The Journal of General Psychology, 2005, 132(1), 5–40
The authors are listed in alphabetical order. They thank Adam Brown, David Geary,
Scott Herschberger, Christopher Hovarth, Andy Kukla, and Peter Richerson for their
helpful comments on earlier drafts of this manuscript.
Address correspondence to Dan Chiappe, Department of Psychology, California
State University, Long Beach, 1250 Bellflower Blvd., Long Beach, CA 90840-0901;
The Evolution of Domain-General
Intelligence and Learning
Department of Psychology
California State University, Long Beach
ABSTRACT. For both humans and animals, domain-general mechanisms are fallible
but powerful tools for attaining evolutionary goals (e.g., resources) in uncertain, novel
environments that were not recurrent features of the environment of evolutionary adapt-
edness. Domain-general mechanisms interact in complex ways with domain-specific,
information-encapsulated modules, most importantly by manipulating information
obtained from various modules in attempting to solve novel problems. Mechanisms of
general intelligence, particularly the executive functions of working memory, underlie
analogical reasoning as well as the decontextualization processes that are central to
human thought. Although there is a variety of evolved, special purpose learning
devices, learning is also characterized by domain-general mechanisms that are able to
achieve evolutionary goals by making novel and serendipitous associations with envi-
Key words: domain generality, intelligence, modularity, problem solving
EVOLUTIONARY PSYCHOLOGY IS RADICALLY AT ODDS with the tradi-
tion that domain-generality is an important component of human cognition. Evo-
lutionary psychologists propose the human mind consists predominantly of high-
ly specialized mechanisms designed to solve specific problems in the environment
of evolutionary adaptedness (EEA; Cosmides & Tooby, 1987; Palmer & Palmer,
2002; Pinker, 1994, 1997; Shettleworth, 2000; Sperber, 1994; Tooby & Cosmides,
1989, 1992). Though evolutionary psychologists acknowledge the existence of
domain-general mechanisms as a possibility, they have not provided analyses of
the evolutionary function of these mechanisms or of how they interface with
domain-specific mechanisms. Their view is that domain-general mechanisms are
inherently weak because “jacks-of-all-trades are masters of none. They achieve
generality only at the price of broad ineptitude” (Cosmides & Tooby, 2002, p.
170). On the contrary, we argue that mechanisms of general intelligence and
domain-general learning are powerful tools designed to solve problems not recur-
rent in the EEA.
A fundamental premise of evolutionary psychology is that evolutionary
adaptations equip animals to meet recurrent challenges of the physical, biolog-
ical, and social environment. When the environment presents longstanding prob-
lems and recurrent cues relevant to solving them, the optimal solution is to
evolve domain-specific mechanisms, or “modules,” specialized to handle spe-
cific inputs and generate particular solutions. Modules are designed to solve
problems in specific domains by mapping characteristic inputs onto character-
istic outputs (Fodor, 1983, 2000). Their operation is mandatory, fast, and uncon-
scious. They carry out their operations by consulting a proprietary database—
information about the domains to which they apply. Modules are also
information encapsulated. Though information relevant to solving a particular
problem may be accessible to other parts of the cognitive system, it is not nec-
essarily available to a module (Fodor, 1983).
The modular view is likely a correct account of how the mind responds to
recurrent, highly stable patterns of evolutionarily significant information (Geary &
Huffman, 2002). It is the optimal way of solving problems with a restricted prob-
lem space—a small range of possible solutions, such as the three-dimensional
structure of the physical world (Gallistel, 1990; Shepard, 1994). The stability of
the structure of physical space favors the evolution of highly modular systems sen-
sitive to the associated information (e.g., geometric relations among landmarks;
Gallistel) and highly constrained mechanisms for learning about variations in fea-
tures within this space (Geary & Huffman).
Nevertheless, we argue that domain-specific mechanisms are only part of
the story. From the perspective of modularity, it is difficult to see how humans
could solve novel problems or how they could solve recurrent problems in novel
ways—things people are capable of doing. The difficulty presented by novel
problems is that, by definition, there is no characteristic input–output relation
based on past recurrences that can solve them. We claim domain-general mech-
anisms are central to human and animal cognition in that they allow for the solu-
tion of nonrecurrent problems in attaining evolutionary goals. These are mech-
anisms captured by the g factor of intelligence tests and some learning
mechanisms. In the case of intelligence, they include the executive functions of
working memory, which are conscious, controlled, unencapsulated, and
The Journal of General Psychology
Motivation and the Frame Problem
A major goal of this article is to delineate a middle ground between the blank
slate perspective based only on domain-general mechanisms and the massively
modular view proposed by evolutionary psychologists as a necessary correction.
The blank slate perspective (the standard social sciences model) proposes
that the mind consists solely of a set of domain-general mechanisms. A basic
problem with this approach is that there are no problems that the system was
designed to solve. The system has no preset goals and no way to determine when
goals are achieved, an example of the frame problem discussed by cognitive sci-
entists (e.g., Dennett, 1987; Fodor, 1983; Gelman & Williams, 1998). This is the
problem of relevance—the problem of determining which problems are relevant
and what actions are relevant for solving them. An organism that is a blank slate
is unable to determine which of the infinite number of problems it must solve to
survive and reproduce. It faces a “combinatorial explosion” (Tooby & Cosmides,
1992, p. 102) of possible behaviors, because at any time it could do any of an
infinite number of things (Tooby & Cosmides). Without framing mechanisms
guiding it toward the solution of adaptive problems, a problem solver would “go
on forever making up solutions that have nothing to do with a nonassigned prob-
lem” (Gelman & Williams, p. 596).
Because of the frame problem, it is difficult to see how domain-general
processes could evolve without further constraints. Perceptual inputs are mas-
sively ambiguous, and domain-general systems have no problems that need solu-
tion and no criteria for when they are solved. Modular systems provide a built-in
sense of relevance: We pay more attention to moving objects than to stationary
objects and to faces than to feet. Men generally seek out young, beautiful women
rather than old women as objects of sexual desire. We do these things as a result
of our evolutionary history. On this view, such adaptations must by definition
respond only to recurrent features of the environment—the Stone Age mind
adapted to recurrent features of the Pleistocene.
On the basis of these considerations,we accept the argument that humans could
not have evolved as nothing but general-purpose problem solvers. We propose,how-
ever, that an important aspect of evolution has been to solve the frame problem in
a manner compatible with the evolution of domain-general mechanisms. A key idea
is that we have evolved motivational systems. These provide positively or negative-
ly valenced signals to the organism—signals of adaptive relevance that help to solve
the frame problem while allowing for the evolution of domain-general problem solv-
ing. Consider the state of hunger. A child confronted with an infinite number of
behavioral choices narrows down this infinite array by choosing behaviors likely to
satiate it, including ones that worked in the past. The motive of hunger, and the fact
that certain behaviors reliably result in satiating it,give structure to the child’s behav-
ior and effectively prevent combinatorial explosion. The child’s behavior is not ran-
dom because it is motivated by the desire to assuage the feeling of hunger.
Chiappe & MacDonald7
One can think of motivational mechanisms as a set of adaptive problems to
be solved but whose solution is massively underspecified. Motivational systems
like the child’s hunger enable the evolution of any cognitive mechanism, no mat-
ter how opportunistic, flexible, or domain-general, that is able to solve the prob-
lem. The child could solve its hunger problem by successfully getting the atten-
tion of the caregiver. It could solve it by stumbling onto a novel contingency or
by observing others who have successfully satisfied their hunger. The child
could also develop a sophisticated plan using explicit representations of goals
and a hierarchically structured set of procedures for achieving them. This last
alternative involves bringing to bear two central concepts discussed here-
inafter—working memory and general intelligence.
Evolved goals help solve the frame problem by channeling the operations
of the executive functions along adaptive lines. They ensure they direct atten-
tion to knowledge relevant to the task at hand (e.g., “how was I successful in
obtaining food on previous occasions?”). They also motivate devising an
appropriate strategy, including strategies based on past experience, but also
new ones designed to overcome new obstacles. Motivational mechanisms allow
for performance examination and evaluation, as in the case of the hungry child,
in which satiation of hunger acts as a cue that the systems have operated suc-
Motivation is a central component of many psychological adaptations. What-
ever cognitive adaptations humans may have, a crucial subset of these adaptations
must function as motivators to engage in adaptive behaviors. Imagine an evolved
cognitive program that functions to detect cheaters during social exchange. As
evolutionary psychologists have pointed out, such a system is essential for the
evolution of reciprocal altruism (e.g., Axelrod, 1984; Cosmides & Tooby, 1989).
For such a system to be effective, it would also have to motivate the person to
alter the situation. Simply knowing that one is being exploited is not enough. It
is for this reason that so much of the psychological research in the areas of altru-
ism and prosocial behavior is concerned with emotions such as guilt, empathy,
and sympathy, as well as those such as moral outrage resulting from nonrecipro-
cated altruistic behavior, free-riding in public goods experiments (Fehr &
Gächter, 2002), or from exploitative behavior.
The scheme of Emmons (1989), shown in Figure 1, is useful in conceptual-
izing the relation between evolved motivational systems and domain-general cog-
nitive processes (see also Bowlby’s  discussion of plan hierarchies). In this
hierarchical model, personal strivings and various lower-level actions and goals
are in the service of motive dispositions at the highest level. An important subset
of these motive dispositions is evolved motive dispositions (EMDs; MacDonald,
1991). EMDs are adaptations that constitute fundamental human biosocial goals.
Personality theory provides a basis for supposing there are many EMDs, includ-
ing those for seeking out social status, sexual gratification, safety, love, and a
sense of accomplishment (MacDonald, 1995, 1998).
The Journal of General Psychology
Some of these EMDs may have characteristic inputs designed to trigger solu-
tions to specific problems in the organism’s EEA. A motivational system such as
hunger or lust, for example, has characteristic inputs (e.g., physiological signals
of hunger [declining blood sugar], the sight of a nubile woman) that motivate the
person to seek the rewards of food and sexual gratification, respectively. The out-
puts of EMDs are typically goals and beliefs rather than specific behaviors. How-
ever, the psychological rewards associated with satisfying these goals, such as the
pleasure associated with satisfying hunger or engaging in sexual intercourse, are
not automatic outputs of the system. Rather, they must be sought after, and their
achievement is by no means guaranteed.
It is such reward seeking (or punishment avoiding) behavior that allows for
flexible strategizing and the evolution of domain-general cognitive mechanisms.
People may solve their hunger problem in any number of ways, including learn-
ing novel contingencies and using mechanisms linked to general intelligence.
There is no requirement that the means of attaining EMDs be an evolutionarily
prepared response: Having a specific set of evolved mechanisms for assuaging
hunger or achieving other EMDs is a nonnecessity. For instance, an organism that
is able to devise novel and opportunistic solutions to the chronic problem of being
hungry would likely have higher biological fitness, as would an organism that is
able to detect causal relations between food and various contingent events via
operant or classical conditioning.
Chiappe & MacDonald9
Evolved Motive Dispositiona
Concern, Project, Taskc
INTIMATE RELATIONSHIP WITH
A GIVEN PERSON
weekends phone number
FIGURE 1. Example of hierarchical model of motivation showing relationships
between domain-specific and domain-general mechanisms. aLevel 1 (evolved
motive dispositions); bLevel 2 (personal strivings); cLevel 3 (concerns, projects,
tasks—use domain-general mechanisms); dLevel 4 (specific action units—use
domain-general mechanisms). Adapted from “The Personal Striving Approach
to Personality,” by R. A. Emmons, 1989, in L. A. Pervin (Ed.), Goal concepts
in personality and social psychology (p. 93).
At a fundamental level, we suppose that problem solving is opportunistic—
people may satisfy their EMDs and achieve the lower-level goals depicted in Fig-
ure 1 by using any and all available mechanisms. The only criterion is what is
effective in goal attainment. Experimentation with a variety of strategies followed
by selection of effective ones is the rule. Children are bricoleurs—tinkerers who
constantly experiment with a wide range of processes to find solutions to prob-
lems as they occur. Children “bring to bear varied processes and strategies, grad-
ually coming through experience to select those that are most effective. . . . Young
bricoleurs . . . make do with whatever cognitive tools are at hand” (Deloache,
Miller, & Pierroutsakos, 1998, p. 803).
Evolutionary Psychology and the EEA
We propose that the view that human cognitive architecture is dominated by
psychological modules stems from a misconstrual of the nature of the evolution-
ary environment and the kinds of adaptations that it produces. According to evo-
lutionary psychologists, the EEA of any animal consists of a set of statistical reg-
ularities—recurring problems and associated cues that can be used in solving
them. Only these regularities can be exploited by natural selection: “It is only
those conditions that recur, statistically accumulating across many generations,
that lead to the construction of complex adaptations. . . . For this reason, a major
part of adaptationist analysis involves sifting for these environmental or organis-
mic regularities or invariances” (Tooby & Cosmides, 1992, p. 69). For example,
the female waist-to-hip ratio is correlated with fertility. Cognitive mechanisms
can evolve that use this cue in solving the problem of identifying viable mates
(Singh, 1993). Natural selection thus results in a set of information processing
devices designed to solve recurrent problems by processing recurring cues from
This view of the EEA, and of the human mind that evolved in response to its
challenges and opportunities, is incomplete. Because recurrence is built into the
definition of an adaptation, it implies there could be no adaptations designed to
deal with novel, nonrecurrent problems: “Long-term, across-generation recur-
rence of conditions . . . is central to the evolution of adaptations” (Tooby & Cos-
mides, 1992, p. 69). Prima facie, this leaves unexplained how humans are rou-
tinely able to solve novel problems, learn novel contingencies, create the
extraordinary human culture characteristic of the last 50,000 years of human evo-
lution, and cope with life in a constantly changing world far removed from the
Pleistocene. It leaves unexplained the massive body of data, reviewed hereinafter,
showing human intelligence and learning function to solve novel problems.
Accordingly, it is necessary to develop a concept of adaptation not restricted
to mechanisms designed to process statistically recurrent features of the environ-
ment. As used here, an adaptation is a system of inherited and reliably developing
properties that became incorporated into the standard design of a species because
The Journal of General Psychology
it produced functional outcomes that contributed to propagation with sufficient fre-
quency over evolutionary time. The functional outcomes include the achievement
of evolved motive dispositions discussed heretofore. This view is broad enough to
include domain-general mechanisms, such as those enabling us to reason by anal-
ogy, which we will discuss hereinafter. Organisms with mechanisms that enable
analogical reasoning would be capable of solving a wide range of adaptive prob-
lems, even those that occur in a single generation.
Central to our position is the view that a critical aspect of the EEA was that
humans were forced to adapt to rapidly shifting ecological conditions by devel-
oping adaptations geared to novelty and unpredictability. The EEA was not a peri-
od of stasis, but rather a period of rapid change that witnessed the appearance and
disappearance of several different hominid species over a 2-million-year period
(Foley, 1996; Irons, 1998). Modern Homo sapiens appeared late in the Pleis-
tocene (100,000 to 200,000 years ago) and exhibited a wide variety of distinct
hunting and gathering ways of life. Humans and other mammals were forced to
adapt to inconsistent selection pressures because of rapidly changing ecological
conditions (Potts, 1998; Richerson & Boyd, 2000). Environmental fluctuations
became increasingly extreme from the Miocene to the present. For example, on
the basis of European pollen sources, there were repeated alternations between
dense, moist forests and cold, dry steppe during the past million years. These
shifts were unpredictable and nonrepetitive rather than cyclic, and included
decade-scale fluctuations between glacial and warm conditions and century-long
shifts between cold steppe and warm forested conditions, interspersed with peri-
ods of climatic stability. Rapid local change also resulted from volcanoes, earth-
quakes, and tectonic activity.
These climatic shifts are associated with increased diversity of encephalization
among mammalian lineages, with some lineages—prototypically the lineage lead-
ing to humans—evolving toward larger brains and increased behavioral flexibility.
There was a broad trend during the Pleistocene toward the evolution of mammalian
taxa that were more flexible in eating habits, patterns of social grouping, and group
size in relation to resource availability. This corresponded to a period of rapid envi-
ronmental shifts during the mid-Pleistocene. In our view, the predominant human
response was to evolve adaptive flexibility by developing mechanisms designed to
deal with novel and unpredictable settings. This adaptive flexibility led to a decou-
pling of the organism from any one habitat:“Hominids became less inclined to track
particular habitats as change occurred and more capable of adjusting to novel con-
ditions and the increasing range of [climatic] oscillation” (Potts, 1998, p. 93).
A major trend in human evolution has been increased encephalization, the
largest increases coinciding with the largest environmental oscillations (Potts,
1998). Larger brain size is also linked with a wider geographic range, which sug-
gests that the larger brain enabled greater adaptation to environmental diversity.
Across mammalian species, and particularly in the line leading to humans, there
are associations among brain size, mental ability, learning ability, flexibility of
Chiappe & MacDonald 11
response, and developmental plasticity. There are also associations among these
variables and the elaboration of costly parenting practices, delayed sexual matu-
ration, and a prolonged juvenile period in which social learning is of great impor-
tance (Barton, 1999; Eisenberg, 1981; Jerison, 1973; Johanson & Edey, 1981;
Richerson & Boyd, 2000).
Associations between brain size and innovation have been found among both
mammals and birds. Reader and Laland (2002) found an association between exec-
utive brain ratio (neocortex and striatum volume over brainstem) and innovation,
tool use, and social learning. Their results suggested that there was selection among
primates for “adaptive complex variable strategies, such as inventing new behavior,
social learning, or using tools” (p. 4440). Social learning frequency was indepen-
dent of group size, providing support for ecological (foraging) hypotheses for brain
evolution in primates. Similarly,Lefebvre,Whittle,Lascaris,and Finkelstein (1997)
found a link between relatively larger forebrain structures and the frequency of
opportunistic foraging innovations for various avian orders. To count as a foraging
innovation, the behaviors had to be noted by field observers as being highly unusu-
al for the species; for example, using automatic sensors to open bus station doors
(House sparrow), using cars as nutcrackers for palm nuts (Common crow), opening
conch shells by dropping them on concrete-filled drums (Osprey), and so on.
General Intelligence as an Adaptation to Novelty and Unpredictability
Though the Pleistocene may have intensified the need to adapt to novelty and
unpredictability, and humans have specialized in the flexible, domain-general
mechanisms of learning and general intelligence, environments are never com-
pletely stable and predictable for any animal. We argue that human general intel-
ligence is an elaboration of abilities present in many animals (perhaps also includ-
ing mechanisms that are unique to humans). Animals and humans often have to
make decisions about how to attain their goals in situations in which past learn-
ing, whether by specialized or unspecialized simple learning mechanisms, is inef-
fective in attaining evolved goals.
General Intelligence in Animals and Humans
Common ravens (Corvus corax), for example, can solve problems they
have not encountered as a selective force in their evolution. According to Hein-
rich (2000), this behavioral flexibility results from problem solving mecha-
nisms that ravens evolved as a way of exploiting diverse and unpredictable envi-
ronments—results highly compatible with the findings of Lefebvre et al. (1997;
see heretofore). Ravens can be found around the globe, in environments rang-
ing from arctic tundra to forests, mountains, and urban environments, and in
each of these environments, they are able to respond to adaptive problems in
very flexible ways.
The Journal of General Psychology
Heinrich (2000) demonstrated the ability of ravens to solve novel problems
by using long pieces of string to hang meat from a perch. For ravens, gaining
access to this food was a novel problem. The solution involved repeated pulls on
the string with the beak while holding and releasing the string with a foot. Though
each step in the solution may be innate (e.g., grabbing objects with their beaks or
with their feet), assembling these behaviors into a sequence that solves the prob-
lem was novel. Not all birds arrived at this solution, indicating individual differ-
ences in performance, as there are for general intelligence in humans.
According to Heinrich (2000, p. 300), the solution was accomplished by
“insight” occurring suddenly and within a short time of the bird being exposed
to the problem. It does not emerge through a gradual trial and error learning
process. Heinrich argued that the ravens formulated a goal, built mental scenar-
ios, and evaluated possible sequences of actions without having to endure their
consequences. They took into account information from various sources in plan-
ning the solution. Thus, the ravens did not pull up the string if the piece of meat
appeared to be too large, nor did they pull up the string if it was attached to rocks
rather than meat.
Heinrich (2000) noted that insight is used to solve problems “whose solution
is not wholly preprogrammed” (p. 289)—that is, problems that are not recurrent
in the EEA and not previously encountered by the individual. Insightful problem
solving has also been demonstrated in apes (Köhler’s  Einsicht problems)
and pigeons. Epstein, Kirshnit, Lanza, and Rubin (1984) showed that pigeons
trained to do two separate tasks (pushing a box, pecking a banana-like object)
were able to put them together to solve a problem that required both abilities. Ani-
mals trained in only one of these tasks could not solve the problem.
Anderson (2000) found evidence for general intelligence in rats by studying
problems that required the ability to “combine noncontiguously learned behav-
iors into a solution for a novel problem” (p. 81). The problems included finding
a route to a goal box when the previously learned route was blocked and com-
bining knowledge obtained from more than one source to solve a novel problem.
Individual differences on these tasks are stable and correlated with other con-
ceptually similar tests to make up an animal g factor, whereas simple learning
tasks do not correlate with each other or show stable individual differences.
Anderson extracted a single factor from three such tests and showed that perfor-
mance on the tests was positively correlated with brain size (which is known to
correlate with general intelligence in humans; Jensen, 1998).
Crinella and Yu (1995) used similar tasks and also extracted a g factor in rats
that was unrelated to simple learning. Tasks loading on the g factor involved ana-
lytical skills, learning and memory, and the ability to form strategies. Their g fac-
tor for rats based on five tests accounted for 34% of the variance, a finding that
was comparable to studies of g in humans (Jensen, 1998). Solving these prob-
lems typically involved combining information from multiple sources, including
from modules specialized for processing spatial information. Spatial learning is
Chiappe & MacDonald 13
a modular process in rats (Gallistel, 1990), but the frontal cortex is involved in
solving novel problems using spatial information. Research results have shown
that the frontal cortex is essential to combining information from different expe-
riences but that it is not essential to spatial learning per se (Poucet, 1990). The
ability to integrate this information with other experiences (learned associations)
is part of a positive manifold linked to success in solving other novel problems
and to brain size—general intelligence.
The animal data fit well with research on humans, which has consistently
found more intelligent people are better at attaining goals in situations of mini-
mal prior knowledge. Of particular importance is fluid intelligence, defined as
“reasoning abilities [consisting] of strategies,heuristics,and automatized systems
that must be used in dealing with ‘novel’ problems, educing relations, and solv-
ing inductive, deductive, and conjunctive reasoning tasks” (Horn & Hofer, 1992,
p. 88). Tests of fluid intelligence produce the highest correlations with g (Car-
penter, Just, & Shell, 1990; Carroll, 1993; Duncan, Burgess, & Emslie, 1995).
Tests such as Raven’s Progressive Matrices and Cattell’s Culture Fair Test tap the
capacity “to adapt one’s thinking to a new cognitive problem” (Carpenter et al.,
p. 404). This highlights the idea that intelligence taps conscious problem solving
in situations in which past recurrences would be unhelpful, except perhaps by
analogy or by induction, to the new situation.
Mechanisms Underlying General Intelligence
Working memory capacity has been implicated as underlying individual dif-
ferences in fluid intelligence (e.g., Bechelder & Denny, 1977; Engle, Tuholski,
Laughlin, & Conway, 1999; Kyllonen & Christal, 1990; Larson & Saccuzzo,
1989). For example, Kyllonen and Christal found correlations from .80 to .90
between a working memory factor (e.g., digit span, mental arithmetic) and a rea-
soning factor (analogies, verbal reasoning). Engle et al. showed that the execu-
tive functions of working memory (assessed by tasks involving attentional con-
trol) predicted g, but that short-term memory capacity (assessed by tasks such as
memory for sets of words) did not. The variance unique to working memory tests
predicted individual differences in fluid intelligence, but the variance common to
both types of tests did not, suggesting that the variability in the executive func-
tions (the component that distinguishes tests of short-term memory from work-
ing memory tests) underlies differences in fluid intelligence.
One role of the executive functions in solving novel problems is in goal man-
agement. This involves constructing, executing, and maintaining a mental plan of
action during the solution of a novel problem (Carpenter et al., 1990). For exam-
ple, the Raven’s Progressive Matrices fluid intelligence test and the Tower of
Hanoi problem (in which participants must develop a long-term plan with multi-
ple subgoals) require one to be able to activate multiple goals and keep track of
the satisfaction of each of the goals (Carpenter et al., p. 413). Performance on
The Journal of General Psychology
these tasks in the study by Carpenter et al. was highly correlated (r = .77), which
suggested that substantial goal management was necessary in both tasks.
Executive functions underlying general intelligence are thus involved when
problems call for substantial planning and keeping track of various subgoals.
They are involved in dealing with situations that are very demanding of atten-
tional resources because multiple constraints that may vary substantially with the
context have to be taken into account. As Marshalek, Lohman, and Snow (1983)
have pointed out, “more complex tasks may require more involvement of execu-
tive assembly and controlled processes that structure and analyze the problem,
assemble a strategy of attack on it, monitor the performance process, and adapt
these strategies as performance proceeds” (p. 124).
Executive functions should play a more important role in earlier stages of
skill acquisition; once planning is no longer essential, the problem is no longer
novel because its solution has become proceduralized. Thus, Ackerman (1988)
found differences in measures of g were important during earlier stages in skill
acquisition. However, with sufficient practice, the effects of g disappeared, pro-
vided the task remained fairly consistent (for instance, the rules didn’t suddenly
change). With practice,individual differences were accounted for by speed of per-
ceptual processing and motor responding.
Neuropsychological evidence suggests that the frontal lobes are the locus of
the executive functions (Duncan et al., 1995; Duncan, Emslie, Williams, Johnson,
& Freer, 1996). Patients with frontal lobe damage have difficulty planning for the
future, they repeat movements and actions, and score lower on measures of fluid
intelligence. Frontal lobe patients matched for scores on crystallized intelligence
with normal controls scored 20 to 60 points lower than did the controls on the Cat-
tell Culture Fair Test, a measure of fluid intelligence (Duncan et al., 1995).
Other studies by Duncan and his colleagues show that the inability to solve
novel problems because of frontal lobe damage arises because of problems in goal
management. The solution of novel problems involves a hierarchically structured
process characterized by goals and a set of progressively detailed subgoals that
require attention to a wide range of information. There is “successive selection
of requirements or constraints at multiple levels of abstraction, using knowledge
concerning the implications of one fact to establish target values for others. Can-
didate goals are suggested both by currently active supergoals and by the state of
the environment” (Duncan et al., 1996, p. 263). People with damage to the frontal
lobes, particularly the dorsolateral prefrontal cortex, are characterized by goal
neglect—the “disregard of a task requirement, even though it has been under-
stood” (Duncan et al., 1996, p. 265).
Controlled attention is critical to goal management (Engle et al., 1999; Kane,
Bleckley, Conway, & Engle, 2001; Lustig, May, & Hasher, 2001). The mecha-
nisms of controlled attention are limited capacity mechanisms responsible for
activating relevant representations and keeping them in an active state while
inhibiting irrelevant ones. Activation of representations is important because they
Chiappe & MacDonald 15
guide behavior. Kane et al. found that keeping task-relevant information in active
state is particularly challenging in conditions in which distracting information is
present. Distracting information needs to be suppressed, because if it is not, the
distracters—and not the goal-relevant information—will guide behavior. “The
controlled attention functions of the central executive are necessary for those
processes required to maintain the activation of memory units and to focus, divide
and switch attention as well as those processes to block inappropriate actions and
to dampen activation through inhibition” (Engle et al., p. 327).
Individual differences in working memory capacity reflect differences in the
capacity for controlled attention. Kane et al. (2001) found that participants with
low working memory capacity were less able to inhibit the prepotent response
of orienting toward a visual cue in a task that required them to look in the direc-
tion opposite the cue. This supports the idea that working memory capacity plays
a crucial role in controlling attention in situations in which responding is not
automatic—that is, situations requiring active engagement with task goals and
the inhibition of prepotent responses.
The frontal lobes play a crucial role in controlling attention and managing
potentially interfering information (Goel & Grafman, 1995; Goldberg, 2001). For
example, frontal lobe patients have difficulty inhibiting immediate impulses and
thus perform poorly on the Stroop test (Goldberg). Frontal lobe patients also have
difficulty inhibiting responses on the Tower of Hanoi puzzle for which success-
ful moves require inhibiting long-term goals in favor of short-term goals that
seem inconsistent with the long-term goal (Goel & Grafman). Frontal lobe
patients performed more poorly because they were less able to resolve conflicts
between end-goals and subgoals requiring the temporary inhibition of certain
The executive functions of working memory and the mechanisms of activa-
tion and inhibition do not seem to satisfy the criteria for modularity. By defini-
tion, mechanisms for solving novel problems have to be unspecialized in the
domains for which they provide solutions. Although they may have access to spe-
cialized information obtained from the various modules that provide them with
inputs, the problem solving procedures would have to be general enough to allow
us to solve novel problems in various domains. We noted above that there is a
substantial correlation between performance on the Raven’s Progressive Matri-
ces and performance on the Tower of Hanoi puzzle. Both tasks require a sub-
stantial amount of goal management, working memory, and inhibition of prepo-
tent responses. However, the types of information used in solving these problems,
the specific goals and subgoals, and the specific responses requiring suppression
are unique to each task.
Furthermore, measures of working memory capacity predict performance
across a wide range of tasks. The only common element is that they make high
demand on attentional resources. For example, people who did well on a mathe-
matical processing task also tended to do well on a perceptual task requiring inhi-
The Journal of General Psychology
bition of prepotent responses (Kane et al., 2001). Similarly, Lustig et al. (2001)
found that individual differences in the capacity to inhibit no-longer-relevant
information (i.e., proactive interference) predicted how well participants remem-
bered the components of a story. Turner and Engle (1989) showed that perfor-
mance on a mathematical processing task and a reading span task, another mea-
sure of working memory capacity, both predicted reading ability. This is what one
would expect if working memory “reflects an abiding,domain-free capability that
is independent of any one processing task” (Kane et al., p. 169).
Nor do these processes seem to be information encapsulated. Modules carry
out their operations by taking into consideration a very limited database (Fodor,
1983). The executive functions of working memory, however, coordinate infor-
mation from various sources. As noted heretofore, working memory is like an
executive who delegates tasks to subordinates, integrates information from other
areas, and selects what information to seek (Goldberg, 2001). Indeed, the pre-
frontal cortex, the seat of the executive functions, is connected to every functional
area of the brain. As a result, it is well suited for coordinating and integrating the
work of all the other brain structures (Goldberg).
The executive functions are thus able to access goal-relevant information
from a wide range of domains when solving a problem. Indeed, it is by being able
to access representations from more modular processes that the executive func-
tions are able to extend cognitive competencies in ways that are unrelated to their
evolutionary function (e.g., Mithen, 1996). The data on general intelligence in
animals is also consistent with this view. For example, Thompson, Crinella, and
Yu (1990) found that six brain regions were involved in psychometric g for the
rat,including a visuospatial attentional mechanism,a visual discrimination mech-
anism, a vestibular-proprioceptive-kinesthetic discrimination mechanism, a place
learning mechanism, and a nonspecific mechanism. Detterman (2000) has noted
the consistency of these data with research on human intelligence.
There is much evidence that general intelligence facilitates the integration of
information obtained from modules. Geary’s (1995) distinction between biolog-
ically primary and biologically secondary abilities is useful in this regard. Bio-
logically primary abilities are domain specific and include abilities such as lan-
guage and simple quantitative abilities, which develop universally and
spontaneously. Biologically secondary abilities, such as reading and mathemati-
cal ability, use these domain-specific modules, but in a novel manner. Rather than
seeming to be spontaneous and effortless, biologically secondary abilities typi-
cally require practice and tuition, often with coercion, bribery, or exhortation.
Learning these biologically secondary abilities involves conscious awareness
rather than implicit awareness. Success at these biologically secondary abilities
is strongly correlated with general intelligence (Geary).
As a case in point,human language results from highly dedicated systems that
enable children to effortlessly and unconsciously learn extraordinarily complex
and productive grammatical rules (Pinker, 1994). However, skill in integrating
Chiappe & MacDonald17
these language systems as well as the output of visual processing mechanisms into
an evolutionarily novel ability—reading—is strongly linked to general intelli-
gence. Unlike language learning, reading is typically mastered only with a great
deal of conscious effort and represents a major hurdle for many schoolchildren.
The correlation between IQ and reading skills ranges from about .6 to .7, even
longitudinally (e.g., Stevenson, Parker, Wilkinson, Hegion, & Fish, 1976). IQ
correlates with reading most when decoding ability—a specialized process—is
controlled (Jensen, 1998). Children at the third- or fourth-grade level are adept
at decoding, and individual differences are mainly in comprehension. Reading
comprehension is approximately as highly correlated with verbal as with non-
verbal IQ. Similarly, there is evidence that children’s language learning is lim-
ited because of limitations in their working memory (Elman, 1994; Newport,
1991). As noted heretofore, working memory is a domain-general ability that is
strongly associated with g.
Functions of General Intelligence:
Decontextualization and Analogical Reasoning
In an admittedly speculative treatment, Cosmides and Tooby (2000, 2002)
proposed to account for the ability of humans to solve novel problems by the evo-
lution of metarepresentational abilities that include a “scope syntax” (Cosmides
& Tooby, 2002, p. 182) that marks some information as only locally true or false.
It includes “a set of procedures, operators, relationships, and data-handling for-
mats that regulate the migration of information among subcomponents of the
human cognitive architecture” (Cosmides & Tooby, 2002, p. 183). Of particular
importance are metarepresentations that allow us to decouple representations of
locally true information from the rest of our knowledge base (e.g., John believes
that X, where X may be true or false). This allows people “to explore the prop-
erties of situations computationally, in order to identify sequences of improvised
behaviors that may lead to novel, successful outcomes” (Cosmides & Tooby,
2000, p. 67). This view implies that intelligence involves what one might term
hyper-contextualization because it highlights local contingencies and an unspec-
ified set of mechanisms that allow for solutions of localized problems in ways
not coupled to the modular mechanisms designed to solve evolutionarily recur-
Though metarepresentational abilities are of undoubted importance in solv-
ing novel problems, they are domain general (Chiappe, 2000). For instance, Sper-
ber (1994) postulated a metarepresentation module specialized for thinking
explicitly about representations. This mechanism has, inter alia, the “ability to
evaluate the validity of an inference, the evidential value of some information,
[and] the relative plausibility of two contradictory beliefs” (Sperber, p. 61). Fur-
The Journal of General Psychology
thermore,it is able to carry out these activities across all domains of thought. “The
actual domain of the metarepresentational module is the set of all representations
of which the organism is capable of inferring or otherwise apprehending the exis-
tence and content” (Sperber, p. 60). However, if this mechanism were able to, say,
evaluate the validity of inferences in any domain, as Sperber himself suggests,
then it seems most reasonable to characterize the mechanism in question as a
domain-general reasoning mechanism and not as a module.
Moreover, Cosmides and Tooby’s (2002) emphasis on hyper-contextualiza-
tion is radically at odds with data showing that general intelligence facilitates
solving novel problems not by emphasizing local contingency but by decontex-
tualization and abstraction. Decontextualization enables humans to inhibit the
operation of highly context-sensitive, implicit, and automatic heuristics for mak-
ing inferences,judgments,and decisions (Stanovich & West,2000). It is an aspect
of Piagetian formal operational thought, “the independence of its form from its
reality content” (Piaget, 1972, p. 10). Decontextualization enables dealing with
novel and unpredictable environments because a common source of solutions to
novel problems involves recognizing similarities between new problems and pre-
viously solved problems, as via analogical reasoning.
IQ researchers are well aware of the centrality of decontextualization for
thinking about intelligence.
One of the well-known byproducts of schooling is an increased ability to decontex-
tualize problems. In almost every subject…pupils learn to discover the general rule
that applies to a highly specific situation and to apply a general rule in a wide vari-
ety of different contexts. The use of symbols to stand for things in reading (and musi-
cal notation); basic arithmetic operations; consistencies in spelling, grammar, and
punctuation; regularities and generalizations in history; categorizing,serializing,enu-
merating, and inferring in science, and so on. Learning to do these things, which are
all part of the school curriculum, instills cognitive habits that can be called decon-
textualization of cognitive skills. The tasks seen in many nonverbal or culture-reduced
tests call for no scholastic knowledge per se, but do call for the ability to decontex-
tualize novel situations by discovering rules or regularities and then using them to
solve the problem. (Jensen, 1998, p. 325)
Investigations of human reasoning show humans often radically contextualize
problems. In particular, Stanovich and West (2000) noted that people have the fol-
lowing tendencies: (a) to adhere to conversational principles even in situations that
lack many conversational features; (b) to contextualize a problem with as much
prior knowledge as is easily accessible, even when the problem is formal and the
only solution is a content-free rule; (c) to see design and pattern in situations that
are undesigned, unpatterned, or random; (d) to reason enthymematically—to make
assumptions not stated in a problem and reason from those assumptions; and (e)
the tendency toward a narrative mode of thought.
Thinking evolved in a social context, and the contextualization process often
works quite well (Anderson, 1991; Oaksford & Chater, 1996). However, there are
Chiappe & MacDonald19
many real-life situations in which decontextualization is called for, and decon-
textualization is linked with g. There is evidence that people with higher g are
better able to reason logically on a wide variety of tasks, including those in which
people are prone to the systematic biases resulting from the radical contextual-
ization characteristic of human thinking.
In two studies, Stanovich and West (1998) found correlations from 0.25 to
0.40 between g and performance on various reasoning problems in a university
sample (thus attenuating the correlations compared with a general population
sample). These included tasks in which successful performance requires ignor-
ing the believability of the conclusion: knowing how to falsify an “if P, then Q”
statement in Wason’s Selection Task; avoiding influence by vivid but unrepre-
sentative information in favor of valid statistical information; avoiding the bias
of allowing prior beliefs to influence evaluations of arguments; evaluating asso-
ciation based on 2 × 2 contingency tables; avoiding the bias of rating positive out-
comes as superior to negative ones when confronted with equally compelling evi-
dence for both; being able to test the influence of one variable by holding others
constant; choosing one of two counterfactual suggestions as better when there is
objectively no difference.1IQ accounted for about 39% of the variance on the
Stanovich and West (1998) interpret their results as indicating two distinct cog-
nitive systems. System 1 is an interactional, social intelligence. It is composed of
mechanisms that support communication in which intention and attribution are crit-
ical. This has been termed interactional intelligence by Levinson (1995). Constru-
als triggered by System 1 are highly contextualized, personalized, and socialized.
They are driven by considerations of relevance and are aimed at inferring inten-
tionality by the use of conversational implicature, even in situations that are devoid
of conversational features. System 1 consists of modules performing specific com-
putations that solve recurrent problems in the human social EEA. They solve these
problems quickly and unconsciously. The primacy of these mechanisms leads to
what Stanovich and West (1998,p. 180) term the “fundamental computational bias”
in human cognition—the tendency to automatically contextualize problems, which
may have yielded solutions to recurrent social problems in our evolutionary past
(Levinson). There appears to be low variability in interactional intelligence and lit-
tle relation between interactional intelligence and IQ (Jones & Day,1997; Matthews
& Keating, 1995; McGeorge, Crawford, & Kelly, 1997).
System 2 conjoins the various characteristics that have been viewed as typi-
fying controlled processing. It encompasses the mechanisms underlying general
intelligence. System 2’s more controlled processes serve to decontextualize and
depersonalize problems. This system is more adept at representing in terms of rules
and underlying principles. Though this system is much slower than System 1 mod-
ules, its advantage is its flexibility—its ability to solve novel problems. It can deal
with problems without social content and is not dominated by the goal of attribut-
ing intentionality or by the search for conversational relevance.
The Journal of General Psychology
This is a central process whereby humans solve novel problems. According
to James (1890, p. 530), “the faculty for perceiving analogies is the best indica-
tion of genius.” People who could analogize are “the wits, the poets, the inven-
tors, the scientific men, the practical geniuses” (James, p. 530). Correlations
between tests of general intelligence and tests of analogical reasoning range from
.68 to .84 (Spearman, 1927; Sternberg, 1977). As indicated hereinafter, this is
because analogical reasoning involves a conscious, controlled, comparison
process that draws heavily on working memory.
Analogical reasoning involves drawing parallels between novel problems
and problems that have been solved in the past. Analogies, such as “sound is like
a water wave,” thus involve transferring information across conceptual domains
(Chiappe, 2000; Gentner & Holyoak, 1997; Holyoak & Thagard, 1995). The
transfer is based on establishing relevant similarities between a source domain
(e.g., water waves) and a target domain (e.g., sound or light). Analogies allow us
to use a familiar situation as a model for making inferences about an unfamiliar
situation (Gentner & Holyoak). An analogy between water waves and the prop-
agation of sound may depend on noticing that both spread from a point of origin,
and it may lead us to infer that sound should bounce back when it strikes a sur-
face (Holyoak & Thagard).
Analogical reasoning does not fit well into a fully modular view of cogni-
tion (Chiappe, 2000; Fodor, 1983; Mithen, 1996). It is thus not surprising that a
discussion of analogical reasoning is largely absent from foundational articles on
evolutionary psychology. The influence of analogy in cognition, however, can be
witnessed across all spheres of human life. It is a truly domain-general process.
Religions analogize gods to humans. Scientists use analogies in developing the-
ories (Huygens’s use of light and sound to support his wave theory of light; Dar-
win’s analogy between artificial selection and natural selection; the mind as a
blank slate or computer). Analogies are also common in political rhetoric (the
domino theory of communism), precedent-based legal reasoning, and everyday
language (e.g., “We’re at a crossroads”; Lakoff & Johnson, 1980). It is important
to note that showing people an analogous situation from a very different domain
facilitates solving novel problems (Gick & Holyoak, 1980).
Implicit in these examples is also the unencapsulated nature of analogical rea-
soning. The more information that a system can take into account, the less encap-
sulated it is. As Fodor (1983, p. 117) notes, “By definition, encapsulated systems
do not reason analogically.”There seems to be no limit to the domains humans can
bring together for comparison—lawyers and sharks, crime and disease, evolution
and lotteries, rage and volcanoes, education and stairways (Chiappe, 2000).
Although many of the comparisons that we are capable of making are fruitless
(computers as windshields), our capacity to make them shows we are capable of
bringing just about any two concepts together (Chiappe; Koestler, 1964).
Chiappe & MacDonald21
Analogical reasoning involves explicit manipulation of mental representa-
tions. In reasoning analogically, we consciously reflect on representations,
searching among their properties for those pertinent to the analogy. Analogical
reasoning also requires comparison processes as described, for example, in Gen-
tner’s (1983) structure mapping theory. The comparison process involves estab-
lishing a common system of relations between the source domain and the target
rather than simply mapping attributes of the objects. For example, an analogy
between the solar system and a hydrogen atom exploits the higher-order relation
between the sun’s attraction of the planet as the cause of the planet revolving
around the sun rather than the superficial attributes of the sun or planets.
Several studies have shown the overall importance of relational matches to
analogical reasoning, especially higher-order relational matches that map in a
systematic and principled manner onto the target (e.g., Clement & Gentner, 1991;
Gentner & Clement, 1988; Markman & Gentner, 1993). In general, people pre-
fer interpretations that involve establishing similarities at abstract levels: “People
prefer to match and carry over systems of predicates governed by higher-order
constraining relations” (Gentner & Clement, 1988, p. 313). Analogical reasoning
is also a goal-driven process (Dunbar, 1997; Holyoak & Thagard, 1995; Spell-
man & Holyoak, 1996). Goals play a crucial role in analogical reasoning because
they serve to ensure that the process is guided along relevant directions, thereby
avoiding the frame problem. As indicated heretofore, goals are critical to the evo-
lution of domain-general mechanisms, and goal management is an important
aspect of general intelligence.
Analogical reasoning on the basis of relations is also found among animals.
The best documentation is on chimpanzees, which researchers have found are
capable of using geometric and functional relationships as the basis of analogies
(Gillan, Premack, & Woodruff, 1981; Oden, Thompson, & Premack, 2001). Such
reasoning may not occur in the wild because it has been observed only in animals
trained to use a symbol system that explicitly contains concepts for the relations
“same” and “different” (Oden et al.). “Prior experience with tokens, analogous to
words, that symbolize abstract same/different relations is a powerful facilitator
enabling a chimpanzee…to explicitly express in judgment tasks…their otherwise
implicit perceptual knowledge about relations between relations” (Oden et al., p.
490). Monkeys given similar training are unable to solve analogical problems
(Oden et al.).
Analogical reasoning and working memory. We have noted that working mem-
ory is a critical component of general intelligence. Analogical reasoning makes
substantial use of the resources of working memory. The comparison process
requires both a storage component and an attention-demanding, processing
component—two hallmarks of working memory tasks. Analogies require the
activation of important elements and relations of the domains involved while
searching for abstract commonalities between the two. The processing goals
The Journal of General Psychology
motivating the analogy must be kept active. Potentially distracting components
of the domains (e.g., superficial features that are irrelevant to the final inter-
pretation of the analogy) must be inhibited.
In a study whose results supported the role of working memory in analogical
reasoning,Mulholland,Pellegrino,and Glaser (1980) found that participants made
more errors and took longer to respond as the number of elements and transfor-
mations required to solve an analogy increased. “Increases in solution latency and
error rates were due to working memory limitations associated with the represen-
tation and manipulation of item features at high levels of transformational com-
plexity” (Mulholland et al., p. 281). Kyllonen and Christal (1990) found positive
correlations ranging from .36 to .54 between performance on verbal analogy prob-
lems and working memory capacity tests. Waltz,Lau,Grewal,and Holyoak (2000)
found that increasing working memory load by having participants generate ran-
dom numbers while solving analogies resulted in fewer higher-level relational
responses and more lower-level attribute responses than those who did not have to
do that task. Similarly,Tohill and Holyoak (2000) found that participants with state
anxiety—a factor known to depress working memory—produced fewer relation-
al responses and more attributional responses than those in the low anxiety group.
Neuropsychological research also supports the connection between working
memory and analogical reasoning. Waltz et al. (1999) found that participants with
damage to the prefrontal cortex—the locus of the executive functions of working
memory—were impaired in the conditions that required integrating multiple rela-
tions, including analogical reasoning. “Relational reasoning appears critical for
all tasks identified with executive processing and fluid intelligence” (Waltz et al.,
p. 123). Waltz et al. suggested that deficits in planning and problem solving can
be explained on the basis of deficits in relational integration. The construction of
a hierarchy of subgoals when solving problems “is a special case of relational
interaction” (Waltz et al., p. 123). The results of neuroimaging studies have indi-
cated increasing activation in the dorsolateral prefrontal cortex and in the parietal
cortex as relational complexity increased (Holyoak & Hummel, 2001). While the
dorsolateral prefrontal cortex is involved in the domain-general task of manipu-
lating relations among display elements,the domain-specific posterior cortex rep-
resents the elements of the relations (Holyoak & Hummel).
Analogical reasoning, decontextualization, and the creation of new categories. As
with other factors related to g, analogical reasoning involves decontextualization.
In terms used by Stanovich and West (2000), analogical reasoning involves System
2,the controlled processing system that decontextualizes problems,rather than Sys-
tem 1, which is automatic, unconscious, and highly contextualized.
Analogies often require abstraction—a form of decontextualization. Map-
ping across very different semantic domains requires generating representa-
tions that abstract away from specific details of the domains involved to pro-
duce a schema that preserves the abstract relations common to the two
Chiappe & MacDonald 23
domains, while ignoring the characteristics unique to each (Holyoak, 1984).
Karmiloff-Smith (1992) referred to the process of abstraction as representa-
tional redescription, through which patterns embedded in a particular domain
become represented more explicitly and more abstractly. As a result, represen-
tations become more broadly accessible: “Information already present in the
organism’s independently functioning, special-purpose representations, is
made progressively available…to other parts of the cognitive system”
(Karmiloff-Smith, pp. 17–18).
Analogical reasoning therefore yields general problem solving schemas—
higher-order categories applicable across a wide range of domains of which the
specific analogs are instances (Holyoak, 1984). This decontextualization “deletes
differences between the analogs while preserving their commonalities”(Holyoak,
p. 208). Such decontextualization plays a role in the generation of new concepts
in science, as when the abstract concept of a wave is used to apply to vastly dif-
ferent domains. “Once a more abstract concept of a wave was established, it
played a role in the further extension [from water waves and sound waves] to light
waves” (Holyoak & Thagard, 1995, p. 23).
The process of creating new categories through analogical reasoning is also
evident in the metaphorical statements that are ubiquitous in natural language,
statements such as “crime is a disease,” “my job is a jail,” and “rumors are weeds”
(Chiappe, 2000; Lakoff & Johnson, 1980). The process of combining concepts in
metaphorical statements leads to the creation of categories that are more abstract
than the source and target concepts involved (Glucksberg,2001). For example,the
metaphor “rumors are weeds” leads to the creation of the category “undesirable
things that spread quickly and uncontrollably.” Once generated, this category can
be applied to a wide range of novel situations.
Domain Specificity and Domain Generality in Learning
To this point, we have argued that the mechanisms underlying general intel-
ligence evolved to solve novel problems. This does not, however, exhaust the role
of domain generality. We hypothesize that many of the mechanisms underlying
what has traditionally been called “learning” also feature domain generality, but
are unrelated to measures of g. This is because these learning processes do not
need the heavy working memory involvement typical of tasks reflecting general
intelligence. Nonetheless, we claim that their domain generality enables organ-
isms to satisfy evolved goals by exploiting novel contingencies.
To begin, motivation represents a major point of contact between evolution-
ary approaches and approaches based on learning theory. Learning theories gen-
erally suppose that some motivational systems are biological in origin, but tradi-
tionally they have tended toward “biological minimalism.” They posit only a bare
minimum of evolved motivational systems. For example, traditional drive theory
proposed that rats and people have drives to consume food, satisfy thirst, have
The Journal of General Psychology
sex, and escape pain. For an evolutionist, this is a good start, but it leaves out a
great many other things that organisms desire innately. As noted heretofore, even
this short list of evolved motivations, or even one such biologically based moti-
vational system, would be consistent with the evolution of domain-general mech-
anisms. Indeed, this has been the implicit and at times explicit rationale behind
discussions by learning theorists that domain-general learning is adaptive in an
evolutionary sense (Skinner, 1981).
In general, learning biases (e.g., biases in favor of learning certain skills) and
guided learning (in which the goal of learning is achieved via genetic systems)
are expected to be weak in highly variable (nonrecurrent) environments that are
not available to the genes directly and in situations in which individual trial and
error learning is quite inexpensive (Boyd & Richerson 1985,1988; Geary & Huff-
man, 2002; Richerson & Boyd, 2000). Conversely, biased learning is favored in
situations in which the problems to be solved are recurrent features of the EEA
and there are high costs to individual learning.
Evolutionary analyses of learning emphasize that learning mechanisms
imply a great deal of evolved machinery and that they are often biased in ways
that make certain types of learning easier than others (Garcia & Koelling, 1966;
Öhman & Mineka, 2001; Rescorla, 1988; Rozin & Schull, 1988). A paradigmat-
ic example is taste aversion learning, which is observable in a wide range of
species, including quail, bats, catfish, cows, coyotes, and slugs (Kalat, 1985). If
a rat consumes food and later feels nauseous, then it associates the illness with
the food rather than with other more recent stimuli such as lights and sounds, and
it will make this association over much longer periods of delay than is typical for
other examples of learning. The association of food with poison is greatly influ-
enced by whether the food is unfamiliar to the animal—an indication that taste
aversion learning is an adaptation to nonrecurrent and unpredictable features of
This example shows that some types of novelty are sufficiently recurrent to
yield dedicated,domain-specific mechanisms designed to cope with them. Recur-
rent novelty occurs when organisms have been confronted over an evolutionari-
ly significant period with a need to evaluate a specific kind of novel situation,
such as rats evaluating novel foods. Novel food items are a potential resource for
the animal and must not be ignored, even though they are more likely to be dan-
gerous. Novel food items were a recurrent but unpredictable feature of the rat’s
EEA, with the result that the animal has evolved adaptations that minimize the
cost of sampling this novelty. Rats will also preferentially eat novel food that they
have smelled on the breath of another rat (Galef, 1987), thereby minimizing the
danger of trial-and-error learning and demonstrating the utility of specialized
social learning mechanisms that evolved to adapt to recurrent problems involv-
ing specific sources of novelty.
A similar claim has been made by Geary and Huffman (2002), who argue
for the existence of soft modules, which are mechanisms that allow animals to
Chiappe & MacDonald25
process both variant and invariant information in particular domains. Soft mod-
ules provide strong, genetically programmed constraints on how specific types of
information are to be processed and are referred to as the module’s exoskeleton.
At the same time, they provide malleable internal structures that allow for learn-
ing in response to unique environmental inputs. These allow animals to acquire
adaptively relevant information by accommodating within-category variability.
For example, the exoskeleton of the face recognition system specifies certain
invariant cues that are used to determine whether or not a stimulus is a face. At
the same time, the soft internal structures process variability within the domain
of faces and allow us to discriminate between individuals on the basis of facial
There are, no doubt, many recurrent but contingent aspects of an animal’s
microenvironment that must be learned. This learning may be best performed by
specialized learning mechanisms, soft modules that allow for rapid and efficient
learning of specific types of information. A paradigmatic example for humans is
language, where the language acquisition device is specialized to learn the lan-
guage spoken around the child. Although the language acquisition mechanism
provides many strong, genetically programmed constraints, it is also sensitive to
localized information (Geary & Huffman, 2002; Pinker, 1994). The language
acquisition device makes learning any human language an effortless task, where-
as the task is impossible for animals that are not so equipped.
There certainly are mechanisms facilitating learning certain types of recur-
rently important information. However, it does not follow that the language acqui-
sition device or other “learning instincts” (Tooby & Cosmides, 1992, p. 113)
should be viewed as a general paradigm for all human learning—that human learn-
ing is always the result of domain-specific systems that evolved to preferentially
learn certain types of information. Language acquisition is more the exception than
the rule in human learning. Unlike social learning and associative learning, there
is a critical period for language during which it is most efficient (Pinker, 1994;
Spelke & Newport, 1998). Moreover, the capacity to acquire language can be
selectively impaired. Children with specific language impairment have normal
intelligence, but their ability to acquire language is disrupted (Pinker). However,
not all forms of learning can be selectively impaired, which suggests that at least
some learning mechanisms apply to a wide range of domains.
Learning novel cause-effect relationships is an important type of learning in
the natural world. Pavlovian conditioning allows animals to make opportunistic
associations between local, transient events that are not recurrent in their EEA. In
some cases, these associations recur sufficiently often to result in evolved biases,
as in taste aversion learning in rats (e.g., Garcia & Koelling, 1966; Rescorla,
1980). However, we maintain that by using a wide range of stimuli, animals are
The Journal of General Psychology
able to opportunistically satisfy evolved goals by making novel associations, as
in Pavlov’s dogs learning that the sound of a bell would be followed by food—
not a recurrent contingency in the animals’EEA.
It is useful to distinguish between domain-specific and domain-general mech-
anisms of classical conditioning. Domain-specific mechanisms rely on evolved
connections between specific unconditioned stimuli (UCSs) and specific classes
of stimuli, as in taste aversion learning in rats. In contrast, domain-general classi-
cal conditioning is designed to detect transient, locally true associations among
any detectable stimuli using general (and fallible) rules of thumb that rely on very
broad, general features of the environment. The main general predictors are con-
tiguity (including temporal order and temporal contiguity) and contingency (reli-
able succession). These predictors reflect that causes are reliable predictors of their
effects, that causes precede their effects, and that in general, causes tend to occur
in close temporal proximity to their effects (Revulsky, 1985; Staddon, 1988).
Causes that are temporally far removed from their effects are difficult to detect,
and the temporal contiguity of cause and effect is a general feature of the world.
The fact that there are exceptions, as in taste aversion learning, where noncon-
tiguous causes have a special status because of the evolutionary history of the ani-
mal, does not detract from the general importance of temporal contiguity. From
the animal’s perspective, in the absence of such a prepared association, the best
default condition is to suppose that causes precede the UCS and are temporally
contiguous. While temporal contiguity is neither a necessary nor a sufficient con-
dition for associating events, in general it is a main source of information on
causality (Shanks, 1994).
The general learning mechanism is also designed so that several pairings of
stimuli for which there are no evolved linkages provide more information than
does one pairing, that repeated occurrences of UCSs in the absence of a particu-
lar stimulus make it unlikely that the UCS will be paired with that stimulus (UCS
habituation), that the physical intensity of a stimulus increases its likelihood of
being framed as a conditioned stimulus (CS), that repeated nonreinforcement of a
previously reinforced association will lead to extinction,and that animals are more
likely to forget associations with greater intervals of time, which makes adaptive
sense because environments are continuously changing (Revulsky, 1985).
Humans and animals are able to make associations between a wide range of
stimuli in probing for causal relations (Dickinson, 1994; Shanks, 1994). Because
the world is not an entirely predictable place, there is no reason to suppose organ-
isms would be restricted to mechanisms designed to find causal relationships
between specific sources of recurrently connected events. Domain-general mech-
anisms designed to opportunistically detect associations among any discriminable
stimuli would be of obvious advantage, and available evidence indicates that such
mechanisms have evolved.
Exactly how humans and animals detect associations remains in dispute, but
the mechanisms certainly do not appear to be restricted to highly delimited sets
Chiappe & MacDonald27
of inputs recurrently linked to specific goals (UCSs) in the EEA. Mechanisms of
classical conditioning probe the world for novel contingent relationships between
experienced stimuli and evolutionarily important UCSs—relationships that may
be transient and only locally true. These mechanisms imply at least some evolved
machinery. For example,Gallistel (1990,1994,2000) proposed that classical con-
ditioning in animals derives from an evolved foraging mechanism specialized to
compute associations between stimulus conditions and rates of reinforcement and
is sensitive to temporal alterations in the contingency between stimulus condi-
tions and reinforcement. The mechanism detects predictive relationships between
a wide range of stimuli and a UCS. Thus, it solves problems that are multivari-
ate (i.e., many different events can predict the UCS), nonstationary (i.e., the con-
tingencies between CSs and UCSs can change at any time), and arrayed in a time
series (i.e., learning the temporal dependence of one event on another). Human
associative learning is also multivariate: We are highly sensitive to contingency
among a wide range of arbitrarily chosen stimuli (including color patches, dot
patterns,schematic faces,slides of skin disorders,pseudowords,phrases,and arti-
ficial grammars), and between actions (e.g., key pressing) and arbitrarily chosen
outcomes (e.g., Shanks, 1994).
At a functional level, such a learning mechanism is an adaptation as we have
defined it: It is a system of inherited and reliably developing properties designed
by natural selection to solve an adaptive problem; in this case, the need to adapt
to constantly changing, locally true information. It is a domain-general adapta-
tion because it functions to adapt the animal to nonrecurrent, constantly chang-
ing relationships between all available stimuli that signal the occurrence of other
stimuli, including those that serve as cues for satisfying evolved goals (UCSs).
The mechanism is domain-general and unencapsulated because the stimuli linked
to reinforcement (i.e., the CSs) are any stimulus detectable by the animal; there
is no proprietary database, except in the vacuous sense, that any stimulus avail-
able to the animal may be linked to reinforcement.
Learning and Fear
The fear system in humans and animals provides a good illustration of the
intertwining of domain-specific with domain-general learning mechanisms. Cer-
tain stimuli recurrently associated with danger in the EEA are particularly easy to
acquire and difficult to extinguish (e.g.,Öhman & Mineka,2001; Seligman,1971).
The fear system is therefore selective in its inputs because some stimuli more eas-
ily induce fears: “Evolutionary contingencies moderate the ease with which par-
ticular stimuli may gain control of the module” (Öhman & Mineka, p. 488).
However, other stimuli can gain control of the fear system. The adaptive-
ness of domain-general aspects of the fear system can be seen from data show-
ing that when the UCS is highly aversive or when a CS without any evolution-
ary significance is known to be very dangerous, the differences between
The Journal of General Psychology
evolutionarily primed fears and nonevolutionarily primed fears disappear. Thus,
pointed guns are a very potent stimulus for fear in our culture saturated with
media reports and dramatizations of shootings, with the result that guns activate
the fear system in a manner indistinguishable from evolutionarily prepared stim-
uli like snakes and spiders. Hugdahl and Johnsen (1989) found the stimulus of
a gun pointed at the participant followed by a loud noise showed superior con-
ditioning compared with slides of snakes. Moreover, the CS of the gun pointed
at the participant was nearly identical in extinction rate to a snake pointed at the
participant when both were followed by a shock. The results indicated that pro-
longed experience with stimuli such as pointed guns associated with intensely
aversive outcomes eventually leads to enhanced connections in the amygdala
that “function like evolutionarily prepared associations” (Öhman & Mineka,
2001, p. 513). Similarly, Sutton and Mineka (as cited in Öhman & Mineka) did
not find a covariation bias for images of a knife-wielding male dressed in black
under normal, nontraumatic conditions but did find a covariation bias similar to
that for evolutionarily prepared stimuli among students primed by real-life
reports of local knife-wielding criminals and a stabbing on campus.2In this case,
fear of a person wielding an item that was not an evolutionary danger produced
the sort of bias typically found with evolved fears. Similarly, Lautch (1971)
found that intense trauma could result in phobias even toward normally benign
objects with no evolutionary prepotency.
Domain generality is also implied by data indicating there are two different
learning systems relevant to fear in animals and humans (LeDoux, 1996; Öhman
& Mineka, 2001). The inputs to the amygdala fear system include prepared con-
nections between fear responses and evolutionarily recurrent stimulation,
although, as we have seen, nonprepared stimuli also have access to the system.
The domain-general associative learning system in the hippocampus is activated
in attempts to link any and all available stimuli to aversive UCSs, including a
range of contextual stimuli. Öhman and Mineka suggest this system typically
functions in novel and unnatural situations typical of laboratory studies on ani-
mals in which the aversive UCS is very motivating and in which picking up any
and all available information on cues related to the appearance of the UCS may
be vital. With humans, Campbell, Sanderson, and Laverty (1964) found a long-
lasting conditioned fear response to an arbitrary tone CS following a single trau-
matic event involving the suspension of breathing because of a drug injection.
There was no extinction even at a follow-up 3 weeks after the experiment. The
drug did not cause pain, but the experience was described by participants as
“extremely harrowing” (p. 632).
In conclusion, the fear system fails to qualify as a module because stimuli
with no phylogenetic importance can serve as CSs for activating the system, espe-
cially if they are intensely aversive. Domain-general learning mechanisms of clas-
sical conditioning function both in mildly aversive situations without the involve-
ment of the amygdala fear system and in intensely aversive situations with
Chiappe & MacDonald29
involvement of the amygdala fear system, where any and all available informa-
tion on contingency is of critical adaptive importance. Nor is the system encap-
sulated, because domain-general cognitive mechanisms are able to influence the
effectiveness of UCSs in producing fear CRs. Experience with dangerous objects
also influences expectations that such objects will have aversive effects and
results in stronger fear CRs that are more resistant to extinction.
Instrumental conditioning allows animals to opportunistically assess the
effects of their own behavior. An animal without the ability to learn contingen-
cies between its actions and their consequences would have to rely on evolved
connections between specific stimuli and specific behaviors. Such a strategy
would suffice in a stable, predictable world but would prevent animals from being
able to opportunistically take advantage of novel, serendipitous, and nonrecurrent
contingencies between its behavior and the satisfaction of evolved goals; such
animals would not be able to alter their behavior in situations in which the goal-
related consequences of behavior vary. For example, in laboratory studies, behav-
ior that satisfies the evolved goal state of hunger is strengthened when rats learn
to push up and down on levers to obtain food. Levers useful in obtaining food are
not a part of the animals’EEA, but rats are designed to be able to take advantage
of novel, serendipitous associations between their behavior and the availability of
food. Animals are able to perform a wide range of behaviors that are not species-
typical foraging behaviors to satisfy their evolved goal of assuaging hunger. Sim-
ilarly, humans, to the great benefit of TV programmers, can be induced to do vir-
tually anything within the realm of what is physically possible for them if it
results in rewards. Organisms that are unable to take advantage of such novel con-
tingencies—contingencies not recurrently present in their EEAs—would clearly
be at a disadvantage.
Natural selection has sometimes resulted in certain default activities occur-
ring as prepotent responses to situations of reward or danger (Staddon, 1988). For
example,raccoons wash any small object that is strongly associated with food,and
pigeons tend to peck anything strongly associated with food. Natural selection has
also operated to make certain operants easier to learn than others. For example,
bees more easily learn to switch their nectar gathering behavior to new flowers
(switch learning) and find it difficult to learn to return to the same flower that pre-
viously had nectar (stay learning)—presumably a reflection of evolutionary pres-
sures linking a particular behavior and a particular goal (Cole, Hainsworth, Kamil,
Mercier, & Wolf, 1982). This situation represents a conflict between evolved link-
ages between action and inference versus general cues of contingency and tem-
poral contiguity for engaging in actions that result in reward. The interesting point
is that bees are able to master the stay learning condition eventually; eventually,
the domain-general mechanism overrides the domain-specific mechanism. In the
The Journal of General Psychology
absence of evolved biases, the domain-general instrumental conditioning mecha-
nism is able to take advantage of novel, serendipitous associations between the
animal’s behavior and various rewards and punishments. The best general cues for
this are contingency and temporal contiguity (Staddon, 1988).
Tooby and Cosmides (1992, p. 95) claimed that support for domain gener-
ality in learning relies on data from “experimenter-invented, laboratory limited,
arbitrary tasks.” They criticized traditional learning experiments for not focus-
ing exclusively on ecologically valid, natural tasks—tasks that deal with prob-
lems that were recurrent in the animal’s EEA. We agree that investigations of
such tasks are likely to reveal specialized learning mechanisms in some cases.
However, an equally remarkable aspect of learning is that pigeons can learn to
peck keys to satisfy their evolved goals of staving off hunger and eating tasty
foods. Although pecking for food is undoubtedly a species-typical behavior for
pigeons, they, like rats learning to push levers, are also able to learn a variety of
arbitrary, experimenter-contrived behaviors that are not components of the ani-
mal’s species-typical foraging behavior. In other words, they are able to solve a
fundamental problem of adaptation (getting food) in a novel and even arbitrary
environment that presents few, if any, of the recurrent associations between the
animal’s behavior and obtaining food experienced in the animal’s EEA. Simi-
larly, humans are able to learn lists of nonsense syllables—another example
highlighted by Tooby and Cosmides, despite the fact that learning such lists was
not a recurrent problem in the EEA. People can learn such lists because their
learning mechanisms can be harnessed to new goals, as in getting course credit
as a participant in a psychology study.
In general, we hypothesize that neither operant nor classical conditioning
evolved to exclusively link specific events or behaviors recurrent in the EEA.
The mechanisms underlying these abilities imply a great deal of evolved
machinery, and there are important cases in which evolution has shaped learn-
ing in ways that depart from domain generality. Nevertheless, there is no pro-
prietary database for garden-variety examples of operant and classical condi-
tioning; nor is there evidence the information available to these mechanisms is
typically encapsulated. In general, there is no characteristic input to these sys-
tems, because the input to associational mechanisms of rats and humans verges
on whatever is detectable by the sense organs, and operant behaviors span vir-
tually the range of physically possible motor behaviors. Because of their
domain generality, these mechanisms allow humans to solve problems with
Social learning is also a domain-general adaptation. It occurs not only among
humans but also among many birds and mammals. For example, rats can learn
new means of obtaining food rewards by observing conspecifics (Heyes, Daw-
Chiappe & MacDonald31
son, & Nokes, 1992). Terkel (1996) showed social learning of a method for open-
ing pine cones allowed the Black Rat (Rattus rattus) to occupy a new ecological
niche, an illustration of the utility of learning for adapting to novel opportunities
not characteristic of the animal’s EEA. Most social learning among animals func-
tions to improve foraging efficiency by allowing animals to take advantage of
novel but transient information rather than to create cultural traditions between
generations (Laland, Richerson, & Boyd, 1996). Reader and Laland (2002) have
provided evidence that among primates, social learning coevolved along with
increased size of the executive functions, tool use, and ability to innovate. These
findings are compatible with the supposition that social learning becomes increas-
ingly important as animals are able to discover innovative solutions via the
processes underlying general intelligence.
There are a variety of methods for the social transmission of information,
ranging from social facilitation (learning facilitated by the presence of a con-
specific) to true imitation (one animal copies another’s specific behavior and
the behavior is not reinforced and not in the natural repertoire of the observer;
Zentall, 1996). Moore (1996) showed that parrots are able to socially learn,
without reinforcement, a wide range of behaviors that are not part of their
species-typical repertoire. Although there are controversies about the extent to
which nonhuman primates are able to exhibit true imitation (see Tomasello,
1996), there is no question they are able to acquire new behaviors from social-
ly transmitted information without reinforcement. Great apes are able to imi-
tate a wide range of behaviors (e.g., using a hammer or a paint brush) modeled
by people (Whiten & Ham, 1992). Human infants, at least beyond 1 year of age,
are “imitative generalists” who “imitate a wide variety of acts in varied situa-
tions. Facial, manual, vocal, and object-related imitation has been documented;
familiar and novel acts are imitated; both immediate and deferred imitation
occurs; imitation can take place in the original setting or be transferred to novel
contexts” (Meltzoff, 1996, p. 361).
Tooby and Cosmides (1992, p. 118) acknowledged the importance of social
learning that results in “a large residual category of representations or regulatory
elements that reappear in chains from individual to individual—‘culture’ in the
classic sense.” However, social learning tasks “would be unsolvable if the child
did not come equipped with a rich battery of domain-specific inferential mecha-
nisms, a faculty of social cognition, a large set of frames about humans and the
world drawn from the common stock of human metaculture, and other specialized
psychological adaptations designed to solve the problems involved in this task”
(Tooby & Cosmides, p. 119).
There is no question that social learning requires a great deal of evolved
machinery, but this is insufficient to establish social learning as domain specific.
To be interesting, the argument must entail that the content of what is learned is
evolutionarily circumscribed. We acknowledge the importance of evolved
machinery, including evolved frames in social learning tasks (see hereinafter).
The Journal of General Psychology
However, this does not imply the learning system evolved to solve a particular,
highly discrete problem recurrent in the EEA. Nor is there evidence for a propri-
etary database for social learning, or that the information available to social learn-
ing mechanisms or transmitted by social learning mechanisms is restricted to a
specific set of messages important for adaptation in the EEA. Social learning sys-
tems in humans are domain general in the critical sense that they allow us to ben-
efit from the experience of others, even when their behavior was not recurrently
adaptive in the EEA but is effective in achieving evolved goals in the current envi-
ronment. This shows the importance of social learning in adapting to the con-
There are important evolved mechanisms guiding human social learning in
adaptive ways. Parent–child affection channels children’s social learning within
the family (MacDonald 1988, 1992, 1997). The human affectional system is
designed to cement long-term relationships of intimacy and trust by making them
intrinsically rewarding (MacDonald, 1992). A continuing relationship of warmth
and affection between parents and children is expected to result in the acceptance
of adult values by the child, identifying with the parent, and a generally higher
level of compliance—“the time-honored concept of warmth and identification”
(Maccoby & Martin, 1983, p. 72). The finding that warmth of the model facili-
tates imitation and identification has long been noted by social learning theorists
(e.g., Bandura, 1969). Besides the framing effect of warmth, evolution has also
shaped children’s preferences for other features of models, such as dominance,
high social status, and similarity (MacDonald, 1988).
For humans, the types of behaviors that can be successfully transmitted by
social learning are not limited to discrete sets of behaviors useful to meeting the
recurrent challenges of the EEA. They are limited only by general cognitive and
motor limitations: limitations on the informational complexity of modeled
behavior, limits on attentional processes and memory, and limitations on human
motor abilities (Bandura, 1969; Shettleworth, 1994). Even among rats, Kohn and
Dennis (1972) found that animals that were able to observe other rats solve a
discrimination problem (and thus avoid shock) were quicker to learn this dis-
crimination than rats that were prevented from the opportunity to observe. The
patterns that were discriminated were entirely arbitrary and in no sense elements
of the EEA. The response pattern involved motor activity to escape the shock by
going through the appropriate door. The mechanism therefore was not domain
specific: It was not triggered by a highly delimited stimulus recurring in the EEA
and it did not result in a highly discrete response designed specifically to deal
adaptively with this problem.
In short, specialized learning mechanisms are only part of the story of human
and animal learning. There are many nonrecurrent events that are learnable with-
out specialized mechanisms, and being able to learn them is adaptive. Apart from
well-known examples in which learning is highly biased, learning mechanisms
are domain general.
Chiappe & MacDonald33
Evolutionary psychology has been of great value in placing evolutionary
thinking at the center of cognitive science. However, by erecting an equally one-
sided paradigm in opposition to the standard social science model, it runs the risk
of overemphasizing modularity and ignoring the vast data indicating a prominent
role for domain-general mechanisms in human and animal cognition. As
described here,domain-general mechanisms are not weak “jacks-of-all-trades but
masters of none.” They are extremely powerful but fallible mechanisms that are
the basis for solving a fundamental problem faced by all but the simplest organ-
isms—the problem of navigating constantly changing environments that present
new challenges that have not been recurrent problems in the EEA. Most impor-
tant, the domain-general mechanisms at the heart of human cognition are respon-
sible for the decontextualization and abstraction processes critical to the scien-
tific and technological advances that virtually define civilization.
The processes discussed here are not meant to be an exhaustive examination
of domain generality in cognition and learning, but merely illustrative. We sup-
pose that a great many other processes will yield to the type of analysis presented
here, including other forms of reasoning and induction besides analogical reason-
ing, memory and categorization, developmental plasticity, and large areas of per-
sonality psychology in which, as in the analysis of the fear system presented
heretofore, there is a complex interplay between evolved emotional responses to
specific stimuli as well as the ability to recruit emotional systems to confront
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1. An example of if-only thinking: Who is more foolish, a man who sat tight when the stock he want-
ed to invest in went up, or the man who sold some other stock to buy the losing stock? (see Stanovich
& West, 1998).
2. In covariation bias studies, participants judge the extent to which there is a covariation between
fear-relevant stimuli and aversive outcomes. Typical findings are that there is a bias such that people
overestimate the extent to which evolutionarily significant stimuli are associated with aversive out-
comes (e.g., Tomarken, Mineka, & Cook, 1989).
Manuscript received January 20, 2004
Accepted for publication May 25, 2004
The Journal of General Psychology
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