IP system will attend to it. Second, because auditory information is stored
longer than visual information, it is more likely to be attended to. Information that is
attended to moves on from sensory memory to the next component of the IP system.
Short-Term and Working Memory
The central, organizing feature of the Atkinson-Shiffrin model of memory is the short-term
memory (STM). On average, the STM can hold 7 bits of information, with typical adults
varying in STM capacity from 5 to 9 bits. Moreover, the maximum storage time for information
in STM is about 30 seconds.
The IP system can overcome the limited capacity of STM through automaticity, the
tendency of frequently used or well-rehearsed information to become accessible to STM
without taking up processing space. We experience automaticity in everyday life when we
find ourselves thinking about something other than what we are doing when we are
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performing a routine task such as driving to work or doing household chores. Our IP systems
automatically access the information we need to perform the tasks, and, as a result, STM
space is freed up for processing other information.
Chunking, reducing the amount of information to be remembered by grouping
individual items into blocks of information, is another means of expanding STM capacity. For
example, if we attempted to remember a telephone number by memorizing ten individual
digits, we would exceed the capacity of short-term memory. Instead, we group the individual
digits in phone numbers into three chunks, XXX-XXX-XXXX. As a result, phone numbers are
processed as three bits of information rather than ten.
In addition, STM processing time can be extended indefinitely through the application
of memory strategies, cognitive techniques that enhance the memorability of information
(see Table 3.1). For instance, simply mentally repeating information stored in STM increases
the amount of time it stays in the system, and linking new information to previously acquired
knowledge increases the chances that the new information will move on to the IP system’s
permanent storage system.
Table 3.1
Memory Strategies
Strategy
Rehearsal
Definition and Everyday Memory Example
Repeating information until it is no longer needed or until it can be easily retrieved from long-term
memory
Example: Repeating the list of items you need at the grocery store until you have memorized it
Sorting information into categories for storage in long-term memory
Organization
Example: Arranging a grocery list according to categories such as meats, produce, dairy, and so on
Relating new and previously learned information in meaningful ways
Elaboration
Example: Using recipes stored in long-term memory to remember the items on a grocery list
Creating non-meaningful links between new and previously learned information
Mnemonics
Example: Using the acronym SALT to remember a grocery list that includes sausage, asparagus,
linguine, and tomatoes.
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The Atkinson-Shiffrin model implies that the effectiveness of the entire IP system
depends on the efficiency of the STM. For this reason, IP theorists often refer to the
processing demands of cognitive tasks to explain differences in individuals’ performance on
them. For instance, think back to when you took the GRE. At least some of your STM capacity
was probably devoted to coping with anxiety. At times, you may have felt that there was a
wall between your thoughts and the knowledge you needed to answer the test questions, but
you were confident that the information was stored in your long-term memory. In these
instances, the anxiety was taking up so much STM space, that there was none left over for
retrieving information from long-term memory. Likewise, algebra students who have not
automatized basic math facts and the relationships among them (i.e., 6 x 4 = 24 is
meaningfully related to 24/6 = 4) have difficulty with complex problems. This is because their
STM capacity is consumed by simple arithmetic calculations. Moreover, they cannot apply
what they know about number relationships to variables (i.e., a x b = c is meaningfully
related to c/a = b). Importantly, too, providing such students with calculators is not helpful,
because calculators increase rather than decrease processing demands. That is, all of the
behaviors, physical and cognitive, involved in operating the calculator add to the processing
demands of the task. Similarly, readers who cannot automatically decode written words into
spoken words have little or no STM capacity available to comprehend what they read. Thus,
instructing such students in the use of comprehension strategies is not likely to lead to
improvements in reading proficiency. The students may learn the strategies, but their lack of
automatic decoding skills will prevent them from applying them, again, because of the
limited space available in STM. In fact, comprehension strategies add to the processing
demands of reading tasks in much the same way that calculators compound the demands
associated with solving algebra problems.
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Some IP theorists, most notably British psychologist Allan Baddeley, argue that STM is
actually a multi-component system that is more accurately described by the term working
memory (Baddeley, 2009). Literally speaking, working memory is the sub-system of the IP
system where information is “worked on” to increase its comprehensibility, memorability,
and applicability to intellectual and practical problems. Consequently, IP theorists often use
“STM” to refer to the baseline capacity of the IP system’s temporary information store and
“working memory” or “WM” to refer to the functions and processes associated with STM.
Baddeley (2009) proposes that WM has four components. The central executive is the
WM system’s “master” component that coordinates the functions of its three sub-systems.
The first of these sub-systems is the phonological loop, the sub-system of WM that verbally
codes information. The second is the visuospatial sketch pad in which information is
represented visually. The third is the episodic buffer whose task is to impose order on
information and to link verbal and visual codes to create multi-dimensional representations of
it. For instance, when you are reading a novel, the episodic buffer keeps a running chronology
of the story’s events and links the images your mind forms of characters, events, and settings
to the words of the novel itself and to words that your WM retrieves from long-term memory
to interpret the story. As you might guess, the working memory cannot function
independently of the system’s permanent store of information. In fact, the more relevant
knowledge the IP system has on hand for processing new information, the more efficiently the
WM functions.
Long-Term Memory
Long-term memory (LTM) is where the IP system permanently stores information. It is
constantly reorganizing information and compressing it to make room for more. To accomplish
these tasks, it tends to organize information into categories that progress from broad to
narrow.
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Explicit and Implicit Memory. The two broadest categories of information in LTM are explicit
memory and implicit memory. The easy way to remember what explicit memories are is to
note that they are declarative (from the root word to declare), that is, you can describe them
quite well and communicate them precisely to others using only words. By contrast, implicit
memories are nondeclarative, meaning that they cannot be effectively communicated in
words. For example, think about answering these two questions:
What is the capital of Texas?
How do you ride a bicycle?
Could you answer both questions equally well using only words? Answering the first question
requires nothing more than verbal knowledge. By contrast, words alone cannot answer the
second effectively. They must be accompanied by a physical demonstration. Consequently, IP
theorists would say that the first question taps explicit memory, while the second draws on
implicit memory.
Episodic and Semantic Memory. There are two subsystems in explicit memory, episodic and
semantic memory. Episodic memory includes memories of events, while semantic memory
contains general information. These two subsystems do not function independently. We need
semantic memory to help us interpret events. Likewise, some events provide us with
information that expands semantic memory. For example, suppose you went on a vacation to
Hawai’i. You would store your vacation memories in episodic memory in verbal codes that tap
into the vocabulary stored in your semantic memory. These codes would enable you to
describe your vacation to others. At the same time, it is likely that you would pick up bits of
information on your trip, such as the fact that Oahu is the third largest of the Hawaiian
Islands, that you would store in semantic memory. You would be able to retrieve your
knowledge of the relative sizes of the islands without retrieving memories of your vacation.
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Interestingly, semantic memory usually does not code information in terms of its
associated episodic context. That is, typically, we do not remember anything about the time,
place, or context in which we acquired a given bit of semantic information unless there is
something out of the ordinary about them (such as learning them in the context of a Hawaiian
vacation). Recollection of the context in which we acquire memories are called source
memories. The tendency to fail to store source memories strongly influences the accuracy of
eyewitness testimony. Witnesses frequently confuse having actually seen something with
having been told by others what they saw. In addition, lack of source memories renders
eyewitness accounts vulnerable to the misinformation effect, a phenomenon in which an
investigator’s questions contain information that a witness unknowingly incorporates into her
memory of the event.
When semantic memories are strongly tied to episodic memories that were formed in
the context of a striking emotional experience, they are often referred to as flashbulb
memories. For example, your episodic memory probably includes a recollection of how you
learned of the terrorist attacks of September 11, 2001. The context in which you learned of
the attacks, your source memory, is stored in episodic memory. Because of the emotions
surrounding the event, the IP system maintained a link between your episodic and semantic
memories of the attacks. Over time, however, as you learned more about the attacks and
failed to store source memories for the additional information you have acquired, the link
between your initial learning of the attacks and the facts you learned at that time has
weakened. That is, due to lack of source memories, your IP system has incorporated laterlearned information into your flashbulb memory. As a result, at this point, you probably have
difficulty remembering which facts you learned from the original source and which you
learned later.
REMEMBERING
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The Atkinson-Shiffrin model provides a good descriptive overview of the structures of the IP
system. The process of remembering involves the functions of these structures and how they
interact when we attempt to bring information to mind.
Remembering as Retrieval
Retrieval is the process of calling up information that has been stored in LTM. There are
various types of retrieval, some of which are more difficult than others. Recognition involves
matching new stimuli to those that are stored in LTM. It requires nothing more than the
realization that a given stimulus has or has not been encountered in the past. Recognition is
the remembering process that requires the least amount of mental effort.
In contrast, recall, requires a person to search her memory and retrieve a specific
piece of information. Clearly, recall is far more difficult than recognition. No doubt you have
had personal experience with a memory task that involves both recognition and recall. Have
you ever run into a person whom you know you have met (recognition) but whose name you
cannot remember (recall)?
Recall is easier when retrieval cues are present. For instance, in the “I-know-I-shouldknow-this-person’s-name” situation above, the person might trigger recall of her name by
mentioning the context in which you met her. Alternatively, she might jump-start your
memory by mentioning a person whom both of you know.
The process of relearning, in which you reprocess previously learned information, also
involves retrieval cues. However, in this case, the information itself serves as its own
retrieval cue. For example, suppose you are reading Chapter 2 for a second time because you
can’t quite recall all of the details regarding classical conditioning. Often, just reading the
first sentence or two of a paragraph will cause you to recall its contents more effectively than
you could prior to rereading.
Remembering as Reconstruction
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In most cases, effectively remembering information requires retrieval as well as
reconstruction, the building up of complete, but not always accurate, memories from
fragmentary recollections. Reconstruction typically results from an integration of new and
previously learned information. Two LTM information structures, schemas and networks,
strongly influence the process of reconstruction.
Schemas. In the course of his studies of cultural influences on memory, early memory
researcher Sir Frederick Bartlett suggested that information in long-term memory is organized
into schemas, generalizable aspects of specific experiences and content domains that are
stored in semantic memory. The IP system deploys schemas to comprehend and filter new
information and to reorganize old information. For instance, our “fast food restaurant”
schema includes paper-wrapped food and plastic utensils, while tablecloths and china are
part of our “fancy table service restaurant” schema. Cues such as the presence or absence of
a drive-through help us determine which schema is most applicable to a specific restaurant
regardless of whether we have ever eaten there. Consequently, we can use these two
schemas to make a decision about whether a given restaurant matches our desire for a
specific type of food, fits with whatever time constraints we are dealing with, and the
amount of money we want to spend.
The construction of schemas happens automatically as we acquire and use
information. Schemas are vital to comprehension and memory because both are
reconstructive processes. That is, memory does not function like a tape recorder. Instead, it
functions like an interpreter who summarizes some of a speaker’s statements in order to
expedite communication between the speaker and the audience for whom she is interpreting.
This strategy is efficient, but it may also distort the speaker’s intended message. Similarly, if
you witness a traffic accident that involves a stop sign, you create a mental summary of the
event using both what you actually saw and the general knowledge stored in your stop-sign
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Introduction to Learning Theories 56
schema (i.e., cars are supposed to stop at them; there are rules about who goes first at a 4way stop, etc.). As a result, your mental summary of the event may not faithfully represent
what you actually saw.
Schemas also play a crucial role in the development and deployment of domainspecific knowledge. For instance, what a person sees when he watches a football game
depends on how much he knows about the game. People who know little about football see
two groups of eleven players lined up opposite one another. By contrast, football coaches,
players, and others with a great deal of knowledge about the game mentally separate the
eleven players on each side into smaller groups of players. The ways in which these smaller
groups are arranged and the directions in which they move when the ball is snapped provide
clues that only those with large stores of relevant information can decipher about the plays
that offenses run and the strategies that defenses use to stop them. Thus, an important
principle derived from information processing research is that quantitative changes in
knowledge generate qualitative changes in the ways in which knowledge is processed. That is,
the more specific knowledge you have about a topic, the better able you are to think about it
in meaningful ways. Thus, meaningful thinking is not a content-free skill. Instead, it is the
natural by-product of accumulated information.
Although most schemas are experience-based, research suggests that a small number
of them may actually be inborn. For instance, infants seem to be born with a schema for what
a face is supposed to look like. They also appear to be born with schemas for categorizing
speech sounds. The story schema—the expectation that all stories include characters, setting,
plot, and a conclusion—may also be inborn.
Networks. Networks are similar to schemas but are semantic in nature. For instance, table
and chair are embedded in a network because they are semantically related. As a result,
when you process information about tables, you also think of chairs, usually without being
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Introduction to Learning Theories 57
aware of it. Research that involves asking people to recall whether specific words occurred in
sentences they have heard or read illustrates the ways in which networks influence memory.
In these studies, researchers expose participants to sentences such as “the girl sat down at
the table” and ask whether the word chair occurred in the sentence. About half of
participants will say yes. Such findings suggest that participants are actually inferring (a form
of reconstruction) rather than remembering and that their inferences are based on
expectations that are generated by networks of meaning stored in their LTMs. The inferring
process goes something like this:
Chairs belong with tables. Sitting down at a table usually requires a chair.
Therefore, it is reasonable to infer that the word chair was in the sentence. On
the other hand, the “table” might be a coffee table, in which case no chair
would be required. In addition, it is acceptable to say “sat down at the table”
without explicitly using the word chair, so it’s reasonable to infer that the
word chair was not in the sentence.
Of course, all of this thinking takes place in a fraction of a second, and such results
show that the IP system stores the gist, or meaning, of the language we hear and read
rather than the specific words it contains. Nevertheless, attempting to distinguish
between knowing facts and thinking about facts results in a false dichotomy.
Information such as what a chair is, what a table is, and how the two are related must
be stored in LTM for a person to be able to think about them in a meaningful way and
to create a memory of the gist of a sentence about a person sitting down at a table.
FORGETTING
As you have seen, some of the features of the IP system can lead to distortions that affect the
process of remembering. Similar, but distinctive, processes are at work in the process of
forgetting, the inability to retrieve information from memory that was previously accessible.
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Context Effects
You learned earlier that the source memories are usually not stored along with semantic
memories. There are some circumstances, however, in which the link between a memory and
the context in which it was acquired are so strong that the information cannot be recalled
unless the original context is reproduced. Such context effects were discovered by early 20th
century psychologists, but they were best documented in a classic study by Duncan Godden
and Allan Baddeley (1975).
Participants in the Godden and Baddeley study memorized words while submerged in
ten feet of water. When asked to recall the words on land, they performed poorly. However,
when asked to recall the words under water, they performed much better. Godden and
Baddeley also had the participants memorize words on land and try to recall them
underwater. Not surprisingly, their findings mirrored those of the underwater studies. That is,
the participants recalled words learned on land best when they were on land and worse when
they were underwater.
Physiological states also exert context effects on memory. For example, research
shows that participants who acquire information while under the influence of alcohol do not
recall the information accurately when sober (Goodwin et al., 1969). However, when they
consume alcohol, they are capable of remembering the information much more effectively.
Research on context effects suggests a possible explanation for episodes of forgetting.
When a person cannot remember something, it may be that the information was encoded
along with some feature of the context in which it was learned. Indeed, most people have
had the experience of forgetting something and remembering it upon returning to the
location in which they last thought of it. Similarly, test-takers may remember answers to
exam questions by visualizing the circumstances in which they studied. Moreover, if the test-
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taker’s levels of anxiety during studying and testing are about the same, then so-called “test
anxiety” works to the test-taker’s advantage.
The Curve of Forgetting
Historians of psychology credit Hermann Ebbinghaus (1850-1909) with carrying out the first
systematic studies of remembering and forgetting. Ebbinghaus reasoned that meaning affects
our ability to remember stimuli and that, in order to arrive at a “pure” measure of memory,
it would be necessary to use meaningless stimuli. As a result, he chose to use nonsense
syllables as to-be-remembered stimuli.
In the first phase of Ebbinghaus’s studies, he memorized lists of nonsense syllables,
repeating the lists until he could recall them perfectly. He made careful records of the
amount of time required to learn each list. In the next phase, he measured how much of each
list he could recall after varying amounts of time passed without having practiced it. Finally,
he relearned each list. Afterwards, he compared the amount of time required to learn each
list originally to the time it took to relearn it. Figure 3.2 illustrates the curve of forgetting
that Ebbinghaus documented (Ebbinghaus, 1885). As you can see from the red line in the
graph, forgetting occurs at a rapid rate for two days or so and then levels off.
Figure 3.2
Ebbinghaus’s Forgetting Curve
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Ebbinghaus also discovered the serial position effect, the tendency to forget
information in the middle of a list more easily than items at the beginning of end. The serial
position effect comes into play any time information is stored in memory in a specific order.
Therefore, to reduce the chances of this type of forgetting, the order in which information is
rehearsed should be varied unless the order itself has meaning.
Other Types of Forgetting
There are several types of forgetting other than those associated with context effects, the
curve of forgetting, and the serial position effect. Encoding failure, the failure to effectively
store information in LTM, is perhaps the most common of them. Typically, encoding failure
occurs when we store only the gist of new information in LTM without encoding the details.
This gives us the feeling that we have stored the information in memory when we really have
stored only its general theme and a few supporting details. Encoding failure is particularly
common in text learning, the kind of learning you are doing right now. When we read, we
tend to store generalizations rather than details. As a result, when we are asked to recall
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details, as might happen on a quiz, we think we “should” know them but cannot recall them.
The fact is that we can’t recall them because we never put them in LTM to begin with.
Another common type of forgetting is interference, information loss that is due to the
limited capacity of working memory. For example, suppose you have forgotten where you put
your car keys. You attempt to retrace your steps back to the point where you took the keys
out of your car’s ignition. Unfortunately, you find yourself able to remember a number of
specific events—setting a grocery bag on the kitchen counter, letting the dog out, leafing
through the mail, and so on—but are unable to recall where you left your keys. Most likely,
you have “forgotten” where you put your keys because your working memory was processing
information about these other events at the time you let go of the keys. The information you
were processing at the time interfered with your working memory’s ability to process
information about the location of your keys.
Motivated forgetting is information loss that is due to the emotional features of the
information. Suppose you wake up one morning and suddenly remember that you have a
dentist’s appointment but forgot to request a day off from work. “Oh well,” you think, “I’ll
just have to cancel the appointment.” It’s at least possible that you forgot the appointment
because you feel anxious about going to the dentist and are relieved that you now have an
excuse to put it off.
Have you ever taken a test and suddenly remembered the answer to one of the
questions after you turned in your exam? If so, you have experienced retrieval failure, the
failure to retrieve information that is stored in LTM in a context in which the information is
necessary. Retrieval failure happens for a variety of reasons. For example, the way in which
the information is stored in LTM may not match the features of the situation in which it is
needed. This can happen in a situation in which the wording of test questions varies from that
of a textbook. The textbook might say “73% of adolescents”, while the test question says
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 62
“about three-fourths of adolescents.” Whatever the cause of retrieval failure, it is often
associated with the tip-of-the-tongue (TOT) phenomenon, confidence in the fact that
needed information is stored in LTM even though it cannot be recalled.
Finally, the neurological underpinnings of memory are responsible for another type of
forgetting, consolidation failure. For instance, people who lose consciousness typically
cannot recall events that immediately preceded the loss of consciousness. This happens
because the physiology of unconsciousness interfered with the physiological processes that
must occur in order for the brain to form a memory. More commonly, consolidation failure
occurs because of sleep deprivation. Neuroscientists have learned that memories are
consolidated during phases of rapid eye movement (REM) sleep that occur as a normal part
every night’s sleep. When the amount of time devoted to sleep decreases, so does the
number of REM sleep phases. As a result, individuals who get less than optimum amounts of
sleep are unable to consolidate some of the memories that their IP systems process during the
day.
EVALUATION OF INFORMATION PROCESSING THEORY
Like all of the theories discussed in this text, information processing theory has had a sizeable
impact on the scientific study of learning. The theory’s heuristic value is not in doubt.
However, to determine its usefulness, assessment of its value with regard to these questions
is required:
Does it explain the basic facts of learning?
Does it generate testable predictions?
Does it lead to practical strategies that are relevant to real-world learning and
instruction?
The explanatory and hypothesis-generating power of information processing theory are
impressive. There are few cognitive events that cannot be described and studied effectively
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 63
by combining the features of the Atkinson-Shiffrin model of the IP system with Baddeley’s
model of working memory. Where IP theory sometimes falls short, however, is in the transfer
of the research findings it has generated from the controlled world of the laboratory to the
“messy” world of real-world learning contexts. Nevertheless, IP researchers have addressed
this problem by applying their theoretical models and research methods to the study of
everyday and academic cognitive functioning. As a result, the theory is now accumulating a
great deal of evidence on everyday and academic cognition that stands alongside its much
larger body of laboratory-based findings. These studies have revealed that perhaps the
greatest strength of the IP model is its capacity for generating hypotheses that can address a
seemingly infinite array of questions about cognition. For these reasons, the IP approach is
arguably the most influential perspective in learning theory today.
Despite the clear scientific value of IP theory, it is also necessary to ask whether the
theory is capable of producing instructional strategies that influence learning. On this
criterion, IP theory also receives high marks, not so much because of strategies that derive
directly from the theory but more so for its capacity for providing empirical support for the
effectiveness of one strategy over another.
SUMMARY
In this chapter, you have learned how the information processing system stores and loses
memories. A key point was that remembering involves both retrieval and reconstruction.
Consequently, task-relevant knowledge stored in long-term memory is vital to the functioning
of the entire system. Moreover, the functional efficiency of the working memory strongly
affects memory processes.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 64
KEY TERMS, CONCEPTS, AND PEOPLE
micro-theories
Richard Atkinson
Richard Shiffrin
sensory memory
icons
echoes
attention
short-term memory (STM)
automaticity
chunking
memory strategies
Allan Baddeley
working memory (WM)
central executive
phonological loop
visual-spatial sketch pad
episodic buffer
long-term memory (LTM)
explicit memory
implicit memory
semantic memory
episodic memory
source memories
flashbulb memories
retrieval
recognition
recall
relearning
reconstruction
Sir Frederick Bartlett
schemas
networks
forgetting
context effects
Duncan Gooden
state-dependent memory
Hermann Ebbinghaus
curve of forgetting
serial position effect
encoding failure
interference
motivated forgetting
retrieval failure
tip-of-the-tongue (TOT) phenomenon
consolidation failure
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 65
4
COGNITIVE DEVELOPMENT
THE DEVELOPING BRAIN
PIAGET’S THEORY OF COGNITIVE DEVELOPMENT
THE INFORMATION-PROCESSING APPROACH TO COGNITIVE DEVELOPMENT
VYGOTSKY’S THEORY OF COGNITIVE DEVELOPMENT
EVALUATION OF COGNITIVE DEVELOPMENTAL THEORY
SUMMARY
KEY TERMS, CONCEPTS, AND PEOPLE
You should remember from Chapter 1 that learning is a relatively permanent change in
behavior or knowledge. Learning stands in contrast to maturation—change due to an inborn,
genetic, species-specific, plan—and development—change that results from an interaction of
learning and maturation. Consequently, theories of development are broader than theories of
learning. In addition, as you learned in Chapter 1, theories of cognitive development, ageassociated changes in intellectual skills and knowledge, are consistent with organismic
epistemology. They assume that learners are active contributors to these changes and that
nature and nurture are interactive influences. Moreover, theories of cognitive development
emphasize qualitative change and, as a result, typically include stages. Because all theories
of cognitive development assume that age-related change arises from an interaction between
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 66
nature and nurture, specifically the maturation of the brain and environmental demands on
its functions, it is important for you to become acquainted with some of the features of the
developing brain before delving into theories of cognitive development.
THE DEVELOPING BRAIN
You may remember from high school biology that the brain consists of two types of cells. The
first of these, neurons, are responsible for brain functions. Figure 4.1 depicts the parts of the
neuron and their functions. The second type of cell, glial cells, support neurons and give the
brain its shape. The brain attains its full complement of neurons in the early weeks of
prenatal development. Thus, the weight and size of the human brain increase with age not as
the result of the development of new neurons, but as the result of the growth of neurons.
Figure 4.1
The Neuron
Dendrites: Cell body extensions that receive messages from the next neuron
Cell body: Contains the nucleus and carries out vital function such as respiration
Axon: Tail-like cell body extension that carries messages to the next neuron
Nerve ending (synapse): Junction of one neuron with another
Neuronal growth spurts are one of the features of brain maturation that influence
cognitive development. These spurts involve changes in neuronal size and the number of
interconnections among neurons. Each spurt is followed by a period of pruning in which
inefficient or infrequently used neuronal connections are destroyed. These growth/pruning
cycles affect different areas of the brain at different ages. Those that are relevant to
cognitive development occur in in the cerebral cortex, the convoluted outer covering of the
brain that is the locus of cognition. Such spurts may be responsible for stage-like changes in
cognitive functioning, because each spurt influences brain functioning differently. Table 4.1
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 67
lists some of the major spurt/pruning cycles and their effects on cognitive functions. Note
that brain growth continues well into adulthood. Thus, some of the key structures in cognitive
functioning do not reach maturity until long after most individuals have completed their
formal schooling.
Table 4.1
Spurt/Pruning Cycles in Brain Development
Age
20 months
Functions Affected
Goal-oriented planning
4-6
First-language fluency, predicting outcomes
6-8
Writing, drawing, emotional control, association of sensory and cognitive functions
10-12
Logic, planning, memory
13-15
Spatial perception, integration of sensory, language, and motor functions, conscious control and
organization of thought processes, risk assessment
17-20
Logic, planning, risk assessment
Myelination, the development of an insulating layer of fat on neuronal axons, is
another key maturational process that underlies cognitive development. It is largely
responsible for the speed at which impulses travel from one neuron to the next. Like brain
growth, myelination occurs in spurts. One of the earliest structures to be myelinated is the
hippocampus, the structure that is primarily responsible for memory formation. Its neurons
are nearly fully myelinated by age 3. By contrast, the association areas, neuronal
connections that link sensory, motor, and cognitive functions, in the brain’s frontal lobes are
not fully myelinated until age 12. The neurons of the reticular formation, the brain structure
that controls attention, are fully myelinated much later, typically around age 25.
Lateralization is the process through which brain functions become relegated to the
right or left hemisphere of the cerebral cortex. Language functions are lateralized to the left
side of the brain in almost all humans prior to birth. However, the process of lateralization is
highly dependent upon development of the corpus callosum, the membrane that connects
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Introduction to Learning Theories 68
the two hemispheres and allows for communication between them. As a result, most
functions that require lateralization to attain optimal efficiency are not lateralized until after
birth. Early childhood, approximately ages two to six years, is the period of life during which
the corpus callosum grows and matures most profoundly. Consequently, lateralization tends
to be complete in most children by age 8 or so. The latest lateralizing function is spatial
perception, the capacity for processing information about locations and spatial relationships
among objects. Differences between 8-year-olds and younger children in the ability to use
and generate maps as well as their performance in activities that involve flying objects (e.g.,
balls, Frisbees) illustrate the impact of lateralization on spatial perception.
PIAGET’S THEORY OF COGNITIVE DEVELOPMENT
Swiss philosopher Jean Piaget (1896-1980) began his science career while still in grade school.
He learned the methods of the emerging field of natural history and applied them to his
observations of plant and animal life in the woods and mountains that surrounded his boyhood
home in Neuchâtel, Switzerland. By the age of 10, he had published his first scientific paper,
and at 15, he was offered a research position at a prestigious museum in Paris. Piaget
graduated from the University of Geneva with a Ph.D. in zoology at the age of 21 and began
to pursue a research agenda that focused on the evolution of human intelligence. While
administering intelligence tests in Alfred Binet’s lab in Paris, Piaget observed children’s
illogical responses to the questions he posed and became more interested in their reasoning
than in whether their answers were correct. As a result, when he joined the faculty of the
University of Geneva, Piaget began to study children’s reasoning and produced a set of
findings that continue to spur research, debate, and theorizing.
Piaget’s Great Discovery: Four Stages of Cognitive Development
Recall from Chapter 1 that the first goal of the scientific study of learning is to produce
accurate descriptions of change. Piaget combined the meticulous observational methods of
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Introduction to Learning Theories 69
the early 20th century’s natural historians with the response-dependent questioning methods
of the psychoanalysts to explore children’s and teens’ responses to a variety of problems. The
description of age-related changes in reasoning that emerged from these studies has proven
to be one of the most accurate and most frequently replicated research findings in the entire
field of psychology. Piaget argued that his findings were best thought of as representing four
universal, hierarchical stages of cognitive development. Each stage features a unique set of
intellectual tools and a few key skills that must be developed before moving on to the next
stage.
The Sensorimotor Stage. In Piaget’s sensorimotor stage, infants use their senses and motor
actions to make sense of the world. At about eight months of age, they develop object
permanence, the understanding that objects continue to exist even when they cannot be
seen, felt, or heard. Over the next year or so, a number of key intellectual tools emerge from
this rudimentary concept. These tools include:
Mental representation: The ability to think about non-present objects (Example:
Searching for a desired object or person)
Means-end behavior: The ability to formulate and carry out goal-based plans
(Example: Using a chair to climb on the kitchen counter to get a cookie)
Pretend play: The use of one object to represent another (Examples: Pretending
that a block is a car, imaginary role play)
The Preoperational Stage. Between the ages of 2 and 6, children continue to use their
senses and motor actions to explore the world, but they also have a firm grasp of the
semiotic function, the understanding that symbols can represent objects, people, and ideas
and that these symbols can be used for both thinking and communicating. Thus, words,
images, and pretend play are the primary intellectual tools children use in Piaget’s
preoperational stage. As a result, preschoolers are much more cognitively sophisticated than
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infants are. However, their thinking includes a number of important logical flaws. These flaws
include:
Egocentrism: The belief that others see the world from the child’s point of view
(Example: Using pronouns without antecedents in recounting an event. The child
knows to whom he is referring when he says “she,” therefore, listeners should as
well.)
Centration: The tendency to process information in terms of single variables
(Example: The size of a coin determines its value relative to other coins.)
Appearance/reality confusion: The reliance on appearance as the primary
indicator of the characteristics of a substance (Example: Living things movie;
therefore a leaf “comes to life” when the wind blows it across the yard.)
Transductive reasoning: Reasoning based on the belief that two events that occur
close together in time are causally related (Example: A magician says
“abracadabra” before pulling a rabbit out of a hat; therefore, the word
“abracadabra” caused the rabbit to come out of the hat.)
As preschoolers accumulate experiences in which they act on and observe the physical and
social worlds, these flaws gradually decline, paving the way for the emergence of the next
stage of development.
The Concrete Operational Stage. In the concrete operational stage, 6- to 12-year-olds
develop a powerful set of intellectual tools that allow them to achieve adult-level mastery of
some of the most important features of the physical and social worlds. These tools emerge
gradually and typically co-exist with the declining, but still influential, logical flaws of the
preoperational stage. One of the key features of concrete operational thinking is
conservation, the understanding that appearance does not alter the characteristics of a
substance. For example, in one of Piaget’s classic problems, children are asked whether a ball
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Introduction to Learning Theories 71
of clay that has been rolled into a sausage shape contains more or less clay than it did when it
was in a ball. Children exhibit conservation by correctly observing that the amount of clay is
the same regardless of its shape and exhibiting the intellectual tools below in the reasons
they give for reaching the conclusion that the amount of clay did not change:
Reversibility: The ability to use backwards thinking to solve problems; the primary
intellectual tool developed in the concrete operational stage (Example: “If you roll
it into a sausage shape, you can just make it into a ball again to see that it’s still
the same amount.”)
Inductive reasoning: Recognizing patterns and using them to formulate rules
(Example: “You didn’t add any clay or take any away. That’s why it’s still the same
amount.”)
Decentration and compensation: The ability to use multiple variables to process
information (Example: “The ball looks like it has more clay because it’s taller than
the sausage, but you have to take into account that the sausage is wider than the
ball.”)
In addition to conservation and the intellectual tools that underlie it, children in the concrete
operational stage also develop an understanding of class inclusion, the reasoning that
underlies hierarchical classification systems such as the familiar class-order-family-genusspecies scheme that is used to classify living things. In addition, they acquire the capacity for
seriation, the ability to arrange a large number of objects in size order. Younger children can
order three to five objects according to size. By contrast, by the end of the concrete
operational stage, children have developed a highly flexibility set of skills for using magnitude
as an organizing principle.
Piaget used the term concrete when in the name of this stage because he concluded
that its purpose was to enable children to develop an accurate mental model of the world. As
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such, the cognitive milestones of this stage involve abstract ideas that can be represented in
the physical world. For example, children develop an understanding of the associative
property ([(a + b) + c] = [a + (b + c)]) during this stage. It can be stated as a rule (abstract) or
demonstrated with objects (concrete). Thus, concrete operational reasoning involves a great
deal of abstract thinking, but the content of the concrete operational thinker’s abstractions is
limited to objects, people, and ideas that can be easily represented in the physical world.
Because of this limitation, children in the concrete operational stage do not function well
when asked to solve problems that involve deductive reasoning, reasoning from a premise to
a valid conclusion. As a result, when a concrete operational child is asked to think creatively
in response to “what if?” questions, he typically copies the world as he knows it instead of
proposing new possibilities. Thus, one of the signs indicating that a child has just about
completed the concrete operational stage is his emerging capacity for deductive thinking that
renders him capable of developing simple, informal theories about academic subject matter
as well as important events in his own life (e.g., “What might happen if I skip a step when
doing this kind of math problem?” or “How will my life be different if I make the basketball
team?”)
The Formal Operational Stage. Thanks to the solid grasp of reality that emerges from the
concrete operational stage, children enter the formal operational stage around age 12 or so
with a rapidly emerging cognitive tool, hypothetico-deductive thinking, a form of deductive
reasoning in which the premise is hypothetical, that is, factually untrue. Hypotheticodeductive thinking is the reasoning that underlies scientific inquiry and, as a result, it endows
teens with a capacity for abstraction that goes far beyond that of the concrete operational
stage. Equipped with hypothetico-deductive thinking, the adolescent is no longer tied to
abstractions that have concrete referents in the physical and social worlds. Thus, she enters a
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Introduction to Learning Theories 73
new world in which she can both generate and test hypotheses in the inner world of her mind
and adjust her outer behavior accordingly.
However, the very cognitive power that makes hypothetico-deductive thinking such a
monumental cognitive advance generates a number of intellectual vulnerabilities. Changes in
these vulnerabilities that occur over the ten to fifteen years that follow the first appearance
of formal operational thought constitute the difference between adolescent and adult formal
operational thinking. The cognitive vulnerabilities exhibited by teens that gradually diminish
in the early adult years include:
Possibilities unconstrained by probabilities: Believing “anything is possible” with
minimal regard for pre-existing limitations (Example: “Even though my progress
report average is 32, I might be able to pass geometry if I get 100s on all the daily
quizzes for the rest of the six-weeks.”)
Naïve idealism: Proposing simple, usually excessively optimistic, solutions for
complex problems (Example: “If all the rich countries shared their money with the
poor countries, there wouldn’t be any more poor countries.”)
Adolescent egocentrism: Believing that others are equally interested in and
concerned about one’s strengths and weaknesses as one is himself (Example:
“Everybody’s looking at me funny today. They must be thinking that the haircut I
got yesterday looks weird.”)
Personal fable: Unrealistic optimism or pessimism along with poor causal reasoning
about one’s autobiography (Examples: “My parents divorced when I was 3. That’s
why I don’t have a girlfriend.” or “He hasn’t called in three weeks, but I know
we’re meant to be together. I’ve already picked out my wedding dress.”)
Imaginary audience: Mentally rehearsing and adjusting behavior for an imagined
peer audience (Example: “I can’t go to the movie with my Mom. Some of my
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Introduction to Learning Theories 74
friends might be there. What will they think if they see me at a movie with my
Mom?”)
Unique invulnerability: Believing that others are more vulnerable to risks than
oneself (Examples: “It doesn’t matter that much if I start smoking now. I’m young
and healthy so I can quit later and avoid getting cancer.” or “I know I should be
using protection, but I just don’t see myself as a teen mom. I don’t think
pregnancy is in my future.”)
Ages and Stages. The ages that Piaget found to be associated with each stage are averages
for children and teenagers in industrialized societies. Thus, there is some degree of individual
variation. In addition, there are no periods of true stage stability, no “plateaus”, in other
words. A 6- to 12-year-old, for instance, is best thought of as being in the process of
developing concrete operations. She won’t show formal operational thought until her
concrete operational skills have fully emerged. Thus, the sequence of stages is universal, but
the ages at which individuals achieve each of them can vary.
Piaget’s Explanation of Cognitive Development
In studies whose publication dates span nearly a century, researchers have replicated Piaget’s
observations of children’s and teen’s thinking across a wide variety of cultural settings. Thus,
there is wide agreement among developmentalists regarding Piaget’s description of cognitive
development. However, the theory that he proposed to explain his findings has been the
subject of much debate.
Schemes. The basic cognitive unit of change in Piaget’s theory is the scheme, a French word
that is best translated as blueprint. Schemes are sometimes referred to as action plans. Their
purpose is to guide behavior in the presence of specific cues. They are the products of
organization, a cognitive process that extracts generalizable information from specific
experiences. For example, a toddler who plays with tennis balls develops a scheme that she
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Introduction to Learning Theories 75
applies to all objects that resemble tennis balls. Consequently, when she encounters a
tangerine for the first time, she treats it as if it has the same properties as a tennis ball. This
is because the process of organization has extracted a scheme, an action plan, from her
experiences with tennis balls. The cues that trigger the scheme are the similarities between
tangerines and tennis balls. Both are round, for example, and the physical actions that enable
an individual to pick up and hold on to a tennis ball also work for tangerines.
Schemes change through adaptation. Adaptation involves two processes, assimilation
and accommodation. Assimilation occurs when an individual applies an existing scheme to a
new experience. So, the toddler mentioned earlier assimilates a tangerine to her tennis ball
scheme when she picks up the piece of fruit and holds it as she would a ball. But she is likely
to discover fairly quickly that there are important differences between tangerines and tennis
balls. One, obviously, is that tangerines don’t bounce. Consequently, when the toddler
applies the throwing part of her tennis ball scheme to the tangerine, she will receive some
information that her ball scheme cannot deal with. As a result, she will have to change the
tennis ball scheme such that it excludes fruit. The process of changing a scheme to fit new
information is called accommodation.
When schemes fit reality, that is, when the toddler is clear about the difference
between balls and round fruits and demonstrates this understanding behaviorally, the
schemes are said to have completed the process of equilibration, a state in which
assimilation and accommodation are in balance. Schemes must be exercised to achieve
equilibration. In other words, the toddler has to throw everything in her world that resembles
a tennis ball—plums, peaches, nectarines, Christmas tree ornaments, knick-knacks at
Grandma’s house—in order to equilibrate her tennis ball scheme.
Stages as Schemes. According to Piaget, each stage is a network of schemes that are
qualitatively distinct from those of the other stages. Moreover, the schemes of each stage
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must reach equilibration before those of the next stage can emerge. Consequently, for
Piaget, an infant in the sensorimotor stage is in the process of developing sensorimotor
schemes. As soon as a sufficient number of sensorimotor schemes reach equilibration, the
symbolic schemes of the preoperational stage begin to emerge. When these are equilibrated,
children enter the concrete operational stage and begin developing rule-based schemes that
represent logical connections among objects, people, and ideas in the physical world. When
these rule-based schemes are equilibrated, the hypothetico-deductive schemes of the formal
operational stage make their appearance. Thus, adults should be thought of as having four
types of equilibrated schemes (the four stages), teens as having three, children as having
two, and infants as having one.
Influences on Progression through the Stages. As we noted earlier, the ages at which
individuals attain each stage vary from one person to another. Piaget proposed that four
factors are responsible for age variations. He described each factor as necessary but not
sufficient for cognitive development. That is, no single factor is responsible for cognitive
development. All of them are necessary.
First, maturation of the central nervous system constrains cognitive development.
That is, Piaget argued that each stage depends on the development of one or more
mechanisms in the brain and cannot develop until those mechanisms reach maturity. Clearly,
children mature at different rates, and these rates are reflected in cognitive development as
well as in other behavioral domains. Furthermore, factors such as illness and malnutrition
that adversely affect maturation also hinder cognitive development.
Second, according to Piaget, social transmission, or information that we get from
others through instruction, modeling, and interaction affects the rate of cognitive
development. Formal schooling is one important source of social transmission. As a result,
children who do not receive formal schooling due to poverty or customs exhibit slower rates
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of progression through Piaget’s stages than those who go to school. However, the quality of
social transmission is equally as important. Thus, children who attend poor quality
educational programs also develop more slowly than peers in high quality programs. The same
is true for access to adults.
Third, children require experience that includes opportunities to act on the world and
observe the results. Fourth, equilibration, which Piaget described as the cognitive system’s
tendency to improve its schemes until they achieve the best fit possible with reality, is
required for cognitive development. Experience and equilibration are interdependent. Think
back to the example of the toddler and the tangerine. The environment provided her with the
tangerine. Her cognitive system supplied the scheme for acting on it. Because she had the
opportunity to apply her tennis ball scheme to the tangerine and observe the outcome, her
cognitive system was able to accommodate the tennis ball scheme appropriately and
construct a new scheme for the tangerine.
Citing Piaget’s stages of cognitive development and his writings on the four factors
that influence progression through them, some educators have interpreted his work to mean
that instruction in a given skill or concept should not occur until the child’s mind is “ready”
for it. Piaget did not agree. He argued that cognitive development is dependent upon the
degree to which the environment challenges children’s schemes and urged educators to
approach instruction with this principle in mind. However, he also pointed out that
instruction that taps children’s reasoning abilities may be more or less efficient depending on
its timing relative to students’ cognitive developmental status. In other words, secondgraders might require several weeks of instruction in a logic-based skill or concept (e.g.,
place value) that third-graders can master in a few days. Balancing the interplay between
challenging children’s schemes and planning for instructional efficiency is the essence of
useful classroom applications of Piaget’s theory of cognitive development.
© Denise R. Boyd, Ed.D.
Introduction to Learning Theories 78
THE INFORMATION-PROCESSING APPROACH TO COGNITIVE
DEVELOPMENT
In Chapter 3 you learned that the model of the information processing (IP) system that
researchers use to study cognitive functioning includes three components: sensory memory,
short-term/working memory, and long-term memory. The IP system functions in children in
the same way that it does in adults. However, there are several age-related variables that
cause the IP systems of children to function much less efficiently than those of adults do.
Thus, some developmentalists have proposed that these variables do a better job of
explaining the patterns of cognitive development that Piaget’s observations brought to light
than his own theory does.
Information Processing Efficiency
The efficiency of the IP system is strongly related to chronological age. Thus, IP theorists
argue that individuals’ performance on Piaget’s tasks improves with age because of increases
in the efficiency of the IP system. These increases result from improvements in processing
speed, short-term memory capacity, working memory functioning, automaticity, and
expertise.
Processing Speed. As neurons become myelinated, the amount of time required for an
impulse to travel from one point to another in the brain declines. Behaviorally speaking, this
means that, given the same knowledge, an older child can access it more rapidly than a
younger child can. For example, if a 6-year-old and a 10-year-old are shown photos of
everyday objects and asked to name them as quickly as possible, the 10-year-old will
outperform the 6-year-old. The reason for this age difference is that more of the neurons in
the 10-year-old’s brain are myelinated, so he processes information more quickly. As a result,
he can retrieve the names of the objectives from memory more rapidly. The more rapidly an
individual can retrieve information from memory, the more efficiently his IP system works.
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Short-term Memory Capacity. The baseline capacity of short-term memory increases a great
deal as children and teens get older. Digit span tasks are typically used as operational
definitions of the baseline STM capacity. Table 4.2 lists average digit span test scores from
ages 5 to 16 years. As you can see in the table, STM capacity increases a great deal across the
elementary and secondary school years. Moreover, the average child does not reach adult
capacity (7 +/- 2 bits of information) until the age of 16. As you read in Chapter 3, short-term
memory capacity contributes to the efficiency of the IP system because it limits the amount
of information that can be processed at one time. So, increased capacity means decreased
limitations and, as a result, increased efficiency.
Table 4.2
Age and Digit Span
Age
Average Digit Span
5
3.5
6
4.1
7
4.4
8
4.8
9
5.2
10
5.6
11
5.6
12
6.2
13
6.2
14
6.2
15
6.7
16
7
Another way of looking at the impact of STM capacity on the efficiency of the IP
system is to relate it to a simple memory task such as memorizing a list of words. If the list
includes six words, memorizing it will be a single learning task for the typical 16-year-old. Her
STM capacity allows her to process all six words at once. In contrast, the typical 5-year-old
will have to break it down into two learning tasks, because she can only process three words
at a time.
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Working Memory Efficiency. Most memory tasks, especially those associated with academic
learning, are far more demanding than memorizing a list of words. As a result, they tap the IP
system’s working memory capacity, that is, the coordinated efforts of the WM’s central
executive, phonological loop, visuospatial sketchpad, and episodic buffer, all of which you
read about in Chapter 3. Children’s working memories are far more limited than simple
measures of baseline STM, such as digit span tests, suggest. This is because each component
of the WM depends on the development and myelination of specific brain structures.
Moreover, maintaining information in WM requires selective attention, which, in turn,
requires inhibition of competing information, what we call in everyday language
“distractions.” As you read earlier in this chapter, selective attention depends on myelination
of the reticular formation, a milestone that is not attained until the mid-twenties.
To get additional insight into age differences in working memory, think of the common
task of retracing your steps to find a lost object. As you work backwards, you have to store
the outcome of the first step and an explanation as to why it failed to lead you to the object
somewhere in working memory. You might verbalize it and store it in your phonological loop,
or you might visualize it and store it in your visuospatial sketchpad. Either way, you have to
keep rehearsing it or you will forget that the missing object wasn’t in the first place you
looked. As you move from step to step, you have to keep rehearsing the results of all the
prior steps at the same time. Consequently, as you probably know from personal experience,
retracing your steps to find something taxes the working memory even in adults, especially
when you factor in the strain that anxiety about having lost something exerts on working
memory efficiency. Furthermore, as you would probably predict, this kind of strategy quickly
overloads a child’s working memory. As a result, children either abandon the search after the
first couple of steps or they needlessly return to the first step time after time. Similarly, if
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decoding and/or comprehending a text overwhelms a student’s working memory capacity, she
will not be able to remember it.
Automaticity. As you read in Chapter 3, automaticity is the chief means by which the IP
system overcomes the limitations of short-term memory. It can be achieved at any age simply
by repeatedly rehearsing and retrieving a memory. Obviously, as children and teens mature,
they develop automaticity for more and more bits of information. With growing automaticity
comes increased efficiency in the IP system, particularly in working memory.
Unfortunately, automaticity is often disparaged as “rote memorization” and
condemned by educators. However, it is absolutely essential to the efficiency of the IP
system. There are many studies showing, for instance, that automaticity of letter-sound
connections is essential for beginning readers because it allows them to devote working
memory capacity to connecting written words to their spoken equivalents. When a child uses
her automatized decoding skills to read a text that contains familiar words, concepts, and
grammatical structures, comprehension happens automatically as well. When both decoding
and comprehension function automatically, the chances that the child will be able to
remember, answer questions about, and meaningfully process a text (e.g., identify the main
idea) greatly improve thanks to the increased efficiency of WM.
Expertise. As you have just read, comprehension occurs automatically when children read
texts comprised of familiar words, concepts, and grammatical structures. Collectively, the
relevant knowledge we tap into when performing a cognitive task is called expertise. As
individuals go through life, the amount of information they have stored in long-term memory
grows. As a result, no matter what kind of cognitive task is involved, the chances are quite
good that an adult will have more expertise than a teenager, who, in turn, will have more
expertise than a child. Consequently, age-related increases in expertise help to explain age
differences in IP efficiency.
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Clearly, however, regardless of age, wide differences in expertise exist across
individuals. Thus, individual differences in expertise help to explain individual differences in
IP efficiency. Moreover, studies that compare children who are expert chess players to adults
who know little about the game show that, when children possess more expertise than adults
do for specific memory tasks, children tend to outperform adults. These findings suggest that
task-relevant knowledge helps the IP system overcome limitations that are associated with
brain maturation. This is a very important principle, because expertise is highly dependent
upon experience. In other words, unlike maturation, the development of expertise can be
very strongly enhanced by teachers, parents, and agents in the environment. This is so
because any experience that helps a child develop a rich base of knowledge has the potential
to improve the efficiency of the IP system. Moreover, a child’s IP system will function most
efficiently when performing memory tasks within the content domains with which he has the
greatest amount of expertise.
Metacognition
As children get older, the amount of knowledge they accumulate about the nature of
knowledge itself and the process of acquiring it increases dramatically. Metacognition,
thinking about thinking, develops along with this knowledge base. The development of
metacognitive knowledge and the ability to intentionally and effortfully apply it to the inner
workings of the IP system contributes to the overall efficiency of the system. Thus, IP
theorists argue that children may perform poorly on Piaget’s problems because they focus on
irrelevant features of them, fail to monitor what is going on in their minds as they attempt to
solve them, or lack the ability to apply memory strategies that may be helpful.
Selective Attention. Suppose a teacher who normally writes the day’s agenda on the left side
of the board (from the students’ perspective) decides for some reason to record it on the
right side one day. As an adult learner, you would notice the change but would also recognize
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that it is irrelevant to the content of the agenda. As a result, you would attend to the content
rather than the placement of the agenda. By contrast, children are likely to pay more
attention to the unusual placement of the agenda than to its content. Psychologists use the
term selective attention to refer to the capacity to focus on the most important and relevant
aspects of an information array. Due to the slow rate of myelination in the reticular
formation, children are far less able than teens to sort out irrelevant from relevant
information. And teens are far less able than adults to do so. As a result, both children and
teens often expend attentional resources on processing information that is irrelevant to the
task at hand.
Cognitive Monitoring. Have you ever been reading a text and suddenly stop yourself because
you realize that you don’t understand what you are reading? Most likely, you went back to the
beginning of the text and reread it. Such episodes result from adults’ capacity for cognitive
monitoring, the ability to track the progress and effectiveness of information processing as it
is happening. Children are less able than adults to monitor their thoughts in this way primarily
because of age differences in WM efficiency.
Metamemory. An individual’s knowledge about memory strategies and how they can be used
to enhance memory functioning is called metamemory. Children’s knowledge of the memory
strategies you learned about in Chapter 3 and their ability to use them independently
improves with age (see Table 4.3). However, due to limitations on children’s working
memories, they are often unable to deploy strategies effectively. To use a memory strategy,
there must be sufficient WM capacity available for both the to-be-remembered information
and the strategy itself. Consequently, in describing children’s approach to memory strategies,
IP theorists often differentiate between production deficiencies, the inability to apply a
strategy unless reminded to do so, and utilization deficiencies, the ineffective use of a
known strategy due to WM limitations.
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Table 4.3
Age and Independent Use of Memory Strategies
Strategy
Definitions and Academic Learning Examples
Age
Repeating information until it is no longer needed or until it can be easily retrieved from
long-term memory
Rehearsal
6
Example: Repeating letter-sound connections until they can be recalled with 100%
accuracy
Sorting information into categories for storage in long-term memory
Organization
10
Example: Grouping spelling words by pattern categories to facilitate studying for a test
Relating new and previously learned information in meaningful ways
Elaboration
Example: Relating the term binary to the word bicycle to remember that it means a base 2
numbering system
15
Creating non-meaningful links between new and previously learned information
Mnemonics
Example: Visualizing John Calvin and Martin Luther in referee uniforms to remember that
they were key figures in the Reformation
18
Directly teaching memory strategies to students, providing them with opportunities for
practice, and giving cues that facilitate strategy transfer from practiced problems to new
ones can solve the production deficiency problem, especially with older children and teens.
However, such instruction does not address the utilization deficiency issue. As a result,
facilitating improvements in WM efficiency in the ways that were mentioned earlier not only
enhances WM but also indirectly influences children’s metamemory functioning. Moreover,
although independent creation of mnemonics does not appear until adulthood in most people,
even very young children have little difficulty learning and using adult-supplied mnemonics
that are linked to specific content, such as using the acronym HOMES to remember the names
of the Great Lakes.
Neo-Piagetian Theories of Cognitive Development
Like IP theorists, advocates of neo-Piagetian theories of cognitive development argue that
age differences in the effectiveness and efficiency of the IP system explain children’s
performance on Piaget’s tasks. However, they integrate a number of Piaget’s theoretical
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concepts into their explanations of cognitive development. For instance, neo-Piagetian
theories assume that Piaget’s characterization of schemes and the ways in which experience
changes them are accurate. Likewise, they accept the notion of stages in cognitive
development.
One of the best known advocates of neo-Piagetian theory was the late Robbie Case
(1945-2000) explained cognitive development in terms of operational efficiency, the
maximum number of schemes that a child can hold in WM at one time. He argued that
operational efficiency improves with age as WM capacity and efficiency, which he called
short-term storage space (STSS), increase. Thus, according to Case, the 7-year-old is better
able than the 4-year-old to handle the processing demands of Piaget’s tasks because the STSS
of the older child possesses superior operational efficiency.
VYGOTSKY’S SOCIOCULTURAL THEORY
Russian educational theorist Lev Vygotsky (1907-1934) proposed a sociocultural theory of
cognitive development that emphasizes language, social interaction, and play rather than
cognitive structures. Vygotsky hypothesized that much of cognitive development results from
the child’s internalization of information that is acquired socially, primarily through the
medium of language. He argued that nature endows children with basic skills that include
perception, attention, and memory, and that these natural skills enable children to take learn
through language and social interaction.
According to Vygotsky, social environments provide children with scaffolding, a type
of instruction in which an adult adjusts the amount of guidance provided to match a child’s
present level of ability. Consequently, through scaffolding, children are able to experience
higher levels of cognitive functioning than they are able to reach on their own. Scaffolding
experiences provide children with models that they internalize as goals. Motivated by these
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internalized goals, children respond with increasing levels of independence as adults
gradually withdraw scaffolding.
Vygotsky argued that scaffolding is likely to be an effective means of facilitating
cognitive development only if it is applied to tasks that are within a child’s zone of proximal
development, the developmental boundaries within which a child is capable of functioning
independently. For example, scaffolding will not help a young child learn to do high school
geometry problems. However, it will help a child who knows how to do two-digit addition
problems with regrouping learn to do three-digit problems.
Like Piaget, Vygotsky proposed that development occurs in stages. He proposed four
stages of sociocultural cognitive development:
Primitive stage: The infant possesses mental processes similar to those of animals
Naïve psychology stage: The child learns to use language to communicate but does
not understand symbols
Private speech stage: The child uses language as a guide to solving problems
Ingrowth stage: Logical thinking results from internalization of speech acquired
from children and adults in a social world
EVALUATION OF COGNITIVE DEVELOPMENTAL THEORY
All of the developmental theories you have read about in this chapter have demonstrated
their heuristic value. They have been widely discussed and debated among developmentalists
and learning theories. In addition, with the exception of Vygotsky’s theory, much research
has been done both with the aim of supporting and refuting the theories. But how well does
each of the theories address the questions below?
Does it explain the basic facts of learning?
Does it generate testable predictions?
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Does it lead to practical strategies that are relevant to real-world learning and
instruction?
The pattern of age-related change in reason ability that Piaget discovered has helped
developmentalists, learning theorists, teachers, and parents alike better understand how
children perceive the world around them. Moreover, his explanation of the stages and the
factors that influence children’s progression through them provides answers as to why
reasoning skills are difficult to teach and, even when teaching succeeds, children rarely
transfer them from practiced problems to novel tasks. However, Piaget’s theoretical
construct of the stage itself has been criticized as difficult to test.
Many of Piaget’s critics argue that the information processing approach provides a
better explanation of his findings than his own theory does. Moreover, IP studies of agerelated change have helped learning theorists gain insight into aspects of cognitive
development that Piaget did not address. These include metacognition and the role of
expertise in cognitive functioning.
Vygotky’s sociocultural theory is intuitively appealing and, on first glance, seems to
explain a great deal about cognitive development. However, in contrast to both Piaget’s
theory and the IP approach, Vygotsky’s theory has produced few testable hypotheses. Critics
argue that the weakness of the theory lies in its vaguely defined constructs such as
scaffolding and the zone of proximal development.
With regard to instructional strategies, of the three approaches discussed in this
chapter, information processing theory appears to be superior to both Piaget’s and Vygotsky’s
theories. Research that employs IP theory’s model of working memory has led to a number of
instructional innovations. These studies have revealed the importance of automaticity, for
example, a feature of working memory efficiency that is highly responsive to instruction and
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opportunities for practice. Similarly, IP research has shown that expertise, which can also be
developed through instruction, is also critical to reading comprehension and text learning.
At the same time, research derived from Piaget’s theory has helped educators
appreciate the issue of developmental timing in curriculum decisions. In addition, it
illustrates the need for “translating,” in a sense, learning tasks into the “language” of a
child’s developmental stage. For example, when asking children to respond to “what if”
writing prompts, Piaget’s findings on the development of hypothetico-deductive thinking
suggest that it would probably best to provide children with alternative answers to the
question from which they can choose one to write about. Adolescents, with their emerging
formal operational schemes, are ready to tackle “what if” questions in open-ended format.
Thus, one important instructional application of Piaget’s theory is its inclusion in teacher
training programs. The theory may not give teachers specific strategies to use in the
classroom, but it helps them better understand students’ thinking and behavior.
Many educators have embraced Vygotsky’s theory and have developed a number of
teaching strategies that are based on its principles. However, it is critical to note that the
theory does not have a strong empirical base to draw upon when applying it to instruction.
That is, few of its assertions have been thoroughly tested, and some have been refuted. For
example, his theory predicts that students who work in groups will produce better solutions
to problems than students who work alone. Research shows that groups produce the same
solutions that their most advanced individual members produce when they work alone. Thus,
groups perform at the highest level of the most capable individual member.
SUMMARY
In this chapter you have read about three different approaches to cognitive development. All
three assume that developing individuals actively contribute to the learning process.
Moreover, proponents of these theories argue that change results from an interaction of
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nature and nurture, and that both quantitative and qualitative change occur as individuals
develop. They differ, however, in the mechanisms they propose to explain development.
Piaget hypothesized that changes in a cognitive structure, the scheme, account for cognitive
development. Information-processing theorists argue that the feature of the developing IP
system constrain development, while neo-Piagetians combine the approach of Piaget with
that of the IP theorists. In contrast to Piaget, IP theorists, and the neo-Piagetians, Vygotsky
argued that language and social interactions are more critical to cognitive development than
mental structures and processes are.
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KEY TERMS, CONCEPTS, AND PEOPLE
cognitive development
neurons
glial cells
growth spurts
pruning
cerebral cortex
myelination
hippocampus
association areas
reticular formation
lateralization
corpus callosum
spatial perception
Jean Piaget
Alfred Binet
sensorimotor stage
object permanence
semiotic function
preoperational stage
concrete operational stage
conservation
formal operational stage
hypothetico-deductive thinking
schemes
organization
adaptation
assimilation
accommodation
equilibration
expertise
metacognition
selective attention
cognitive monitoring
metamemory
production deficiencies
utilization deficiencies
neo-piagetian theories
Robbie Case
operational efficiency
short-term storage space (stss)
Lev Vygotsky
sociocultural theory
scaffolding
zone of proximal development
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5
SOCIAL COGNITIVE THEORY
LEARNING THROUGH IMITATION
LEARNING THROUGH MODELING
EVALUATION OF SOCIAL COGNITIVE THEORY
SUMMARY
KEY TERMS, CONCEPTS, AND PEOPLE
In pursuit of the most comprehensive theory of learning possible, social cognitive theory
(SCT) borrows ideas from most of the theories you have read about in previous chapters. It
focuses largely on learning through observation. In addition, SCT addresses the effects of the
social environment on learners’ internalized expectations regarding the likely outcomes of
behavioral options. Like information processing and cognitive developmental theorists, SCT
advocates, the best known of whom is Albert Bandura, assume that learners actively
participate in the learning process. However, SCT shares with behaviorist epistemology the
assumption that environmental factors more strongly influence learning than those within the
learner do and that learning primarily involves quantitative change. Nevertheless, Bandura
argues that the outcome of social cognitive learning is an internal set of expectations derived
from interactions with others (the social cognitive element in learning) that motivates
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learners to engage the environment in ways that either facilitate or impede learning.
Consequently, SCT is at least somewhat consistent with constructivist epistemology.
LEARNING THROUGH IMITATION
As we noted above, the best known proponent of social cognitive theory is Albert Bandura.
Early in his career, Bandura’s views were consistent with those of Skinner, whose operant
conditioning theory you should remember from Chapter 2. However, when Bandura became
interested in studying the acquisition of complex social behaviors, such as aggression, he
observed that a great deal of social behavior is learned through imitation rather than through
reinforcement. Consequently, he began to look beyond the conditioning theories in search of
an explanation of imitative learning.
Early Theories of Imitative Learning
Early explanations of imitative learning arose from general theories of learning. For
example, noting the existence of imitative learning in several animal species, some early
twentieth century biologists argued for the existence of a genetically programmed “instinct”
for imitative behavior. B. F. Skinner argued against the instinct explanation. According to his
view, individuals learn from models when the models reinforce them for imitative behavior.
In opposition to both the instinct and Skinnerian approaches, Piaget hypothesized that
imitative learning is a form of assimilation in that it provides developing individuals with
opportunities to practice existing schemes. In support of his hypothesis, Piaget cited research
showing that children readily imitate problem-solving strategies for which they have
developed appropriate schemes but do not imitate more advanced strategies.
Perhaps the most plausible explanation of imitative learning that was proposed prior
to the mid-twentieth century was that of Neal Miller and John Dollard (1941). They
hypothesized that imitative learning is a form of instrumental learning, a type of change that
organisms display in order to attain specific outcomes. For example, you have learned to pay
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your bills on time in order to attain a specific outcome, that of avoiding late fees. Thus, you
have acquired this habit through an instrumental learning process.
Miller and Dollard argued that the degree to which an individual is willing to match the
behavior of a model (i.e., imitate the model) is dependent on his expectation of
reinforcement. Therefore, they coined the term matched-dependent behavior to refer to
instrumental learning that is acquired through imitation. Miller and Dollard’s approach was an
advance over earlier theories, but, like them, it failed to explain imitative learning that
occurred in the absence of direct reinforcement or imitative learning that is performed long
after observation of a model occurs.
Bandura’s Approach to Learning through Imitation
One of the shortcomings that Bandura found in all of the theories of imitative learning he
examined was that they failed to take into account Edward Tolman’s discover of latent
learning, change that occurs in the absence of reinforcement but is demonstrated when
reinforcement is made available. In his classic studies of maze learning, Tolman showed that
laboratory rats learned to find their way around complex mazes even when they were not
reinforced for doing so. They demonstrated their learning by rapidly moving from the
beginning to the end of the maze as soon as he provided a bit of food at the end of the maze.
Thus, Bandura noted, lack of performance, demonstration of learned behavior, should not be
taken to mean that an organism has not learned a behavior. He argued that the
learning/performance distinction was especially relevant to learning through imitation
because an observer may learn a behavior by mentally imitating it and overtly perform it long
afterward, if at all.
In addition to distinguishing between learning and performance, Bandura argued that a
comprehensive explanation of learning should differentiate between the direct and indirect
effects of the environment on learning. He used the term enactive learning to refer to
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change that is acquired through direct action of the environment on the learner. He adopted
the term vicarious learning to refer to change that is acquired through the observation of the
action of the environment on a model.
Bandura also noted that the major theories of the day tended to take an “either-or”
approach to internal and external influences on imitative learning. Piaget and the biologists
focused on internal factors—schemes and instincts, respectively—to the exclusion of external
variables. In contrast, Skinner, as well as Miller and Dollard, emphasized external agents—
models as sources of reinforcement—at the expense of internal factors. Consequently,
Bandura sought to develop a theory that integrated both internal and external variables.
Bandura used the term reciprocal determinism to refer to his model of change,
because it included internal factors, external factors, and interactions among them (See
Figure 5.1). His model suggests that three sets of interacting factors influence both learning
and performance. Personal factors include internal variables such as temperament, prior
learning, developmental stage, and so on. Situational factors are characteristics of the
context in which learning and performance occur such as whether one is being watched and
the presence of incentives to learn and/or perform behavior. Behavioral factors are the
behaviors that a learner performs.
Figure 5.1
Bandura’s Reciprocal Determinism
Personal Factors
Behavioral Factors
Situational Factors
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Introduction to Learning Theories 95
LEARNING THROUGH MODELING
Based on the theoretical rationale outlined above, Bandura developed and carried out
research supporting a comprehensive theory of modeling, a collective term that encompasses
the entire process of learning from a model. When Bandura first began to publish his ideas
and research findings, his theory was known as social learning theory (e.g., Bandura &
Walters, 1963). However, as Bandura and others developed the theory’s empirical foundation
across a wide range of studies, they discovered that the role of cognition was greater than
they originally assumed. Consequently, the theory ultimately became known as social
cognitive theory in the 1980s.
What and How We Learn from Models
Across numerous studies, Bandura and others found that, in general, modeling involves
four types of learning:
Observational learning: Acquisition of new behavior after observing a model
Facilitation: Performance of a previously learned socially acceptable behavior
after observing a model
Inhibition: Abstention from performance of a previously learned deviant behavior
after observing a model
Disinhibition: Performance of a previously learned deviant behavior after
observing a model
Each of these types of learning is caused by the development of an outcome expectancy, the
belief that one will be rewarded or punished for performing an observed behavior, in the
mind of the observer. When the observer expects to be rewarded for performing an observed
behavior, the chances are good that he will store the behavior and its associated outcome
expectancy in his long-term memory. He may or may not perform the learned behavior
immediately. In fact, he may never perform it at all. It is the outcome expectancy that is
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learned, not the behavior itself. An observer also stores outcome expectancies in long-term
memory when observation of a model leads him to believe that he will be punished for
performing an observed behavior. As a result, he may be less likely to perform the behavior
himself in the future.
Learning from models is far from an automatic process. It depends on a number of
factors. One is attention, that is, the observer must attend to the model’s behavior and to
the outcome that the model experiences in order to develop an outcome expectancy. The
observer’s memory abilities are also important, as he must retain the behavior and its
associated outcome expectancy in long-term memory. Similarly, the observer’s capacity for
production, the developmental ability to perform the observed behavior, influences the
degree to which he is likely to acquire an outcome expectancy for an observed behavior.
Finally, to perform the observed behavior himself, the observer must have motivation.
Influences on Modeling
There are several characteristics of models that influence whether an observer will
learn a modeled behavior from them. The status and competence of the model is one such
factor. Observers do not acquire outcome expectancies from low-status models whom they
deem to lack competence. Think about how teens decide what to wear. How often do they
follow their parents’ clothing choices, and how often do they follow those of their peers? In
their eyes, at least when it comes to fashion, peers have higher status and are more
competent.
The consequences that models experience also influence observers’ learning. For
example, some students highly value being listed on the school honor roll and work hard to
attain this goal. It might seem that their doing so would serve as a model for students who
are low-achievers. The presumed message is: “Work hard and you’ll get on the honor roll.”
However, low-achievers may not value the consequence of getting on the honor roll. As a
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result, seeing high-achievers’ names on the list will not cause them to develop the outcome
expectancy “If I work as hard as the kids on the honor roll do, I’ll get there, too.”
The honor roll example brings to light another issue in modeling. In order for an
observer to believe that he has a chance of attaining the same consequence as a model does,
he must also believe that he is capable of performing the behavior that yielded the
consequence as well as the model did. Bandura’s term for the belief that one is capable of
attaining desirable goals is self-efficacy. Thus, low self-efficacy for academic tasks is
probably another reason that low-achievers learn little from observing the behavior of highachievers. In contrast, the students who fall slightly short of making the honor roll are likely
to learn from the high-achievers’ example for two reasons. First, they value the consequence,
and, second, they have high levels of self-efficacy for academic work.
EVALUATION OF SOCIAL COGNITIVE THEORY
Given that social cognitive theory, like the theories you have read about in earlier chapters,
has proven to be of heuristic value, how does it respond to the remaining three questions that
address a theory’s usefulness?
Does it explain the basic facts of learning?
Does it generate testable predictions?
Does it lead to practical strategies that are relevant to real-world learning and
instruction?
Although the roots of social cognitive theory are in the behaviorist epistemological family,
Bandura’s thoughtful addition of cognitive components that have been demonstrated in
empirical research has given explanations of learning drawn from his theory far more
explanatory power than classical and operant conditioning have. Searches of research data
bases using key terms such as “self-efficacy” and “modeling” readily demonstrate the
theory’s capacity for generating testable hypotheses. Moreover, these searches show that the
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theory has been applied in practical ways across a wide variety of learning situations. For
example, Bandura’s ideas are frequently found at the heart of studies that compare varying
approaches to changing people’s health-related behaviors such as compliance with medical
instructions. Similarly, social cognitive theory is the basis of hundreds, if not thousands, of
studies of motivation in students from preschool to graduate school. Finally, Bandura’s
studies of children’s responses to aggressive models have enlightened parents, educators, and
clinicians about the necessity of monitoring the kinds of models to which children are exposed
through entertainment media.
SUMMARY
In this chapter you have been introduced to Bandura’s social cognitive theory. Despite what
many believe, learning from models does not happen in every case. This is because of
characteristics of both observers and models. Moreover, certain conditions are necessary for
modeling to be an effective means of learning. The observer must attend to, retain, be
capable of producing, and be motivated to perform the modeled behavior.
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KEY TERMS, CONCEPTS, AND PEOPLE
social cognitive theory (SCT)
Albert Bandura
instrumental learning
Neal Miller
John Dollard
matched-dependent behavior
Edward Tolman
latent learning
performance
enactive learning
vicarious learning
reciprocal determinism
personal factors
situational factors
behavioral factors
modeling
observational learning
facilitation
inhibition
disinhibition
outcome expectancy
self-efficacy
© Denise R. Boyd, Ed.D.