Analogy-Based Learning

A
A Salience Theory of Learning
DUANE M. RUMBAUGH
1
, JAMES E. KING
2
, MICHAEL J.
BERAN
3
, DAVID A. WASHBURN
4
, KRISTY GOULD
5
1
Language Research Center, Georgia State University,
Atlanta, GA, USA
2
University of Arizona, Tucson, AZ, USA
3
Language Research Center, Georgia State University,
University Plaza, Atlanta, GA, USA
4
Department of Psychology, Georgia State University,
University Plaza, Atlanta, GA, USA
5
Luther College, Decorah, IA, USA
Synonyms
Comparative cognition; Emergent learning; Rational
behaviorism
Definition
The perspective that responses are elicited by stimuli to
which they have become associated or learned because
they are reinforced remains strongly entrenched in
psychological thought. Just what reinforcers are and
how they operate, perhaps as agents that bond responses
to stimuli, are unresolved issues. The most generally
accepted definition of a reinforcer is that it is an event
that increases the probability that a response will
reoccur if it is reinforced. But that definition is circular
and does not explain how reinforcement works. Here,
we outline a perspective on learning called Salience
Theory that offers a process by which learning occurs
across instances of stimulus pairings and the resultant
sharing of response-eliciting processes that occur.
Theoretical Background
Despite its popularity and robust history, this stimulus–
response–reinforcement formulation has inherent
weaknesses. How can radically new and creative behav-
iors suddenly occur in the absence of a training history
that included those behaviors? How does the fixedness
that inheres in response reinforcement give way to the
emergence of new behaviors and concepts, such as
complex musical compositions and groundbreaking
inventions?
The Salience Theory of Learning proposes a new
approach to account for the origins of novel, unex-
pected, and even intelligent behaviors and new abilities
(e.g., competence in speech comprehension). It
reformulates reinforcement and how it has its effects
upon learning and behavior in terms of its salience,
stimulus strength, and response-eliciting properties.
Indeed, it formulates the contributions, the impact
of all stimuli in the formation of our basic unit of
learning, amalgams, in precisely those same terms.
We know that what is trained via specific reinforce-
ment of specific responses does not necessarily
constrain what is learned (Rumbaugh and Washburn
2003). How can this be according to Reinforcement
Theory? What the subject learns might well be far
more complex and even qualitatively different from
the behavior that one specifically reinforced. For
instance, a rhesus monkey (Macaca mulatta) was
trained with reinforcement over the course of several
months and thousands of training trials to control
a joystick with its foot in a complex interactive com-
puter task. Use of a hand was precluded, hence never
trained. Only in a later test was the monkey given its
very first opportunity to use either its hand or foot to
do the task. Now, since all reinforced training had been
with its foot, use of its hand should have been at most
a remote probability. Yet, when given a choice, the
monkey promptly used its hand, scoring significantly
better than it had ever done with its foot.
Clearly, this finding is inconsistent with Reinforce-
ment Theory. That the monkey used its hand might be
said to reflect its massive and prior history in the use of
its hand for fine manipulations of objects and foods,
but that does not answer the stronger question of how
the monkey knew how to use its hand skillfully.
N. Seel (ed.), Encyclopedia of the Sciences of Learning, DOI 10.1007/978-1-4419-1428-6,
# Springer Science+Business Media, LLC 2012

The answer to this stronger, more pointed, question
is that the monkey somehow had learned about the task
and principles of performing with precision by
reinforced training with its foot. The effects of
reinforced training were not limited to controlled use
of the foot. The learning became more abstract and
served the skillful use of its hand when that became
an option in subsequent test.
Reinforcement Theory dates back more than
100 years to E. L. Thorndike and Alexander Bain.
Today, advocates of controlling and modifying behavior
testify to the apparent effectiveness of reinforcement and
its pragmatic effects. Although we do not deny the seem-
ingly special power of reinforcement in the acquisition
and control of behavior, we suggest that it has no special
power apart from its salience, its strength as a stimulus,
and its response-eliciting properties as it enters amalgam
formation with other contiguous stimuli that originate
either from the external environment or internally. It
thus stands to reason that it also will be the salience of
any given stimulus and the strength of its response-
eliciting properties that will determine its impact in
amalgam formation – but always relative to the salience
strengths and the response-eliciting properties of other
stimuli with which it might form other amalgams.
The Salience Theory of Learning proposes an
account of learning that does not have the extreme
fixedness that inheres in the stimulus–response–
reinforcement model, at least when the latter is taken
at its face value. Behavior is too variable, too clever, too
creative, and too versatile for it to be so constrained.
Tolman (1948) observed this fact and concluded, as
does Salience Theory, that expectancies and cognitions
about what-leads-to-what emerge from the integration
of past experiences including conditioning.
Stimulus–response and stimulus–stimulus associa-
tions are posited to have a basic role in our Salience
Theory of Learning and Behavior, but not as instanti-
ated by reinforcement as historically defined. Rather,
associations that are induced among reliably and con-
tiguously associated events are held to generate new
composites that we term amalgams – our basic units of
learning in Salience Theory. Amalgams are neither
habits nor bonds. Importantly, all of the contiguous
events that enter into the formation of a given stream of
amalgam formations are posited to share interactively
their saliencies and their response-eliciting properties.
Thus, amalgams are somewhat different from the
individual events that form them. Accordingly, amal-
gams might generate unique behavior, possibly apart
from any single event that enters in their formation.
Salience Theory (Rumbaugh et al. 2007) embraces
behavioral parameters from heritable and stereotyped
instincts through conditioning and on to the emer-
gence of highly complex behaviors that are adaptive.
It merits emphasis that complex behaviors can be so
novel, so complex, that their emergence through selec-
tive shaping and reinforcement is virtually impossible.
Those complex behaviors and skills are called emer-
gents, and they constitute a category of behavioral
adaptations distinctly separate from the well-known
respondent (i.e., Pavlovian) and operant dichotomy
proposed mid-twentieth century by B. F. Skinner.
Thus, Salience Theory proposes a trichotomy of
learned behaviors: Respondent conditioning, Operant
conditioning, and Emergents. Each of these categories
has its own distinctive protocols and defining attributes
(Rumbaugh et al. 2007).
Traditional learning theory has regarded both
respondent and operant conditioning as contingent
upon stimulus events that are in close temporal conti-
guity with the responses to be conditioned – the
unconditional stimulus in the case of respondent
conditioning and contingent reinforcement of the
response in the case of operant conditioning. Rather
than limiting emphasis to events that act solely upon
responses, Salience Theory views organisms as con-
stantly surveying their perceptual worlds as if foraging
for stimuli that are important or salient along with
other stimuli temporally or spatially contiguous with
those salient stimuli. Thus, organisms are able to garner
the resources needed to sustain life and learn adaptive
behavior, while minimizing risk and conserving energy.
Salient events, including those related to significant
others (e.g., mothers, family members and cohorts) in
a social group, provide the basis for observational
learning from birth through maturity and the trans-
mission of culture
Salience Theory illuminates both the antecedents
and the consequences of learned and emergent behav-
iors, as does Reinforcement Theory. The theory is
eclectic; it includes many components that are parts
of other theories. It does not reject any body of empir-
ical evidence and intends no derision of the giants of
our time (see Marx and Hillix 1987, for an overview of
our roots).
2 A A Salience Theory of Learning

On the Parsimony of Salience Theory
Amalgam formation is similar to the initial learning
stages of sensory preconditioning and classical condi-
tioning. However, psychologists dating back to Thorn-
dike have invoked a new process to explain instrumental
conditioning, namely reinforcement. So there is an
awkward discontinuity here going from the primitive
association of two contiguous stimuli to the more
complex operation of reinforcers.
There is an old and extensive literature on the
awkwardness of the reinforcement explanation, includ-
ing the problem of explaining how an event following
a response can affect its probability. Furthermore,
reinforcement implies an evolutionary discontinuity
in the learning process in which the primitive associa-
tion of two stimuli is amended by the conceptually
more complex operation of reinforcement.
The advantage of the saliency approach is that both
the evolutionary discontinuity and consequent lack of
parsimony of the reinforcement approach disappear.
There is just one fundamental process: amalgam
formation. Amalgam formation underlies sensory
preconditioning, classical conditioning, and most
importantly instrumental conditioning and the forma-
tion of emergents.
Amalgam formation at its most basic level occurs
when a highly salient stimulus (i.e., the unconditional
stimulus) and a less salient stimulus (i.e., the condi-
tional stimulus) come into either temporal or spatial
contiguity. The impact of the unconditional stimulus is
so strong and dominant in classical conditioning that
the conditional stimulus comes to serve as an approx-
imation of it. Thus, the conditional stimulus accrues
salience and response-eliciting properties approximat-
ing those of the unconditional stimulus. To a lesser yet
measurable degree, the conditional stimulus shares its
salience and response-eliciting attributes with the
unconditional stimulus. In operant conditioning, the
reward is the most salient stimulus event. Response-
produced stimuli produced by the correct response
form an amalgam with the reward produced stimuli.
The already existing high salience of the reward-related
stimuli then accrues to the response-associated stimuli,
thereby increasing the likelihood of the response. Thus,
the subject learns how in a given situation it can obtain
the resources for which it forages while minimizing risk
and injury. As amalgams are incorporated into higher-
level templates, emergent behaviors become possible.
Although great gaps of knowledge need to be filled
by neuroscience and continuing behavioral research,
Salience Theory advances the consilience of psychol-
ogy, biology, and neuroscience (Naour et al. 2009;
Rumbaugh et al. 2007).
Instincts, Respondents, Operants, and
Emergents
We assume that organisms attend most closely to the
most salient events in their perceived worlds and thus
garner vital resources and minimize risk. To avoid
circularity insofar as possible, we describe the major
sources of natural and acquired facets of saliences.
Briefly, they are as follows:
Genes – sign stimuli; releasers (e.g., the pecking of
the red dot on mother’s beak by gull chicks to induce
her to regurgitate food)
Stimulus intensity – pressure, pain, sharp roar,
bright lights, strength of sign stimuli
Past associations – conditioning; sensory
preconditioning; classical and operant conditioning;
conditioned reinforcers (Skinner 1938); and secondary
reinforcers (Hull 1943)
Principles of perceptual organization – (e.g.,
closure, clustering of similar stimuli, induced motion,
uniqueness/novelty)
Amalgams are posited as the basic units of learning.
Metaphorically, they may be viewed as neural entries to
a never-ceasing sequence of events and stimuli. In
creating that record, salient events might serve as
commands to “make an entry.” The brains of all species
have become honed to make these entries in such a way
that the likelihood of adaptation and survival are
maximized.
Salience Theory views the brain as generating an
endless flow of amalgams that reflect experience as time
flows on; the brain also organizes the amalgams into
natural templates (e.g., readiness to learn different
things within a general category.) and/or acquired
characteristics (e.g., symbol-based, as with language,
traffic signs, and language itself).
Salience Theory posits that as the brain works to
resolve for the best fit among the amalgams and the
templates to which they are assigned that emergents
and even new skills might be given birth (Rumbaugh
et al. 2007; Savage-Rumbaugh et al. 1993; Tolman
1948). They, in turn, might enable the performance of
familiar tasks in more efficient ways and facilitate novel
A Salience Theory of Learning A 3
A

problem solving in an ever-changing environment.
Thus, the accumulation of experience can contribute
richly to the formation of a knowledge base.
By contrast, from the perspective of basic Rein-
forcement Theory, what is the etiology of new and
creative behaviors (i.e., emergents) that have no train-
ing history? Their etiology in Reinforcement Theory is
unlikely to involve a history of specific reinforcement
because all responses, either elementary or highly
complex and novel (e.g., a highly-complex emergent),
must occur at least once before reinforcement can affect
its reoccurrence. Salience Theory outlines how this
problem is obviated.
Important Scientific Research and
Open Questions
Might naturally salient stimuli not be blockable? Or
might they be pre-potent or dominant as elements of
compound CSs? Might they more readily enter into the
formation of new amalgams than do arbitrary stimuli?
What is the loading of naturally salient stimuli vs.
neutral stimuli in amalgam formation? How strong
must an arbitrary stimulus be to be equal in effect to
naturally salient stimuli?
Do the relative strengths of stimuli in sensory-
preconditioning alter how they interrelate?
Can symbols functionally substitute for physical
stimuli in compound CSs?
How does incorporation of a stimulus into one
kind of amalgam impact its availability and function
in other types of amalgams?
Does amalgam formation predict flash-bulb
memory instatement?
Does amalgam formation account for perceptual
discriminations as with new objects or in learning
visual discrimination?
How does Salience Theory align with neural
activity/recordings of areas of the brain in various
kinds of contexts?
Acknowledgments
Preparation of this article was supported by the
National Institute of Child Health and Human
Development grant HD-38051.
Cross-References
▶ Abstract Concept Learning in Animals
▶ Analogical Reasoning in Animals
▶ Animal Learning and Intelligence
▶ Comparative Psychology and Ethology
▶ Intelligent Communication in Social Animals
▶ Language Acquisition and Development
▶ Learning and Numerical Skills in Animals
▶ Linguistic and Cognitive Capacities of Apes
▶ Reinforcement Learning
▶ Theory of Mind in Animals
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Hull, C. L. (1943). Principles of behavior. New York: Appleton.
Marx, M. A., & Hillix, W. A. (1987). Systems and theories in psychology
(p. 312 ff.). New York: McGraw-Hill.
Naour, P., Wilson, E. O., & Skinner, B. F. (2009). A dialogue between
sociobiology and radical behaviorism. New York: Springer.
Rumbaugh, D. M., & Washburn, D. A. (2003). Intelligence of apes and
other rational beings. New Haven: Yale University Press.
Rumbaugh, D. M., King, J. E., Washburn, D. A., Beran, M. J., &
Gould, K. L. (2007). A Salience theory of learning and behavior -
with perspectives on neurobiology and cognition. International
Journal of Primatology, 28, 973–996.
Savage-Rumbaugh, E. S., Murphy, J., Sevcik, R. A., Brakke, K. E.,
Williams, S., & Rumbaugh, D. M. (1993). Language comprehen-
sion in ape and child. Monographs of the Society for Research in
Child Development, 58(3–4), 1–242. Serial No. 233.
Skinner, B. F. (1938). The behavior of organisms: An experimental
analysis. New York: Appleton-Century.
Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological
Review, 55, 189–208.
A Stability Bias in Human
Memory
NATE KORNELL
Department of Psychology, Williams College,
Williamstown, MA, USA
Definition
Human memory is anything but stable: We constantly
add knowledge to our memories as we learn and lose
access to knowledge as we forget. Yet people often make
judgments and predictions about their memories that do
not reflect this instability. The term stability bias refers to
the human tendency to act as though one’s memory
will remain stable in the future. For example, people fail
to predict that they will learn from future study oppor-
tunities; they also fail to predict that they will forget in
the future with the passage of time. The stability bias
4 A A Stability Bias in Human Memory

appears to be rooted in a failure to appreciate external
influences on memory, coupled with a lack of sensitiv-
ity to how the conditions present during learning will
differ from the conditions present during a test.
All memories are not created equal. Some memories
feel strong, vivid, and familiar; others feel shakier and
less reliable. People are generally confident in the first
type of memory but unsure about the second. Behavior
reflects this difference; for example, most people only
volunteer to answer a question in class if they feel
confident about their response.
The term metacognition refers to the process of
making judgments about one’s cognition and,
frequently, about one’s memory (Dunlosky and Bjork
2008). Metacognitive processes are used to distinguish
accurate memories from inaccurate ones. A memory is
only valuable to the degree that we can trust it, which
makes metacognition vital in our day-to-day use of
memory. Moreover, virtually all memory retrievals are
associated with a feeling of certainty (or lack thereof).
Thus, metacognition is a critical, and omnipresent,
component of human memory.
Metacognitive judgments are often accurate. For
example, your memory of what you ate for breakfast
today is probably more accurate than your memory of
what you ate for breakfast on this date 11 years ago, and
it probably feels more accurate as well. It would be
natural to assume that metacognitive judgments are
made on the basis of the memory being judged – that
is, that when confidence is low, it is because a memory is
weak. The empirical evidence suggests otherwise.
Instead of being made based on memories
themselves, metacognitive judgments appear to be
made based on inferences about those memories. For
example, if an answer comes to mind quickly and easily,
people tend to judge that they know that answer well.
This inference is usually correct. But it is an inference
all the same, and when conditions are created that
reverse this relationship – when answers that come to
mind quickly are less memorable – people give high
judgment of learning ratings to information that comes
to mind quickly, not to information that is highly
memorable (Benjamin et al. 1998).
If metacognitive judgments are inferential, what is
the basis of the inferences? Koriat (1997) put forward
a highly influential framework that has successfully
accounted for a great deal of subsequent data. He
proposed that three categories of cues influence
metacognitive judgments. Intrinsic cues were defined
as information intrinsic to the information being
judged (e.g., the semantic relatedness of a question
and its answer). Mnemonic cues were defined as infor-
mation related to the learner’s experience (e.g., the
fluency with which an answer comes to mind). Extrin-
sic cues were defined as information extrinsic to the
learner and the to-be-learned material (e.g., the
number of times an item was studied).
A second key distinction, related to Koriat’s (1997)
framework, is between judgments based on direct expe-
rience and judgments based on analytical processes
(Kelly and Jacoby 1996). Intrinsic cues and mnemonic
cues tend to elicit experience-based judgments. That is,
these cues (e.g., how easily one thinks of an answer) are
part of the learner’s experience at the time of the
judgment. Metacognitive judgments are usually highly
sensitive to a person’s current experience. Thus, expe-
rience-based judgments often occur automatically.
Extrinsic cues, by contrast, tend to elicit more
analytical belief-based judgments. For example, the
number of times an item will be studied is not
a salient part of the learner’s experience while studying.
Instead, responding to an extrinsic cue often requires
applying one’s beliefs about memory (e.g., I will do
better on items I study more). Doing so does not tend
to happen automatically. As a result, people regularly
fail to make belief-based judgments, even when they
should. Thus, people tend to be sensitive to experience-
based cues but not belief-based cues.
It is important to be able to predict how future
events will affect one’s memory. For example, a student
may need to predict the value of spending the rest of the
day studying. Future events are extrinsic cues – they are
external to the learner’s current experience – and, as
such, they require belief-based judgments. Thus,
people should exhibit a stability bias: They should be
relatively insensitive to the impact of future events on
their memories.
Open Questions
Koriat et al. (2004) investigated how sensitive people
are to future forgetting. After studying a list of word
pairs, their participants were asked to predict their
likelihood of recalling the pairs on a cued-recall test
A Stability Bias in Human Memory A 5
A

(i.e., their ability to recall the second word in the pair
when shown the first word). There were three groups of
participants, who were told, respectively, that their test
would take place immediately, a day later, or a week
later.
Actual recall performance dropped off precipi-
tously as the delay between study and test increased.
Shockingly, predictions hardly changed at all. In other
words, the participants demonstrated a stability bias:
They acted as though they would remember just as
much in a week as they would remember immediately.
The predictions were highly sensitive to the degree of
association between the pairs, which is an experience-
based, intrinsic cue. But they were insensitive to reten-
tion interval, an extrinsic cue. In one extreme case, tests
that would take place immediately and in one year
elicited the same predictions.
A key change in the procedure greatly altered
participants’ predictions. When a single participant
was told about all three retention intervals, their
predictions became sensitive to retention interval. It
appeared as though the participants believed that they
would forget over time, but that they did not apply that
belief in the first experiment. When they were told
about all of the retention intervals, they began to
apply belief-based judgments. Phrasing the question
in terms of forgetting had a similar effect: Apparently,
making the idea of forgetting salient was enough to
make judgments sensitive to retention interval.
One potential implication of ignoring retention
interval is extreme overconfidence. People tend to be
overconfident in their memories in general. But when
someone is overconfident about an immediate test,
and is not sensitive to retention interval, their
overconfidence is destined to grow. For example,
assume you have a 70% chance of recalling a fact
from your textbook if you are tested in 10 min. If you
are tested in a week, that chance might decrease to 20%.
If you judge that you have an 80% chance of recalling
the fact at either retention interval, you will be
overconfident immediately, but only by 10% points.
A week later, you will be overconfident by 60% points.
This increase in overconfidence with time has been
referred to as long-term overconfidence (Kornell 2010).
The stability bias is not limited to forgetting.
Kornell and Bjork (2009) investigated predictions
about another seemingly obvious principle of memory,
namely, that people learn by studying. Their
participants were told that they would be allowed to
study a list of word pairs between one and four times.
They were asked to predict how they would do when
they took a test on the pairs. The predictions were
almost entirely insensitive to the number of study rep-
etitions, again demonstrating a stability bias. The sta-
bility bias did not go both ways; people recognized the
value of past studying, but underestimated the value of
future studying. Like with forgetting, when the concept
of learning was made salient, in a within-participants
design, the predictions became more sensitive. Unlike
forgetting, however, the predictions continued to
underestimate the value of studying. As a result, across
a number of different experiments, participants were
overconfident in their current knowledge, but simulta-
neously underconfident in their learning ability.
One potential implication of undervaluing future
study opportunities is that people might underestimate
their own learning potential. For example, a student
might look at a set of challenging course materials and
decide to drop out of a class, assuming that he or she
cannot learn all of the material. If this student is
underconfident in his or her learning, he or she might
be giving up in the face of a challenge that could be
overcome.
Cross-References
▶ Confidence Judgments in Learning
▶ Cued Recall
▶ Metacognition and Learning
▶ Metacognitive Learning: The Effect of Item-Specific
Experiences
▶ Overconfidence
▶ Self-confidence and Learning
▶ The Role of Stability in the Dynamics of Learning,
Memorizing, and Forgetting
References
Benjamin, A. S., Bjork, R. A., & Schwartz, B. L. (1998). The
mismeasure of memory: When retrieval fluency is misleading
as a metamnemonic index. Journal of Experimental Psychology:
General, 127, 55–68.
Dunlosky, J., & Bjork, R. A. (Eds.). (2008). A handbook of
metamemory and memory. Hillsdale: Psychology Press.
Kelly, C. M., & Jacoby, L. L. (1996). Adult egocentrism: Subjective
experience versus analytic bases for judgment. Journal of Memory
and Language, 35, 157–175.
Koriat, A. (1997). Monitoring one’s own knowledge during study:
A cue-utilization approach to judgments of learning. Journal of
Experimental Psychology: General, 126, 349–370.
6 A A Stability Bias in Human Memory

Koriat, A., Bjork, R. A., Sheffer, L., & Bar, S. K. (2004). Predicting
one’s own forgetting: The role of experience-based and theory-
based processes. Journal of Experimental Psychology: General, 133,
643–656.
Kornell, N., & Bjork, R. A. (2009). A stability bias in human memory:
Overestimating remembering and underestimating learning.
Journal of Experimental Psychology: General, 138, 449–468.
Kornell, N. (2010). Failing to predict future changes in memory: A
stability bias yields long-term overconfidence. In A. S. Benjamin
(Ed.), Successful Remembering and Successful Forgetting: A Fest-
schrift in Honor of Robert A. Bjork (pp. 365–386). New York, NY:
Psychology Press.
A Tripartite Learning
Conceptualization of
Psychotherapy
DOUGLAS J. SCATURO
Syracuse VA Medical Center, State University of New
York Upstate Medical University, Syracuse University,
Syracuse, NY, USA
Synonyms
Behavior therapy; Cognitive-behavioral therapy;
Counseling; Psychoanalysis; Psychological treatment
Definition
Psychotherapy is a complex interpersonal interaction,
relationship, and method of treatment between a
licensed mental health professional (most often a psy-
chiatrist, psychologist, or social worker) and a patient
aimed at understanding and healing the patient’s
emotional distress and suffering, most often evident
by symptoms of anxiety or depression. Psychotherapy
predicated upon learning theory assumes that
a patient’s maladaptive coping behaviors that have
been unsuccessfully invoked by the patient to deal
with his or her distress are learned behaviors that can,
therefore, be unlearned through psychological treat-
ment. The Tripartite Learning Conceptualization of
Psychotherapy holds that there are multiple forms of
learning involved in both the learning and unlearning
of behavioral problems. In essence, there are three
principal learning processes that are involved in
psychotherapy: (1) learning to build and maintain
a therapeutic alliance between the therapist and
patient, (2) learning the use of a number of empirically
tested techniques that have been found helpful to alle-
viate emotional distress, and (3) the gradual relearning
of more adaptive behavioral responses to cope with life
stressors. Each of these three processes takes place
within one of three designated learning domains: the
affective, cognitive, or behavioral learning domains.
Psychotherapy and Learning
Processes
For over a half century, psychotherapy has been
conceptualized as a learning process (e.g., Dollard and
Miller 1950). Given this premise, it is expectable that
multiple forms of learning are involved in this process.
Mowrer (1947) was one of the first theorists to recog-
nize the presence of multiple forms of learning in the
acquisition and maintenance of “neurotic” (i.e., anx-
ious) behavior. According to his “two-factor learning
theory,” neurotic anxiety, or fear of a harmless situa-
tion, is acquired and maintained via a two-step learn-
ing process. The two-factor learning theory of anxiety
encompassed a combination of associative learning
processes via classical conditioning and instrumental
learning via operant conditioning. Mowrer noted the
importance of associative learning in the initial acqui-
sition of a fear response to a previously unfeared or
nonthreatening stimulus. In addition, he cited instru-
mental learning in which the organism then learns to
avoid the feared stimulus as critical in maintaining the
fear response. The learned avoidance of the anxiety-
provoking stimulus prevents the reduction of the
acquired fear through subsequent experiences with the
stimulus that would result in alternative, less fearful
consequences. Because the avoidance deprives the
patient of an opportunity to relearn alternative
responses to the stimulus, acquired neurotic anxiety is
resistant to extinction or change. In a similar manner,
multiple forms of learning are involved in providing
corrective experiences that take place in psychotherapy,
using principles of learning to understand both the
therapeutic alliance and the technical aspects of therapy.
Contributions from Educational
Psychology: Learning in Three
Domains
In educational psychology, it has long been recognized
that there is more than one type of learning. Bloom and
A Tripartite Learning Conceptualization of Psychotherapy A 7
A

his colleagues (e.g., Bloom et al. 1956) identified three
domains of learning: cognitive (intellectual), affective
(emotional), and psychomotor (behavioral). Cognitive
learning involves the acquisition of factual knowledge
and the development of intellectual skills, abilities, and
thought processes. Affective learning involves the ways
in which people emotionally process information and
stimuli. Emotional learning and development are
essential to the construction of the learner’s feelings,
values, and motives, and are at the foundation of one’s
receptivity to information. Finally, psychomotor learn-
ing involves behavior and activity connected with one’s
perceptual responses to inputs, to the activity of imita-
tion (modeling, vicarious learning), and to the manip-
ulation of one’s environment (instrumental learning).
Therapeutic Learning in Three
Processes
The Tripartite Learning Model of Psychotherapy
(Scaturo 2005, 2010) proposes that there are three
broad learning processes in therapy that correspond
to the three learning domains noted above. The thera-
pist’s skill in facilitating these three types of learning in
patients is basic to the process of psychotherapy, as well
as to the therapist’s acquisition of the technical skills
necessary for becoming an effective psychotherapist of
any given theoretical persuasion. These three treatment
processes have been designated as alliance building and
maintenance, technical interventions, and relearning.
They are incorporated into the three learning domains
as illustrated in Fig. 1. The factors relevant to the thera-
peutic alliance as described in a variety of theoretical
approaches to psychotherapy are clustered together in
Fig. 1a. Tacit emotional learning is at the core of the
therapeutic alliance. The patient learns to associate
expectancies for behavior change with aspects of safety
and viability (i.e., hope) in the therapeutic context. In
Fig. 1b, a number of well-documented cognitive-
behavioral therapy (CBT) technical interventions are
noted. These comprise the more proactive and directive
interventions on the part of the therapist. Technical
interventions tend to be targeted toward anxiety disor-
ders and depressive disorders, as the two fundamental
emotional states for which the instructional aspects of
CBT have had demonstrated effectiveness. These inter-
ventions are designed primarily to engage cognitive
learning processes: the patient receives therapeutic
directives and homework (instructional learning),
observes the therapist modeling and rehearsing behav-
ioral alternatives (vicarious learning), and learns the
needed coping behaviors and social/interpersonal
skills. Finally, in Fig. 1c, newly relearned behaviors
in therapy are ultimately performed by the patient
in his or her everyday life. The more adaptive life
consequences experienced through these alternative
ways of coping with anxiety and depression bring
about eventual self-generating reinforcements (instru-
mental learning, operant behavior).
Within the affective domain of learning, the general
process of building and maintaining a constructive
therapeutic alliance with the psychotherapist is pri-
mary with emotional learning as the key. The concept
of the therapeutic alliance has been denoted in the
psychotherapy literature with a wide variety of terms
from a broad range of theoretical perspectives. These
terms include, but are not limited to Freud’s concept of
the transference relationship; the holding environment;
the notion of emotional containment; the corrective
emotional experience; the facilitative conditions of
genuineness, empathy, and positive regard; nonspecific
or common factors in psychotherapy; the therapeutic
alliance; empirically supported therapy relationships;
the concept of remoralization; patient readiness; moti-
vational enhancement and preparation for therapy;
and joining with the family system. Within the cogni-
tive domain of learning, a variety of psychotherapeutic
technical interventions have been subsumed. These
include a wide variety of cognitive-behavioral tech-
niques for the treatment of anxiety and depression,
modeling behaviors by the psychotherapist,
psychoeducational interventions; the concept of reme-
diation, empirically supported psychological treatment
programs; and family enactment in family therapy
sessions. Finally, within the behavioral domain
of learning, the concept of relearning more adaptive
behaviors in response to life stressors has been denoted
as critical in psychotherapy. This relearning by the
psychotherapy patient has been signified by the terms
counterconditioning, extinction, and reacquisition of
behaviors; rehabilitation; self-efficacy; the upward
spiral; family restructuring; and generalization
enhancement of newly learned behaviors over time
and over a variety of problem situations. The interested
reader is referred to Scaturo (2005, 2010) for a detailed
discussion of these terms and their theoretical
foundations.
8 A A Tripartite Learning Conceptualization of Psychotherapy

In sum, the understanding of psychotherapy here
describes three important human processes that
comprise this form of treatment: (1) building and
maintaining the psychotherapeutic relationship and
alliance mediated by affective learning; (2) intervening
with empirically tested techniques such as verbal
instructions, recommendations, and homework
assignments/directives that provide cognitive learning
to the patient about his or her difficulties; and
(3) assisting the patient in making proactive behavioral
changes in his or her life resulting in an instrumental
relearning about the patient’s problem areas in his
or her life.
Open Questions
Empirical support for the present three-part learning
conceptualization of psychotherapy processes can be
found in Howard et al.’s (1993) three-phase model of
psychotherapy outcome. Their model postulated three
phases of treatment outcome involving the concepts of
“remoralization,” “remediation,” and “rehabilitation”
I. Alliance building/maintenance:
The affective domain
Corrective Emotional
Experience
Transference Relationship
Holding/Facilitating
Environment
Emotional Containment
Attachment/Secure Base
Genuineness, Empathy,
Positive Regard
Nonspecific factors
Common Factors
Therapeutic Alliance
Empirically-Supported
Therapy Relationships
Remoralization
Patient Readiness;
Stages of Change
Preparation Stage
Motivational Interviewing
Joining with the Family
Corrective Cognitive
Experience
Psychoeducational
Instructions
Psychological Modeling
Remediation
Cognitive-Behavioral
Techniques
Empirically-Supported
Psychological Treatments
Family Enactment
Corrective Behavioral
Experience
Extinction/Deconditioning
Reacquisition
Rehabilitation
Upward Spiral
Self-Efficacy
Learned Optimism vs.
Learned Helplessness
Empowerment
Family Restructuring
Generalization
Enhancement
II. Technical interventions:
The cognitive domain
III. Relearning:
The behavioral domain
A Tripartite Learning Conceptualization of Psychotherapy. Fig. 1 Tripartite learning conceptualization of
psychotherapy (Scaturo [2010], adapted and reprinted with permission of the Association for the Advancement of
Psychotherapy)
A Tripartite Learning Conceptualization of Psychotherapy A 9
A

that correspond conceptually to the three therapeutic
processes designated in the Tripartite Learning Model
of Psychotherapy. These authors studied a large sample
(N = 529) patients at the time of their initial intake
interview before beginning individual psychotherapy
at the Institute of Psychiatry at Northwestern
University’s Memorial Hospital. Self-report data of
the patients’ subjective well-being, symptomatic
distress, and life functioning was collected at the outset
of treatment and re-administered following Sessions 2,
4, and 17. Sophisticated statistical analyses revealed
that changes in subjective well-being, symptomatic
distress, and overall life functioning scores over this
period of time lent strong empirical support for the
three-phase model described by the authors.
In addition, Howard et al. (1993) research has
clearly demonstrated that these three phases of treat-
ment are “sequentially causally dependent”:
Remoralization ! Remediation ! Rehabilitation. To
the extent that the three components of the tripartite
learning model correspond conceptually to Howard
et al.’s three phases of psychotherapy, there is the impli-
cation that the components of the learning conceptu-
alization are not only “tripartite” but also “triphasic”:
Alliance Building/Maintenance ! Technical Interven-
tions ! Relearning (see Fig. 1). Thus, there is a notion
of progressive clinical improvement from one phase to
the next. That is to say, improvement in one phase
potentiates improvement in the subsequent phases.
The initial phases in psychotherapy appear to act as
prerequisites for effectiveness in the succeeding phases
of treatment. The tripartite learning explanation of
therapeutic process further expands on Howard’s
model of therapeutic outcome and incorporates the
notion of three learning domains – affective, cognitive,
and behavioral – that occur along with one another to
produce therapeutic change. Learning theory thus
provides a common theoretical bridge for the essential
therapeutic processes by which we may better under-
stand the treatment concepts of broad range of theo-
retical perspectives pertaining to psychotherapy.
Cross-References
▶ Associative Learning
▶ Behavior Modification as Learning
▶ Bloom’s Taxonomy of Learning Objectives
▶ Cognitive Learning
▶ Instrumental Learning
▶ Modeling and Simulation
▶ Operant Behavior
▶ Psychoanalytic Theory of Learning
▶ Psychodynamics of Learning
References
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl,
D. R. (Eds.). (1956). Taxonomy of educational objectives: The
classification of educational goals. Handbook I: Cognitive domain.
New York: David McKay.
Dollard, J., & Miller, N. (1950). Personality and psychotherapy: An
analysis in terms of learning, thinking, and culture. New York:
McGraw-Hill.
Howard, K. I., Lueger, R. J., Maling, M. S., & Martinovich, Z. (1993).
A phase model of psychotherapy outcome: Causal mediation of
change. Journal of Consulting and Clinical Psychology, 61, 678–685.
Mowrer, O. H. (1947). On the dual nature of learning –
a reinterpretation of “conditioning” and “problem-solving”.
Harvard Educational Review, 17, 102–148.
Scaturo, D. J. (2005). Clinical dilemmas in psychotherapy:
A transtheoretical approach to psychotherapy integration. Wash-
ington, DC: American Psychological Association.
Scaturo, D. J. (2010). A tripartite learning conceptualization of psy-
chotherapy: The therapeutic alliance, technical interventions,
and relearning. American Journal of Psychotherapy, 64(1), 1–27.
AAVL
▶ Effects of Anxiety on Affective Learning
Abduction
▶ Metapatterns for Research into Complex Systems of
Learning
Abductive Learning
BRIAN D. HAIG
Department of Psychology, University of Canterbury,
Christchurch, New Zealand
Synonyms
Explanatory inference; Inference to the best explana-
tion; Retroduction
10 A AAVL

Definition
The word abduction comes from the Latin word
abducere, which means “to lead away from.” It is some-
times used interchangeably with the word retroduction,
which comes from the Latin words retro, meaning
“backwards” and ducere meaning “to lead.” The term
abduction is most commonly used to describe forms of
reasoning that are concerned with the generation and
evaluation of explanatory hypotheses. Abductive
reasoning, then, is often portrayed as explanatory
reasoning that leads back from facts to a proposed
explanation of those facts. It is different from inductive
reasoning, which is commonly concerned with descrip-
tive inference that results in generalizations. The phrase
“abductive learning” can be taken to cover a wide range
of concerns where learning outcomes result from the
employment of abductive reasoning. Abductive learn-
ing occurs widely in scientific, professional, lay, and
educational endeavors.
Over 100-years ago, the philosopher-scientist, Charles
Sanders Peirce (Collected papers, 1931–1958), referred
to a form of reasoning that he called abduction. For
Peirce, abduction involved the generation of new
hypotheses that explained one or more facts. Peirce
(1958, pp. 5.188–5.189) took abduction to have
a definite logical form, which he represented in the
following argument schema:
The surprising fact, C, is observed.
But if A were true, C would be a matter of course.
Hence, there is reason to suspect that A is true.
In this schema, C can be a particular event or an
empirical generalization. A is to be understood as
an explanatory hypothesis or theory, and C follows,
not from A alone, but from A combined with relevant
background knowledge. Finally, A should not be taken
as true, but as plausible and worthy of further
investigation.
Abductive learning takes a variety of forms. One
common form is known as existential abduction, where
the explanatory hypothesis or theory postulates the
existence, but not the nature, of an entity thought
to explain the relevant fact(s). For example, one
might explain a number of symptoms of a common
cold in terms of a viral infection without being able to
say anything about the nature of the virus. In science,
the multivariate statistical method of exploratory
factor analysis is sometimes used to hypothesize the
existence of underlying causal factors thought to
explain correlated performance indicators. For exam-
ple, general intellectual ability is hypothesized to
explain correlated scores on subtests of an intelligence
test (Haig 2005). Exploratory factor analysis, then, is
a method of learning that facilitates the abductive
generation of elementary plausible hypotheses that
explain positive correlations of variables.
A second form of abductive learning is captured by
a strategy of analogical modeling which exploits a type
of abductive reasoning known as analogical abduction
(Abrantes 1999). The reasoning involved in analogical
abduction can be stated in the form of a general
argument schema as follows:
Hypothesis H
about property Q was correct in situa-
tion S1.
Situation S1 is like situation S2 in relevant respects.
Therefore, an analogue of H
might be appropriate in
situation S2.
This is a valuable reasoning strategy for learning
about the nature of hidden causal mechanisms that are
hypothesized or theorized in both science and everyday
life. It is also an important means for assessing the
worth of our expanded understanding of such mecha-
nisms. Expansion of our knowledge of the nature of
our theories’ causal mechanisms is achieved by
conceiving of these unknown mechanisms in terms of
what is already familiar and understood. With the
strategy of analogical modeling, one builds a model of
the unknown subject or causal mechanism based on
appropriate analogies derived from the known nature
and behavior of the source. Two examples of models
that have resulted from this strategy are the molecular
model of gases, based on an analogy with billiard balls
in a container, and the computational model of the
mind, based on an analogy with the computer. These
examples can be reconstructed to conform to the
argument schema for analogical abduction presented
above.
Another, and important, form of abductive learn-
ing is known as inference to the best explanation. Like
existential abduction, inference to the best explanation
justifies knowledge claims in terms of their explanatory
worth. However, unlike existential abduction, inference
to the best explanation involves accepting a hypothesis
Abductive Learning A 11
A

or theory when it is judged to provide a better expla-
nation of the evidence than its rivals do. Paul Thagard
(1992) has developed a detailed account of inference to
the best explanation known as the theory of explanatory
coherence. According to this theory, inference to the
best explanation is concerned with establishing
relations of explanatory coherence. The determination
of the explanatory coherence of a theory is made in
terms of three criteria: explanatory breadth, simplicity,
and analogy. The theory of explanatory coherence is
implemented in a computer program (ECHO), which
is connectionist in nature. Judgments of explanatory
coherence are employed widely in human affairs. For
example, Charles Darwin argued for the superiority of
his theory of evolution by natural selection on the
grounds that it provided a more coherent explanation
of the relevant facts than the creationist alternative of
his time. And in courts of law, jury decisions are
significantly governed by consideration of the compar-
ative explanatory coherence of cases made by defending
and prosecuting lawyers.
Open Questions
Learning through abductive reasoning is as pervasive as
it is important for generating, expanding, and justify-
ing many of our knowledge claims. And yet, abductive
reasoning, and the learning on which it depends, is not
widely known. There is a major role for science educa-
tion to promote and provide an understanding of how
we learn through abduction. Researchers in science
education are beginning to study abduction in different
learning contexts, but there is much more to be done.
The processes of abductive learning through use of
different research methods needs further investigation.
The abductive methods of exploratory factor analysis
and the theory of explanatory coherence were briefly
considered above, as was the strategy of analogical
modeling that employs analogical abduction. However,
there are other research methods that involve abductive
reasoning in ways that have not been fully articulated.
The well-known qualitative method of grounded
theory is a prominent case in point.
Finally, abduction is an important human ability.
It seems to be complicit in perception and emotion, as
well as cognition (Magnani 2010). Just how these
spheres of human functioning exploit abductive
processes deserves further intensive investigation.
Additionally, the suggestive hypotheses about the
history and biological origins of abductive reasoning
are in need of further research. For example, did the
powers of human abductive reasoning have their ori-
gins in the tracking behavior of hunter-gatherers, and is
abductive reasoning an evolved adaptation (Carruthers
2002)? The answer to these and related questions are
yet to be properly given.
Cross-References
▶ Abductive Reasoning
▶ Bayesian Learning
▶ Creative Inquiry
▶ Explanation-Based Learning
References
Abrantes, P. (1999). Analogical reasoning and modeling in the
sciences. Foundations of Science, 4, 237–270.
Carruthers, P. (2002). The roots of scientific reasoning: Infancy,
modularity and the art of tracking. In P. Carruthers, S. Stich,
M. Siegal (Eds.), The cognitive basis of science (pp. 73–95).
Cambridge: Cambridge University Press.
Haig, B. D. (2005). Exploratoy factor analysis, theory generation, and
scientific method. Multivariate Behavioral Research, 40, 303–329.
Magnani, L. (2010). Abductive cognition: The epistemological and
eco-cognitive dimensions of hypothetical reasoning. New York:
Springer.
Peirce, C. S. (1931–1958). In C. Hartshorne, P. Weiss, A. Burks
(Eds.), Collected papers (vols. 1–8). Cambridge, MA: Harvard
University Press.
Thagard, P. (1992). Conceptual revolutions. Princeton: Princeton
University Press.
Abductive Reasoning
ROGER W. SCHVANEVELDT
Applied Psychology, Arizona State University,
Mesa, AZ, USA
Synonyms
Hypothesis; Hypothesis generation; Inference to the
best explanation; Retroduction
Definition
Abductive reasoning consists in applying norms under-
lying the generation of hypotheses.
12 A Abductive Reasoning

Logic and reasoning are usually thought of in the realm
of deductive reasoning which is concerned with
preserving truth. A valid deductive argument is one
for which true premises guarantee a true conclusion.
Aristotle’s syllogisms are familiar examples of such
arguments. All A are B and C is an A lead to the
conclusion that C is a B. All men are mortal and
Socrates is a man requires that Socrates is mortal.
The hypothetico-deductive method provides
a means of analyzing scientific reasoning. Given
a hypothesis, predictions can be deduced from the
hypothesis which is then tested by scientific experi-
ments. However, as Karl Popper argued, the conse-
quences of testing a hypothesis are quite different in
the case of finding confirming as opposed to
disconfirming evidence. Given a hypothesis (H) and
a prediction (P), the underlying logic can be character-
ized as follows:
Confirmation Disconfirmation
Argument Comment Argument Comment
If H then P H implies P If H then P H implies P
P The
prediction
occurs
Not P The prediction
does not occur
Therefore ? H is
confirmed,
not proven
Therefore
not H
H is certainly
false
So disconfirmation conclusively establishes that
a hypothesis is false (by the deductively valid modus
tollens argument form) whereas confirmation does not
prove the truth of the hypothesis (concluding that H is
true given that P is true is known as the logical fallacy,
affirming the consequent). Going beyond deductive
logic, we might say that confirmation provides support
of a hypothesis, perhaps increasing our confidence in
the hypothesis, but it does not prove the hypothesis.
Inductive logic is involved in coming to accept hypoth-
eses, but such logic does not involve absolute proof. We
could turn to probability theory, including Bayesian
theory, to try to quantify the notion of confidence
with inductive logic, but we cannot achieve the
certainty associated with deductive logic.
This brief account of the hypothetico-deductive
method starts with a hypothesis to initiate the work
of the method. As C. S. Peirce noted in the nineteenth
century (see Peirce 1940), a complete account of the
method should also include an analysis of the origin of
hypotheses, and he argued that a logic underlying
hypothesis generation could and should be developed
in addition to the extensive work on deductive and
inductive logics. He proposed hypothesis and
retroduction as names for this logic, but he later settled
on abduction or abductive logic as parallel with deduc-
tive and inductive logic.
Peirce proposed that hypotheses originate in
attempts to explain observed phenomena so the
process starts with observations (O) and generates
a hypothesis (H) such that “if H then O” which is to
say that the observations follow from the hypothesis.
We might call this the primary constraint for
a hypothesis, the observations must follow from it.
We might say that at this stage, a hypothesis is plausi-
ble, certainly not proven. A mundane example might
be of assistance here. Say that you look out your
window and observe that the street is wet. It might
occur to you that it had rained. You have just generated
a hypothesis to explain your observation. Note that just
as with scientific hypotheses, the rain hypothesis is not
necessarily true. The street may have become wet by
some other means such as a street cleaning truck or
a lawn sprinkler that went awry. Still the rain hypoth-
esis is plausible, and it could be provisionally held and
tested further.
Figure 1 shows another example. Taking the figure
as observations to be explained, we can offer sugges-
tions about the figure such as: “olives on toothpicks”
or “onions on barbeque spits” or “balloons on strings.”
In other words, we are generating hypotheses to explain
the observations provided by the figure.
Abductive Reasoning. Fig. 1 What is this?
Abductive Reasoning A 13
A

Each of these suggestions has the form of a plausible
hypothesis in that if the suggestion were true, the figure
would follow. Consider still another plausible hypoth-
esis, that the figure represents a bear climbing the
other side of a tree. Most people find this hypothesis
preferable to the others advanced. Some of the possible
reasons for the preference are discussed later.
While abductive logic was originally proposed as an
aspect of scientific reasoning, such reasoning can be
seen in many human activities including perception,
language comprehension, creativity, and problem
solving. The advancement of an understanding of
abductive logic can potentially impact many of these
cognitive processes.
Open Questions
Many have dismissed inductive and abductive reason-
ing as logic because there is no guarantee of the truth
of the inferences as there is with deduction. Such
arguments usually boil down to the realization that
hypotheses (and theories) are underdetermined by
data. There are always alternative hypotheses that
account for any set of data so there is no compelling
reason to accept any one of the alternatives. Inductive
generalizations (e.g., “Swans are white”) may prove to
be false even after countless confirmations, and the best
we can claim for generated hypotheses is that they are
plausible which is far from a guarantee of their truth.
The issue here is really what we want the term logic to
mean. If we want logic to provide certainty, only
deductive logic counts. Peirce thought that logic
referred to correct thinking which may not always
insure truth. Other writers such as Hanson (1958),
Harman (1965), and Simon (1973) agree with Peirce,
that there is a logic to discovery. Simon points out that
we call a process logical when it satisfies norms we have
established for it. On this view, the study of the logic of
discovery involves identifying such norms. In
a previous paper, we examined abductive reasoning in
some detail (Schvaneveldt and Cohen 2010). The
following factors are among those identified.
● The Observations. This amounts to the constraint
that the observations follow from the hypothesis
(If H then O) or (H implies O).
● Economics. Hypotheses that can easily be put to the
test should have some priority. This is one of the
criteria suggested by Peirce as he developed his
thinking about abductive reasoning.
● Parsimony. Simpler hypotheses that fit the observa-
tions are preferred over more complex ones (also
known as Occam’s razor).
● Aesthetics. Considerations of beauty, elegance, sym-
metry, and attractiveness figure into entertaining
a hypothesis.
● Plausibility and Internal Consistency. Hypotheses
consistent with each other and with background
knowledge are preferred over ones that lead to
contradictions.
● Explanatory Power (Consilience). Consilience
includes how much a hypothesis covers, how fruit-
ful a hypothesis is in suggesting interpretations of
observations, and the connections a hypothesis
establishes between various observations. The bear
climbing the tree hypothesis illustrates the ideas
behind consilience. The bear hypothesis provides
an explanation of the entire figure including an
account of all of the lines and circles and why they
are arranged in just the way they are. There are no
coincidental details left over as there are with the
other suggestions offered for interpreting the figure.
● Pragmatics. Goals and the context of situations
influence the hypotheses generated.
● Analogy. Analogy consists of sets of relations found
in a source domain that can be applied to a target
domain. Analogies can suggest additional relations
that might apply as well.
● Similarity and Association. Similarity is a weak
constraint on abductive reasoning, but similarity
of various kinds is often involved in suggesting
hypotheses. Some basis of drawing a connection
between observations and potential explanations
can often be traced to a weak association of
elements of the observation and a hypothesis or to
common connections to intermediate elements
from both observations and a hypothesis. In
a study of insight in problem solving, Durso et al.
(1994) found that critical associative connections
underlying the solution of the problem often
appeared before the problem was solved suggesting
that arriving at a solution may be mediated by
establishing critical connections.
Researchers in the field of artificial intelligence have
developed and applied many systems to implement
14 A Abductive Reasoning

abductive reasoning in such areas as medical diagnosis,
troubleshooting, problem solving, and language
comprehension. These are still active areas of research
with many different approaches to developing rigorous
models of abductive logic.
Cross-References
▶ Abductive Learning
▶ Adaptive Memory and Learning
▶ Analogical Reasoning
▶ Creative Inquiry
▶ Creativity, Problem Solving and Feeling
▶ Creativity, Problem Solving, and Learning
▶ Deductive Reasoning and Learning
▶ Discovery Learning
▶ Inductive Reasoning
▶ Insight Learning and Shaping
▶ Logical Reasoning and Learning
▶ Problem Solving
References
Durso, F. T., Rea, C. B., Dayton, T. (1994). Graph-theoretic confirma-
tionofrestructuringduringinsight.Psychological Science, 5, 94–98.
Hanson, N. R. (1958). Patterns of discovery: An inquiry into the
conceptual foundations of science. Cambridge: Cambridge
University Press.
Harman, G. H. (1965). The inference to the best explanation. The
Philosophical Review, 74, 88–95.
Peirce, C. S. (1940). Abduction and induction. In J. Buchler (Ed.),
Philosophical writings of Peirce. New York: Routledge.
Schvaneveldt, R. W., Cohen, T. A. (2010). Abductive reasoning and
similarity: Some computational tools. In D. Ifenthaler, P. Pirnay-
Dummer, N. M. Seel (Eds.), Computer based diagnostics and
systematic analysis of knowledge. New York: Springer.
Simon, H. A. (1973). Does scientific discovery have a logic? Philoso-
phy of Science, 40, 471–480.
Abilities and Learning: Physical
Abilities
BENJAMIN K. BARTON, THOMAS A. ULRICH
Department of Psychology Communication Studies,
University of Idaho, Moscow, ID, USA
Synonyms
Fine motor skills; Gross motors; Motor skills; Reflexes
Definition
Capacity to engage in reflexive or voluntary goal-
directed physical behavior.
Physical abilities serve an integral role for learning
during early childhood. The type of learning the child
engages in is directly related to the physical abilities
that the child is able to draw upon while interacting
with the world. With increased physical abilities, more
complex learning occurs.
During early childhood, children learn how to interact
with the environment through physical experiences.
This process is largely constrained by developing phys-
ical abilities that the child possesses during the different
stages of development. As children gain physical abili-
ties the variety and complexity of interactions
increases, which results in more complex forms of
learning.
According to Piaget’s cognitive-development the-
ory, as children age, they build increasing complex
schemes or associations between motor activity and
the resulting physical experiences (Piaget 1936, 1963).
Schemes are developed through direct physical inter-
action with the environment. The sensorimotor stage is
the primary period in which children learn the neces-
sary motor movements that allow them to interact with
the environment. The sensorimotor stage occurs dur-
ing the time from birth to approximately 2 years of age.
Another theoretical perspective that highlights the
connection between physical abilities and learning is
Gibsonian affordance theory (Gibson 1977). In this
perspective, objects in the world have physical charac-
teristics that provide intuitive clues as to the manner in
which a person may interact with the object. The per-
son requires little to no sensory processing to decipher
the potential use of the object as a direct result of its
physical composition. Infants are receptive to these
affordances within the environment similar to adults.
For example, a toy hanging above a child suggests it
may be struck to swing back and forth. In learning,
affordances serve a vital role by providing clues for
potential physical interactions that elicit experiences
the infant can build schemes from.
Young children are receptive to affordances, yet they
may not have the physical capability to act on the
provided affordances. In line with Piagetian theory,
Abilities and Learning: Physical Abilities A 15
A

the physical capability to move one’s body and limbs
serves a crucial function for learning during the senso-
rimotor stage. There are several substages within the
sensorimotor stage that are distinct from one another
based on the interactions between physical and cogni-
tive capabilities the child is able to draw upon to learn
about the world.
A newborn child has very little experience
interacting with the world. The primary mechanism
for learning entails building new schemes through
physical interaction in which the child’s own motor
activity generates novel experiences (Piaget 1936,
1963). In reaction to these novel experiences, the
child attempts to repeat the motor activity that gener-
ated the initial experience in what Piaget termed
a circular reaction. The reactions are circular because
the child repeatedly attempts to elicit the same experi-
ence through motor activity. These physical interac-
tions with the world are the fundamental components
of learning during this stage of development. As the
physical capabilities of the child increase, more con-
trolled motor activities can be performed to generate
more experiences and lead to learning experiences.
Open Questions
In line with Piategian theory, during the early stages of
infancy learning primarily revolves around building
simple sensorimotor schemes. These simple sensori-
motor schemes are comprised of action patterns for
simple motor movements such as grasping. Grasping
serves as a physical ability developmental milestone
required for learning rudimentary environmental
principles. For example, grasping allows children to
learn the physical properties of objects such as the
gravitational force exerted on a ball after released
from the child’s grasp. As the grasping physical ability
becomes more refined, the child can learn to interact in
complex ways with the environment. The child can
now learn certain properties of specific objects, such
as learning to roll a ball possessing the spherical shape
affordance which suggests it can be rolled.
Following the grasping ability, the physical ability
to crawl is a developmental milestone that allows chil-
dren to learn more complex ways of interacting with
the environment. The ability to crawl requires coordi-
nation of multiple schemes to generate the correct
motor movements and a very basic ability to balance
the body over the limbs. The ability to crawl assists the
child in developing depth perception as a result of
the optical flow experienced while moving throughout
the environment. The environment must contain
affordances to allow for depth perception develop-
ment, such as physical objects that provide optical
flow which are also within a close proximity to where
the child is crawling. For example, if the child were
crawling in a large room devoid of physical objects or
markings on the floor or walls, there is nothing to serve
as a point of reference which hinders the development
of depth perception.
The physical ability of walking is a major develop-
mental achievement that expands the learning oppor-
tunities for children. Learning to walk requires
environmental affordances of open space with vertical
handholds that the child can grasp to lift the body
upright into a walking position. Furthermore, walking
requires a multitude of coordinated schemes with a
more advanced ability to balance than what is required
for crawling. Upon learning to walk, the child can
engage in a wide variety of exploratory learning. Not
only has the horizontal plane in which the child can
navigate significantly increased, but the vertical plane is
expanded since the child can now reach higher when in
an upright standing position. The freedom enjoyed by
a walking child leads to safety concerns for parents. The
areas in which the child explores now overlap with
areas that previously only adults could access. This
can create potentially dangerous situations because
now the walking child can access areas that may contain
dangerous objects such as cleaning chemicals, electrical
appliances, and sharp knives and tools.
The ability to walk marks the tail end of the primary
sensorimotor scheme development stage, which occurs
at approximately 2 years of age. Throughout this early
developmental period, the physical abilities of children
serve as the primary component in the learning that
occurs. During this time, children learn about their
motor capabilities and the way these capabilities affect
their surrounding environment. After mastering the
physical ability of walking, the sensorimotor scheme
building shifts from gross motor movements into a new
developmental stage in which sophisticated motor
schemes begin to develop that allow children to engage
in highly coordinated and complex activities. In this
later stage, children begin to learn the necessary motor
schemes required for athletic activities, such as kicking
16 A Abilities and Learning: Physical Abilities

or throwing a ball, climbing, and running. Addition-
ally, fine motor schemes begin to develop, which allows
the child to engage in activities that require a high
degree of dexterity, such as drawing and writing.
Cross-References
▶ Affordances
▶ Piaget’s Learning Theory
▶ Play, Exploration, and Learning
▶ Visual Perception Learning
References
Gibson, J. J. (1977). The theory of affordances. In R. Shaw J.
Bransford (Eds.), Perceiving, acting and knowing. Hillsdale, NJ:
Erlbaum.
Piaget, J. (1936, 1963) The origins of intelligence in children. New York:
W. W. Norton Company, Inc.
Abilities to Learn: Cognitive
Abilities
PETER ROBINSON
Department of English, Aoyama Gakuin University,
Tokyo, Japan
Synonyms
Aptitudes; Cognitive processes; Individual differences;
Intellect; Traits
Definition
Cognitive abilities are aspects of mental functioning,
such as memorizing and remembering; inhibiting and
focusing attention; speed of information processing;
and spatial and causal reasoning. Individual differences
between people are measured by comparing scores on
tests of these mental abilities. Tests of general intelli-
gence, such as the Wechsler Adult Intelligence Test, are
based on a broad sample of these mental ability tests,
and measures of aptitudes for learning in specific
instructional domains, such as mathematics, or
language learning, are based on a narrower sampling
of the domain-relevant abilities.
Theoretical and empirical research into the structure of
memory by Hermann Ebbinghaus (1850–1909) and
the functions of attention by William James (1842–
1910) provided the foundations for the development
of operational tests of cognitive abilities at the begin-
ning of the twentieth century. The observation of
a “positive manifold,” or consistent positive correla-
tions across multiple tests of abilities led Charles Spear-
man (1863–1945) to propose that a single general
intelligence factor, termed “g,” underlay performance
on each of them. From this early work the field of
differential psychology began, which examined the
extent to which measured differences in abilities corre-
lated with performance on tests of academic achieve-
ment, or lifetime success in an intellectual or
performative domain. Based on these studies, and
confirming Spearman’s proposal, a large-scale factor-
analytic survey of results has shown relationships
among cognitive abilities to be hierarchically related
at three levels or strata of increasing generality, with
measures of separate cognitive abilities occupying the
lowest, least general stratum, and “g” representing the
single uppermost factor (Carroll 1993). Results of
many studies have shown that “g” and higher intelli-
gence quotient (IQ) – a score obtained from perfor-
mance on various tests of intelligence – reliably
predicts greater academic and lifetime success.
The measurement of cognitive abilities is only one
facet of research in differential psychology concerned
with identifying correlates of academic learning and
lifetime success. Two other facets are the assessment
of individual differences in affect, such as emotion and
anxiety, and conation, such as self-regulation and moti-
vation. It is widely acknowledged that academic
achievement is the result of a complex interplay
between cognition, affect, and conation. But in one
sense cognitive abilities are clearly different from affec-
tive and conative factors, since the growth and decline
of memory, attentional, reasoning, and other cognitive
abilities show clear inverted U-shaped developmental
trajectories across the life span, in contrast to affect and
conation.
For example, it is well known that memory abilities
follow such a trajectory. In early childhood children not
only lack the ability to explicitly remember and recall
prior events (long-term memory), but also to maintain
memory for a current event while performing
a simultaneous operation on the remembered informa-
tion. The latter ability, termed working memory, has
been shown to develop and increase in capacity
Abilities to Learn: Cognitive Abilities A 17
A

throughout childhood and into adolescence, when it
plateaus, and then to decline in aged populations. In
a similar manner, other cognitive abilities, such as
reasoning, processing speed, and spatial memory,
have been shown to increase throughout childhood, to
plateau in adulthood, and to decline during aging
(Salthouse 2010).
Open Questions
The extent of individual differences between children
and adults in cognitive abilities is a major area of
research in developmental psychology. This research
aims to chart the time-course of the emergence and
consolidation of cognitive abilities over the lifetime. It
also aims to identify individuals at the low and high
tail-ends of measures of abilities in these populations
who consequently have what are judged to be marked
deficits and talents in each ability domain.
Related to this, the extent to which individual
differences in cognitive abilities influence learning in
schooled and unschooled settings, for any population
of learners, is a major area of research in educational
psychology. There is evidence that cognitive abilities do
not contribute equally to success in all areas of learning,
and a major area of research is to identify what these
ability-learning domain correlations are.
For example, in general, schooled academic
achievement in domains such as mathematics increases
during childhood in proportion to the measured
increase in working memory capacity that children
have at different ages (Dehn 2008). On the other
hand, the precocious ability to learn a first or other
language during infancy and early childhood (when
compared to the relative failure of adults to learn
languages) has been attributed to their lack of such
well-developed working memory capacity and explicit
memory ability. This has been termed the Less-is-More
Hypothesis for child language learning. Lacking explicit
working memory ability, infants are only able to
remember immediately contingent sequences of
sounds. This associative learning, drawing on uncon-
scious implicit memory, has been argued to be the
essential foundation for statistical processes of lan-
guage acquisition. With the later emerged development
of more reflective, conscious explicit and episodic
memory, and greater control of attention allocation
and reasoning ability (and the metacognitive awareness
this gives rise to) associative implicit language learning
is disrupted.
Such a developmental account of the growth of
cognitive abilities, and the learning processes they facil-
itate and inhibit, fits well with evidence for the Critical
Period Hypothesis (CPH) for language learning. The
CPH claims that if languages are learned after the age
of 6 years, then native-like levels of ability in them are
unattainable. In other domains, however, such as the
acquisition of literacy and mathematics, where explicit
learning and memory are essential, then growth in
explicit memory and reasoning abilities “across”
populations of different ages, and for individuals with
relatively greater strengths in these abilities “within”
any age-matched population, lead to higher levels of
academic achievement.
Other areas of research and theory that are impor-
tant concern the influence of cognitive disabilities on
schooled learning. For example, throughout childhood
the ability to focus attention, voluntarily, on an event
or object increases, as does the ability to inhibit atten-
tion to irrelevant events, noises, and other distractor
stimuli. However, for some children these attention
focusing and inhibiting abilities do not develop, with
the consequence that the resulting Attention Disorder
Hyperactivity Deficit (ADHD) has negative effects on
academic progress. Those at risk of ADHD can be
diagnosed during early childhood, using tests of the
cognitive abilities to control attention and to inhibit
attention to distractions. The extent to which such
deficits in the ability to control attention are remedia-
ble is an important area of research. Interestingly, it has
been found that bilingual children, who have the daily
experience of switching between two languages during
speaking and listening, show particularly good perfor-
mance on tests of cognitive control of attention, when
compared to monolingual counterparts. Therefore,
experience plays a role in training the ability to focus,
and switch attention between stimuli, though the
relative contributions of experience and genetics to
such abilities is not yet clearly known.
An area of much recent research and theory
concerns the cognitive ability to successfully attribute
intentions and beliefs to others that cause them to
perform actions. This form of intentional reasoning
emerges at around the age of 3 years during childhood,
when children develop what is called a “Theory of
Mind.” Before this age children consistently fail
18 A Abilities to Learn: Cognitive Abilities

a variety of false-belief tasks. For example, a researcher
shows a child a packet of Smarties (a well-known con-
tainer for sweets) then opens it to reveal it is empty. The
researcher then asks the child what a second child will
think is inside the container if he/she shows it to them.
The child invariably replies “nothing” showing that
they cannot distinguish their own current mental
understanding from that of a second child. With devel-
opment, children begin to successfully distinguish their
own understanding of the world from that of others.
However, this ability does not develop in all children,
with the consequence that they may be diagnosed as
“autistic.” Autism, and lack of theory-of-mind ability,
has been shown to affect first language development,
and also social interaction and schooled learning, in
negative ways. For example, lacking a theory of mind,
autistic children do not understand the conceptual
meaning of different psychological verbs used to refer
to others’ mental states, such as “he/she wonders/
believes.” These verbs are accompanied by complex
subordination in all languages, e.g., “he wonders
whether (subordinate clause)”; “she believes that
(subordinate clause).” Consequently, autistic children
do not use syntactic subordination in their first
language as much as non-autistic children, and their
development of complex syntax is negatively affected
by comparison with non-autistic peers. As with ADHD,
the remediability of this cognitive disability, and the
relative contributions of genetics and experience to it,
continue to be researched.
Two summary points need to be made in conclu-
sion, regarding the issues raised above, and the rela-
tionship between cognitive abilities and instructed
learning. Firstly, cognitive abilities do not facilitate
learning independently of the conditions under which
the material to be learned is presented in instructional
contexts. Learning conditions, for example, can predis-
pose learners to learn incidentally (unintentionally and
on many occasions implicitly, without awareness) or
explicitly (with intention and awareness). In the
domain of instructed second language acquisition
(SLA), for example, different combinations of cogni-
tive abilities, or aptitude-complexes, have been shown to
facilitate incidental learning (unintentionally acquiring
knowledge of the second language) versus explicit
learning (intentionally understanding pedagogic expla-
nations of language) (Robinson 2005). Many details of
the optimum levels of cognitive abilities within such
complexes remain to be explored for incidental versus
explicit second language learning, and for learning of
other domains that provide for incidental exposure to
content, versus explicit instruction about content. This
is a fundamental area of current research into cognitive
abilities with applications to learning and instruction.
The term “aptitude complex” was coined by Rich-
ard E. Snow (1936–1997), and Snow’s lifetime of work
points to a final summary implication of research into
cognitive abilities for learning. Snow argued through-
out his career (e.g., Snow 1994) that aptitudes for
learning from instruction are many and varied, but
not infinite. On the one hand, Snow argued – in the
way described above – that cognitive abilities differen-
tially facilitate learning under some, versus other
conditions of instructional exposure. But he further
argued that cognitive abilities only contribute to apti-
tudes for learning in combination with other affective
and conative coordinates of learning processes. Much
current theory and research, across domains of
language, science, mathematics, and other areas of
education, is concerned with identifying what these
multidimensional cognitive-affective-conative com-
plexes are, and the extent to which they contribute to
success during instructed learning (Shavelson and
Roeser 2002). If they can be theorized, and measured,
then learners with strengths in one or another aptitude
complex can be matched to instructional interventions
and learning conditions that draw optimally on them.
This research can be expected to continue, and should
contribute much to our increased knowledge of the role
of cognitive abilities in learning.
Cross-References
▶ Aptitude-Treatment Interaction
▶ Attention Deficit and Hyperactivity Disorder
▶ Implicit Learning
▶ Intelligence and Learning
▶ Language Acquisition and Development
▶ Motivation and Learning: Modern Theories
▶ Statistical Learning and Induction
▶ Working Memory
References
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-
analytic studies. New York: Cambridge University Press.
Dehn, M. J. (2008). Working memory and academic learning: Assess-
ment and intervention. Hoboken: Wiley.
Abilities to Learn: Cognitive Abilities A 19
A

Robinson, P. (2005). Aptitude and second language acquisition.
Annual Review of Applied Linguistics, 25, 46–73.
Salthouse, T. A. (2010). Major issues in cognitive aging. New York:
Oxford University Press.
Shavelson, R. J., Roeser, R. W. (Eds.) (2002). Educational assess-
ment. Special issue: A multidimensional approach to achievement
validation. (Vol. 8, No. 2, pp. 77–205). Mahwah: Lawrence Erlbaum
Associates, Inc.
Snow, R. E. (1994). Abilities in academic tasks. In R. J. Sternberg
R. K. Wagner (Eds.), Mind in context: Interactionist perspectives
on human intelligence (pp. 3–37). New York: Cambridge Univer-
sity Press.
Ability Determinants of
Complex Skill Acquisition
MATTHEW J. SCHUELKE, ERIC ANTHONY DAY
Department of Psychology, University of Oklahoma,
Norman, OK, USA
Synonyms
Aptitudes and human performance; Cognitive abilities
and skill; Individual differences and learning; Intelli-
gence and skill; Performance gains; Performance
trajectories; Skill development; Skill growth; Skill
improvement
Definition
▶ Skill is the level of proficiency on specific tasks. It is
the learned capability of an individual to achieve
desired performance outcomes (Fleishman 1972).
Thus, skills can be improved via practice and
instruction.
Although skills differ in many ways, important
distinctions can be made in terms of complexity. Task
complexity is described via differences in component,
coordinative, and dynamic complexity (Wood 1986).
Component complexity concerns the number of distinct
acts and processing of distinct information cues
involved in the creation of task products. Much of the
empirical literature on complex skill acquisition
involves tasks comprised of both cognitive and percep-
tual-motor components. Coordinative complexity
concerns how different acts, information cues, and
task products are interrelated. Dynamic complexity
concerns how acts, information cues, and task products
or their relationships change across time. Dynamic
complexity can also be thought of as the degree of
inconsistency in information-processing demands.
Thus, a ▶ Complex action learning can be defined in
terms of the proficiency required for task performance
that contains an amalgamation of strong component,
coordinative, and dynamic complexity.
▶ Acquisition is a process, internal to an individual,
which produces a relatively permanent change in
a learner’s capabilities. Acquisition is distinct from the
execution of skill in that acquisition is observed
through increases of successive performances during
practice and instruction or training. Skill acquisition
typically requires adaptive interaction with the learning
environment to detect information and to respond in
a correct and timely manner. The process of acquisition
produces behavior that is less vulnerable to transitory
factors such as fatigue or anxiety (Davids et al. 2008).
Skill acquisition is studied by examining performance
changes over time and practice. Skill acquisition
research is longitudinal by nature, involving repeated
measures of performance over a large number of trials
or training sessions.
▶ Ability refers to a general trait, reflecting the
relatively enduring capacity to learn tasks. Although
fairly stable, ability may change over time primarily in
childhood and adolescence through the contributions
of genetic and developmental factors (Fleishman
1972). In the psychological literature, abilities have
been grouped in many different ways. Early taxonomies
of human ability were concerned with those abilities
utilized during motor-skill performance. For example,
Fleishman’s early research on the ability requirements
approach differentiated between 11 perceptual-motor
and 9 physical-proficiency abilities. Subsequent
research distinguished between more than 50 abilities
underlying human learning and performance. Many of
these abilities have been categorized as cognitive in
nature. Other abilities have been categorized as more
physical, psychomotor, or sensory-based.
Although many taxonomies of human ability exist,
two common ability specifications include general
mental ability and broad-content abilities (Ackerman
1988). ▶ General mental ability (also commonly
referred to as general cognitive ability, general intelli-
gence, or g) is defined as the factor common to tests
of cognitive ability and is theorized to be the ability
to efficiently acquire, process, and use information
(also commonly referred to as fluid intelligence).
20 A Ability Determinants of Complex Skill Acquisition

Broad-content abilities describe a class of abilities which
pertain to the general content of a given task. For
instance, a task primarily composed of oral or written
components might require the broad-content ability
termed verbal ability. Two other broad-content abilities
in skill acquisition research are numerical and spatial
ability. Additionally, perceptual-speed and psychomo-
tor ability are frequently investigated in studies of
skill acquisition (Ackerman 1988). ▶ Perceptual-speed
ability refers to speed of processing information. Psy-
chomotor ability refers to the speed and accuracy of
motor responding. Regardless of the type or taxonomy,
greater ability generally leads to faster acquisition of
skill and higher levels of performance.
In 1926, Snoddy proposed the power law of practice
which predicts a linear relationship between the loga-
rithmic functions of practice amount and perfor-
mance. The theory predicts a quadratic or
decelerating trend such that gains in performance
slow over time. However, the theory was not widely
accepted due to observations of skips, jumps, and other
short-term observable phenomena in learning curves
presumably due to factors outside the theory’s consid-
eration (Davids et al. 2008). Although not stated at the
time, this criticism might be viewed as the earliest
recognition of individual differences affecting the
learning process.
Around this time, Fitts and Posner developed their
theory of motor learning. This three-stage model
describes gradual changes during a continuous learn-
ing progression. The first stage, named the cognitive
stage, is characterized by the learning of discrete pieces
of information, and performance is often variable and
error ridden. During the second, the associative stage,
the distinct knowledge gathered in the first stage is
assimilated, and performance is more consistent and
less error ridden. Both task complexity and learner
abilities contribute to varying lengths of time across
individuals in this stage. The third, the autonomous
stage, requires extensive practice to achieve and is char-
acterized by few errors and minimal mental effort
(Davids et al. 2008). These stages can also be thought
of in terms of novice, journeyman, and master stages of
skill acquisition.
Fleishman’s work became important because
he developed a taxonomy describing individual
differences in perceptual-motor performance when
learning theory lacked useful taxonomies. Using
a combination of experimental and correlational
approaches, he sought to link the concepts of aptitude
measurement, learning and training, and human task
performance. In general, Fleishman’s research showed
that (a) changes occur in the specific combinations of
abilities contributing to performance over the course of
skill acquisition, (b) such changes are progressive and
systematic and become stabilized, and (c) the impor-
tance of task-specific ability increases over the course of
skill acquisition.
With the advent of information-processing theories,
researchers started focusing on specific cognitive pro-
cesses associated with skill acquisition. For example,
such theories highlight how learners move from
cognitively intense closed-loop systems, where a learner
utilizes feedback to help direct current action, to less
cognitively demanding open-loop systems, where
a learner does not utilize feedback as much, when
forming a compiled schema, production, or sequence
of discrete actions to achieve a desired effect in context
(Davids et al. 2008).
Open Questions
A more recent cognitive theory, Ackerman’s dynamics
of ability determinants (Ackerman 1988) integrates the
results of previous work. Ackerman’s model includes
three stages of skill acquisition (namely, cognitive,
associative, and autonomous), but adds a component
showing how different abilities contribute to each of
the three stages. Various task-, person-, and situation-
related factors dictate the relative importance of
various abilities during each time point of acquisition,
but the factors of complexity and consistency are the
most prominent. Complexity, particularly component
and coordinative, primarily moderates the relationship
between cognitive ability and performance, whereas
inconsistency in information-processing demands
primarily moderates learning-stage progression.
For complex yet consistent tasks, Ackerman
suggests early skill acquisition will depend primarily
on cognitive abilities – general and broad-content –
because everything is new and learners must continu-
ally process new information. As a learner progresses to
later stages of skill acquisition, cognitive ability will
either remain or decrease in its contribution toward
Ability Determinants of Complex Skill Acquisition A 21
A

acquisition. For inconsistent tasks, cognitive ability
should continue to contribute because performers
must continually process inconsistencies. For consis-
tent tasks, learners get better at processing the consis-
tent information as acquisition progresses, and the
contribution of cognitive ability thus declines. Percep-
tual-speed ability is particularly important during the
middle of skill acquisition. As the production systems
generated in the first cognitive phase are fine-tuned in
the second associative phase, perceptual-speed ability
becomes important but less so once the task becomes
largely automatized in the final autonomous phase. If
a task is perceptual-motor in nature, psychomotor
ability should have a stronger role in the final stage of
skill acquisition. Production systems are largely autom-
atized at this stage, and therefore, it is psychomotor
ability that determines further skill acquisition
(Ackerman 1988).
Complex tasks require the creation of more pro-
duction systems which increase the contribution of
cognitive ability toward skill acquisition but attenuate
that of perceptual speed. This is because attention is
utilized for increased system production while percep-
tual speed is not as effective across many uncompiled
productions. Consistency moderates learning-stage
progression because without some consistency learning
is not possible. Therefore, inconsistency slows acquisi-
tion. For example, a learner may never progress beyond
the first stage of learning in an extremely inconsistent
task, suggesting that cognitive ability will strongly con-
tribute to performance no matter the degree of practice
(Ackerman 1988).
Because skill acquisition differs depending on task
complexity and consistency, high- and low-ability
learners might converge in performance over the
course of practice and instruction. The prediction of
decreasing interindividual variance in performance
across time is consistent with the lag hypothesis in that
slower learners lag behind faster learners but may catch
up given additional practice and instruction. That is,
high-ability learners display stronger linear and qua-
dratic relationships between practice and performance
(i.e., reach asymptote more quickly) than their lower-
ability counterparts. The opposite hypothesis involving
divergence, termed the deficit hypothesis, fan-spread
effect, or Matthew effect, describes increasing
interindividual variance. Put another way, the high-
ability learners display a smaller quadratic relationship
between ability and performance than their lower-
ability counterparts. Less complex and more consistent
tasks typically portray a lag pattern. More complex and
inconsistent tasks require more cognitive resources,
which may prevent some learners from ever
progressing beyond earlier stages of acquisition. Diver-
gence in performance is especially likely for tasks that
are largely dependent on declarative knowledge yet do
not involve a finite domain of knowledge than on tasks
which primarily require speed and accuracy of motor
responding (Ackerman 2007).
There is still much to study and clarify. Growth
curve modeling (e.g., hierarchical linear modeling, ran-
dom coefficients, or mixed effects) and spline models are
more sophisticated analytic procedures that overcome
limitations of previously used analyses which primarily
involved correlational and factor-analytic approaches.
Much of the past research utilized a time-slice approach
to examine the contributions of ability to acquisition
by only showing relationships between ability and
performance at discrete points in time. Contributions
to actual growth (i.e., improvements in skill) needed
to be inferred. The more sophisticated analyses allow
for growth to be modeled explicitly and allow for the
direct examination of ability contributions toward
growth.
As a current example of using a more sophisticated
analytic approach, Lang and Bliese (2009) used discon-
tinuous mixed-effects growth modeling and examined
the effects of general mental ability on two types of
adaptation or transfer: transition and reacquisition
adaptation. Transition adaptation refers to an immedi-
ate loss of performance following task changes, and
reacquisition adaptation refers to the rate of relearning
after task changes. Analyses indicated general mental
ability was positively related to transition adaptation
but showed no relationship between general mental
ability and reacquisition adaptation. In other words,
the findings showed higher general mental ability
corresponded to greater losses in performance during
a change period. These findings suggest the possibility
that high-ability learners either reach automaticity
faster, and therefore do not process task changes as
quickly, or simply learn more and therefore have
more to lose when a task changes. These findings con-
tradict commonly held beliefs that individuals with
high general mental ability are better able to adapt to
fundamental environmental changes. However, despite
22 A Ability Determinants of Complex Skill Acquisition

greater losses during transition and no advantage in
reacquisition, higher-ability learners continued to
outperform their lower-ability counterparts both dur-
ing and after task changes.
Research has addressed much in the development of
skilled performance. Changes in variance among indi-
viduals during skill acquisition can now be reliably
predicted. Both practice and abilities explain variance
in skill acquisition. Practice explains more variance
than abilities, but the effects of practice depend upon
the ability levels of learners. The role of abilities during
early skill acquisition is generally known. However,
there is still much to discern, particularly in terms of
relating abilities to later stages of skill acquisition and
skill adaptation. Additionally, prior knowledge (i.e.,
crystallized intelligence) and non-ability trait complexes,
which refer to a combination of interests, personality,
motivation, and self-concept traits, also appear impor-
tant, and should be investigated in future research
(Ackerman 2007).
Cross-References
▶ Abilities and Learning: Physical Abilities
▶ Abilities to Learn: Cognitive Abilities
▶ Complex Action Learning
▶ Evaluation of Student Progress in Learning
▶ Expertise
▶ Intelligence and Learning
▶ Longitudinal Learning Research
▶ Qualitative Learning Research
References
Ackerman, P. L. (1988). Determinants of individual differences
during skill acquisition: cognitive abilities and information
processing. Journal of Experimental Psychology: General, 117(3),
288–318.
Ackerman, P. L. (2007). New developments in understanding skilled
performance. Current Directions in Psychological Science, 16(5),
235–239.
Davids, K., Button, C., Bennett, S. (2008). Dynamics of skill acqui-
sition: A constraints led approach. Champaign: Human Kinetics.
Fleishman, E. A. (1972). On the relation between abilities, learning,
and human performance. The American Psychologist, 27(11),
1017–1032.
Lang, J. W. B., Bliese, P. D. (2009). General mental ability and two
types of adaptation to unforeseen change: applying discontinu-
ous growth models to the task-change paradigm. The Journal of
Applied Psychology, 94(2), 411–428.
Wood, R. E. (1986). Task complexity: definition of the construct.
Organizational Behavior and Human Decision Processes, 37(1),
60–82.
Ability Grouping
▶ Cooperative Learning Groups and Streaming
▶ Effects of Tracking and Ability Grouping on
Learning
Ability-Based
▶ Competency-Based Learning
Abnormal Aggression
▶ Psychopathology of Repeated (Animal) Aggression
Abnormal Avoidance Learning
HENRY W. CHASE
School of Psychology, University of Nottingham,
Nottinghamshire, Nottingham, UK
Synonyms
Avoidance behaviour; Individual differences;
Psychopathology
Definition
Avoidance of aversive events is of critical importance
for an organism’s chances of survival. Many organisms
are thought to experience fear in anticipation of an
aversive event, such as an electric shock, and tend to
show two types of associated behavior. First, they may
show “species-specific defensive responses” (SSDR):
innate defensive responses such as freezing. Second,
they may learn to perform particular actions to reduce
or abolish the likelihood of the shock, either cued
by a predictive conditioned stimulus (CS: see, e.g.,
Solomon and Wynne 1953) or performed without the
control of a CS (Sidman 1953). Acquisition of the
avoidance response is usually enhanced if it is compat-
ible with an SSDR.
Abnormal Avoidance Learning A 23
A

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