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Evolution and Children’s Cognitive and Academic Development

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Children’s extraordinarily long developmental period and their play and exploratory activities provide them with a greater opportunity to learn than is found in any other species. We argue that these developmental activities co-evolved with attentional, cognitive, and motivational biases to learn some types of information but not others. These biases support a universal cognitive development that results in the fleshing out of inherent skeletal knowledge that is organized around other people and social relationships (folk psychology), other species (folk biology), and the physical world (folk physics). In contrast, academic development, as in reading, writing, and arithmetic, occurs in some cultures and not others and largely in formal educational settings. The development of academic competencies does not come as easily to children as the development of folk abilities. Evolutionary educational psychology is focused on understanding how children’s evolved cognitive and motivational biases can help or hinder the learning of formal academic skills and provides a framework for designing educational interventions to facilitate this learning.
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Chapter Title Evolution and Children’s Cognitive and Academic Development
Copyright Year 2016
Copyright Holder Springer International Publishing Switzerland
Corresponding Author Family Name Geary
Particle
Given Name David C.
Suffix
Division
Organization/University University of Missouri
City Columbia
State MO
Country USA
Email GearyD@Missouri.edu
Author Family Name Berch
Particle
Given Name Daniel B.
Suffix
Division
Organization/University University of Virginia
City Charlottesville
State VA
Country USA
Email dberch@virginia.edu
Abstract Children’s extraordinarily long developmental period and their play and
exploratory activities provide them with a greater opportunity to learn than
is found in any other species. We argue that these developmental activities
co-evolved with attentional, cognitive, and motivational biases to learn some
types of information but not others. These biases support a universal cognitive
development that results in the fleshing out of inherent skeletal knowledge that
is organized around other people and social relationships (folk psychology),
other species (folk biology), and the physical world (folk physics). In contrast,
academic development, as in reading, writing, and arithmetic, occurs in
some cultures and not others and largely in formal educational settings. The
development of academic competencies does not come as easily to children
as the development of folk abilities. Evolutionary educational psychology is
focused on understanding how children’s evolved cognitive and motivational
biases can help or hinder the learning of formal academic skills and provides
a framework for designing educational interventions to facilitate this learning.
Keywords (separated
by “ - ”)
Evolution - Development - Cognition - Cognitive development - Academic
learning - Intelligence - Folk abilities - Education
217© Springer International Publishing Switzerland 2016
D.C. Geary, D.B. Berch (eds.), Evolutionary Perspectives on Child Development
and Education, Evolutionary Psychology, DOI 10.1007/978-3-319-29986-0_9
Chapter 9
Evolution and Children’s Cognitive
and Academic Development
David C. Geary and Daniel B. Berch
Natural selection is the unifying theory for all of the life sciences and one of
humanity’s most important scientific accomplishments (Darwin, 1859). As living
organisms, human behavior, cognitive biases, and other traits are necessarily a
reflection of the survival and reproductive pressures experienced by our ancestors,
and as such, the study of the here-and-now development and expression of these
traits can be situated in an evolutionary context. This is not to say that social context
does not influence human behavior; it does. Rather, a deep understanding of how
evolution works will provide insights into human behavior and development that are
not fully achievable from other theoretical perspectives. Unfortunately, the power of
evolutionary theory has not been fully appreciated by many psychologists or social
scientists more generally, with of course the exceptions represented in this volume
and a few others. In this chapter, we examine cognitive and academic development
from an evolutionary perspective to provide a cohesive framework for understand-
ing children’s ability and motivation to learn evolutionarily novel competencies in
modern schools, such as reading, writing, and arithmetic.
Cognitive Development
A complete understanding of any trait requires evolutionary analysis on four levels,
as outlined by Tinbergen (1963): The ultimate selection pressures that resulted in
the evolution of the trait; the function of the trait in terms of increasing survival
D.C. Geary (*)
University of Missouri, Columbia, MO, USA
e-mail: GearyD@Missouri.edu
D.B. Berch
University of Virginia, Charlottesville, VA, USA
e-mail: dberch@virginia.edu
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prospects; the proximate, reductive mechanisms that support the here-and-now
operation of the trait; and the development of the trait. As Tinbergen noted, “All
concerned agree that a complete understanding of behavior requires an understand-
ing of its ontogeny, just as morphologists agree that it is not sufficient to understand
the adult form, but also the way in which this develops during ontogeny” (Tinbergen,
1963, p. 423). Our focus is on development, specifically aspects of children’s cogni-
tive development that are likely to be universal and the experiences and mechanisms
that support this development. One cannot actually study development without first
determining or at least speculating as to what it is that develops. We do this in the
next two sections and then move to a discussion of how cognition develops in chil-
dren and finally the evolution and function of the domain general abilities of work-
ing memory and fluid intelligence.
Function of Mind and Brain
Evolution shapes brains and minds such that they are biased to attend to and process
the classes of information that were correlated with survival and reproductive out-
comes during the species’ evolutionary history. Brains and minds also organize
behavior toward the achievement of these outcomes, which Geary (2005) described
as a “motivation to control.” This is not an explicit motivation, but rather a heuristic
that allows us to more easily understand the function of behavior. Consider as an
example the well-documented differences in beak size and shape across the many
species of finch that reside on the Galapagos islands (Darwin, 1845; Grant, 1999),
as shown in Fig. 9.1. These reflect differences in species’ specialization in different
types of food, such as smaller or larger seeds. When combined with a bias to attend
to the appropriate seeds and engage in associated foraging behaviors (e.g., cracking
open seed shells), these physical traits allow the birds to gain control of these foods.
Having birdbrains, they of course have no explicit awareness of what they are doing
or an explicit motivation to control. This heuristic nevertheless allows one to readily
see how these perceptual, behavioral, and physical traits coevolved because they
enable successful seed foraging or more abstractly successful resource control.
The developmental period is an evolved trait in and of itself, and any lengthening
of this period necessarily results in delayed reproduction. The costs of delayed
reproduction generally include fewer offspring during the reproductive lifespan and
elevated risk of dying before having the opportunity to reproduce at all. An extended
period of immaturity must therefore result in cognitive, behavioral, or social changes
that enhance resource control in adulthood. Bjorklund and Beers (this volume) refer
to these as deferred adaptations—skills that emerge over the course of development
that function to improve outcomes in adulthood—and this is our focus here; ontoge-
netic adaptations, those that enable developing organisms to negotiate specific
developmental tasks, are important as well but are not considered here (see Bjorklund
& Ellis, 2014). We begin by outlining broad classes of information, or folk domains,
that were likely important for survival and reproductive prospects during our
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evolutionary history, followed by a discussion of children’s behavioral and cogni-
tive biases and developmental changes in these competencies that likely enhanced
these prospects.
Folk Domains
All living organisms have to cope with the competing interests of members of their
own species, need to exploit (prey) and avoid being exploited by other species
(predators), as well as cope with the realities of the physical world. These classes of
information have also emerged in studies of children’s unschooled cognition and in
studies of unschooled adults in traditional populations and are often termed folk
psychology, folk biology, and folk physics, respectively (Atran, 1998; Geary, 2005;
Gelman, 2003; Leslie, Friedman, & German, 2004; Medin & Atran, 1999; Mithen,
1996; Wellman & Gelman, 1992). Folk domains represent universal forms of knowl-
edge and competencies that emerge from a combination of inherent cognitive biases
and evolutionarily expectant experiences. The latter results from self-initiated activ-
ities that give rise to experiences that in turn elaborate on inherent biases and flesh
out folk knowledge such that it is adapted to local conditions (Gelman, 1990;
Greenough, Black, & Wallace, 1987), as elaborated in Mechanisms. In Fig. 9.2, we
Fig. 9.1 Four species of finch from the Galápagos islands; (1) Large ground finch (Geospiza
magnirostris); (2) Medium ground finch (G. fortis); (3) Small tree finch (Camarhynchus parvulus);
(4) Warble finch (Certhidea olivacea) from Journal of researches into the natural history and geol-
ogy of the countries visited during the voyage of H.M.S. Beagle round the world, under the
Command of Capt. Fitz Roy, R.N. (2nd edition), by C. Darwin, 1845, London: John Murray, p. 379
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present a taxonomy of folk competencies and knowledge (Geary, 2005; Geary &
Huffman, 2002). Functionally, these abilities evolved because they allowed our
ancestors to focus their behavior on attempts to achieve access to and control of the
social (e.g., finding a mate), biological (e.g., food), and physical (e.g., control of
rich territory) resources that tended to enhance survival or reproductive prospects
during human evolution.
Folk Psychology
The evolution of this complex system of cognitive, emotional, and behavioral traits
was almost certainly driven by intense social competition and the cooperation that
often facilitates competitive ability (e.g., Alexander, 1989; Bailey & Geary, 2009;
Dunbar, 1998; Flinn, Geary, & Ward, 2005; Geary, 2005; Humphrey, 1976). This
constellation of traits allows people to negotiate social interactions and relation-
ships, and the corresponding social cognitions are largely organized around the self,
relationships, and interactions with other people, and group-level relationships (see
also Shaw, this volume; Hawley, this volume).
Self. Humans are very likely to be unique among species in their awareness of
their emotional and mental states and their ability to compare and contrast their
unobservable traits (e.g., personality, intelligence) with those of others.
Self- awareness is a conscious representation (in working memory) of the self as a
Fig. 9.2 Evolutionarily salient information-processing domains and associated cognitive modules
that compose the domains of folk psychology, folk biology, and folk physics. Adapted from “The
origin of mind: Evolution of brain, cognition, and general intelligence,” by D. G. Geary, 2005,
p. 129. Copyright 2005 by American Psychological Association
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social being and of one’s relationships with other people (e.g., Harter, 2006), and
may have been evolutionarily preceded by visual self-recognition (Butler &
Suddendorf, 2014). Self-awareness is tightly related to the ability to mentally time
travel; specifically to project oneself backward in time to recall and relive episodes
that are of personal importance and to project oneself forward in time to create a
self-centered mental simulation of potential future states (Suddendorf & Corballis,
1997; Tulving, 2002), as we elaborate in Variation and the Evolution of Domain
General Abilities. Self-schema is a long-term memory network of information that
organizes knowledge and beliefs about the self, including positive (accentuated)
and negative (discounted) traits (e.g., warmth), memories of personal experiences
(Fiske & Taylor, 1991; Markus, 1977), and self-efficacy—beliefs about one’s ability
to achieve a goal in various domains (Bandura, 1997). Self-schemas can regulate, at
least to some extent, goal-related behaviors; specifically, where one focuses effort
and whether or not one will persist in the face of failure.
Individual. Common one-on-one human relationships can be found across
societies, including attachment between a parent and a child and friendships
(Bugental, 2000; Caporael, 1997). Although there are emotional and motivational
differences across these relationships, they are all supported by the same suite of
folk competencies shown in Fig. 9.2, including the ability to read nonverbal com-
munication signals (e.g., body posture), facial expressions, language, and theory of
mind (Adolphs, 2003; Baron-Cohen, 1995; Brothers & Ring, 1992; Humphrey,
1976; Leslie, 1987; Pinker, 1994; Wellman, 2014: Wellman, Fang, Liu, Zhu, & Liu,
2006; Wellman, Fang, & Peterson, 2011; Wellman & Liu, 2004). Theory of mind
represents the ability to make inferences about others’ desires, beliefs, and emo-
tional states, and awareness that other people can differ on these. This is a set of
competencies that may be especially developed in humans (Leslie et al., 2004) and
are important in educational contexts (e.g., in students’ making inferences about the
intentions of teachers and teachers’ understanding of the beliefs of students; Gopnik
& Wellman, 2012). In any case, all of these competencies are engaged during the
dynamics of one-on-one social interactions and provide the functional competen-
cies needed to understand and modulate the dynamics of the interaction.
The integration of these cognitive systems with motivational and emotional sys-
tems provides the basis for the development and maintenance of long-term relation-
ships and the development of person schema. People develop these schemas for
familiar people and people for whom future social relationships are expected (Fiske
& Taylor, 1991). The schema is a long-term memory network that includes repre-
sentations of the other persons’ physical attributes, especially race, sex, and age, as
well as memories for specific behavioral episodes, and the same warmth and com-
petence traits associated with the self-schema (Schneider, 1973). This knowledge
allows people to better understand and predict the behavior of familiar others
(Kahneman & Tversky, 1982).
Group. In all cultures, people parse the world into social groups, largely in terms
of kinship, in-groups and out-groups, and group schema. An evolved bias to differ-
entially favor kin over nonkin is found in all species and should not be surprising
(Hamilton, 1964). In-groups and out-groups are constellations of people with whom
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one has shared interests and cooperative relationships and people with competing
interests, respectively; out-groups need not be competing groups, but the salience of
“our group” and “the other group” is more prominent during times of conflict (Fiske,
2002). In traditional societies, in-groups and out-groups are often determined by
kinship, but this is not always the case (e.g., Macfarlan, Walker, Flinn, & Chagnon,
2014). People have more positive attitudes and beliefs about members of their in-
group and more negative and often hostile attitudes and beliefs about members of
out-groups, especially when the groups are competing (Fiske & Taylor, 1991;
Hewstone, Rubin, & Willis, 2002; Horowitz, 2001). Group schema is an ideologi-
cally based social identification, as exemplified by nationality and religious affilia-
tion (Abrams & Hogg, 1990). These ideologies allow for the formation of larger
groups than would be possible based only on personal relationships. These large
cooperative groups are particularly advantageous during between-group conflicts,
given the competitive advantage that results from being a member of a large group
(Alexander, 1990).
Folk Biology
Analogous to species’ variation in beak size among Darwin’s finches, there are
species-specific brain, cognitive and behavioral specializations that enable the loca-
tion and manipulation (e.g., raccoons, Procyon lotor, cleaning of food) of edible
plants, fruits, and nuts, as well as the location and capture of prey species (e.g.,
Barton & Dean, 1993; Huffman, Nelson, Clarey, & Krubitzer, 1999). The folk bio-
logical competencies represent the most rudimentary cognitive specializations that
support humans’ ability to learn about, identify, and secure biological resources in
the wide range of ecological niches occupied by our species (Atran, 1998; Caramazza
& Shelton, 1998; Malt, 1995; Medin & Atran, 2004). These competencies emerge
from a combination of biases and experiences in the ecology and support hunting,
gathering, and horticulture in traditional societies.
There is both cross-cultural variation in the extent and organization of folk bio-
logical knowledge and a universal core. As a reflection of the latter, people through-
out the world are able to categorize the flora and fauna in their local ecologies and
show similar categorical and inferential biases when reasoning about these species
(Atran, 1998; Berlin, Breedlove, & Raven, 1966). Through the study of this knowl-
edge across traditional societies, “it has become apparent that, while individual
societies may differ considerably in their conceptualization of plants and animals,
there are a number of strikingly regular structural principles of folk biological clas-
sification which are quite general” (Berlin, Breedlove, & Raven, 1973, p. 214).
Bailenson, Shum, Atran, Medin, and Coley (2002) asked groups of novices and bird
experts from the United States and Itza’ Maya Amerindians (Guatemala) to classify
about 100 birds from their region and from the region of the other group. There were
similarities in the classifications of all three groups, as well as differences.
The classification system of US experts and the Itza’ Maya was more similar to the
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scientific taxonomy of these species than that of the US novices. For the Itza’ “their
consensual sorting agrees more with (western) scientific taxonomy than does the
consensual sort of US non-experts. This difference held for both US birds and Tikal
birds” (Bailenson et al., 2002, p. 24).
Bailenson et al.’s (2002) findings for novices are not unique; without sufficient
experience with the natural world (e.g., children living in modern urban areas), only
rudimentary aspects of folk biology develop (Medin & Atran, 2004). With sufficient
experience, people develop at least a three-level organization to their knowledge of
the biological world. The most general level—corresponding to the kingdom level
in the scientific classification—is shown in Fig. 9.2. People further subdivide flora
and fauna into classes of related species, including birds, mammals, and trees, and
then more specific species, such as bluebirds (Sialia) and robins (Turdus).
Knowledge of the species’ morphology, behavior, growth pattern, and ecological
niche (e.g., arboreal versus terrestrial) help to define the essence of the species
(Atran, 1994; Malt, 1995). The essence is a species schema, analogous to the person
schema, and includes knowledge of salient and stable characteristics (e.g., Medin
et al., 2006). This knowledge enables use of mental models of flora and fauna for
representing and predicting the likely behavior of these organisms (e.g., seasonal
growth in plants). The combination of folk biological categories, inferential biases,
and knowledge of the species’ essence allows people to use these species in evolu-
tionarily significant ways (Figueiredo, Leitão-Filho, & Begossi, 1993, 1997).
Folk Physics
Folk physics, as noted, enables organisms to negotiate the physical world, as in find-
ing food, shelter, or mates and avoiding potential threats (Dyer, 1998). Our inclusion
of movement and representation in Fig. 9.2 is based in part on Milner and Goodale’s
(1995) analysis of the functional and anatomical organization of the visual system.
They argue that the systems for movement and representation are functionally and
anatomically distinct, but interact. Indeed, there are examples of distinct visuomotor
pathways for a variety of movement-related functions, such as predator avoidance
and navigating around obstacles. Barton and Dean (1993), for instance, examined the
relations among the number and size of neurons in one specific visual pathway and
predatory behaviors within four groups of mammals, Rodentia, Primates, Carnivora,
and Marsupials. Within each of these groups, species were classified as more (i.e.,
diet heavily based on prey capture) or less (e.g., heavy reliance on fruits) predatory.
Predatory species had more and larger neurons in this visual pathway than did their
less predatory cousins, but there were no cross- species differences in the volume of
adjacent visual pathways not related to prey capture.
The search for prey, shelter, and other resources requires systems for navigating
in three-dimensional space (Gallistel, 1990; Shepard, 1994). Studies of a variety of
mammalian species reveal that organisms have egocentric and allocentric views of
this space (Byrne, Becker, & Burgess, 2007; Maguire et al., 1998). The egocentric
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representation is what the organism sees, including objects and locations with
respect to itself (Byrne et al., 2007). The allocentric system codes for large-scale
geometric relations and positioning of objects in space independent of the organism.
Both systems work conjointly to enable the organism to remain oriented and goal-
focused while moving in space. The allocentric representation may result in an
implicit three-dimensional analog map that codes the geometric relations among
features of the environment and enables navigation by means of dead reckoning;
movement from one place to another on the basis of geometric coordinates (Gallistel,
1990). Human navigation involves both the egocentric and allocentric systems, but
for different aspects of navigation (Byrne et al., 2007). A few species, especially
humans, can also generate explicit cognitive representations of egocentric and allo-
centric physical space in working memory (Kuhlmeier & Boysen, 2002).
Tool use is found in one form or another in all human cultures and enables people
to more fully control biological resources in the local ecology (Murdock, 1981).
The neural, perceptual, and cognitive systems that support tool use have not been as
systematically studied as the systems that support movement and representation in
space. On the basis of brain imaging studies and cognitive deficits following brain
injury, Johnson-Frey (2003) concluded that homologous brain regions are involved
in basic object grasping and manipulation in humans and other primates. At the
same time, it is clear that humans have a much better conceptual understanding of
how objects can be used as tools (Pellegrini, this volume; Povinelli, 2000), and their
definition of how these objects can be used is influenced by the inferred intentions
of potential tool users (Bloom, 1996). At the core, human tool use involves the abil-
ity to mentally represent an object as a potential tool, to manipulate this mental
representation to explore the different ways in which the object might be used, and
finally to integrate such representations with active tool use (Lockman, 2000;
Pellegrini, this volume).
Finally, we have classified organisms’ intuitive sense of time and number—for
instance the approximate quantity of food available in two distinct foraging
patches—as an aspect of folk physics. These competencies support the representa-
tion and discrimination of the exact quantity of small collections of items and the
approximate quantity of larger collections or continuous quantities (e.g., area;
Feigenson, Dehaene, & Spelke, 2004; Geary, Berch, & Mann Koepke, 2015) and
appear to be integrated with systems for representing the passage of time (Meck &
Church, 1983).
Folk Heuristics and Attributional Biases
The behavioral features of folk domains can be described as “rules of thumb”
(Gigerenzer, Todd, & ABC Research Group, 1999). The information to which the
folk systems are sensitive is processed implicitly and the behavioral component is
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more or less automatically executed (Simon, 1956), although people have the ability
to override these if necessary (below). Barton and Dean’s (1993) analysis of the
visuomotor pathway in mammals as related to prey-capture illustrates the point.
Cells in this system are likely to be sensitive to the movement patterns of prey spe-
cies and enable the coordination of these perceptions with behaviors necessary to
capture this prey. The organization of this integrated neural system results in built-in
attentional and perceptual biases and an implicit understanding of how to catch
prey. Competence at prey capture is likely to improve with experience, including
play behavior during development for many species of mammal, but the foundation
is built-in (Burghardt, 2005).
The same applies to folk knowledge more generally. For instance, during face
processing the pattern generated by the shape of the eyes and nose provides infor-
mation about the sex of the individual, whereas the pattern generated by the con-
figuration of the mouth provides information about the individual’s emotional state
(Schyns, Bonnar, & Gosselin, 2002). The receiver automatically and implicitly pro-
cesses this information, and in turn, expresses corresponding emotional and other
social signals (e.g., smile) as appropriate. The receiver may also make implicit deci-
sions regarding the interaction, but these do not need to be explicitly represented in
working memory and made available to conscious awareness (see below). These
quick, rule-of-thumb decisions can be based on automatically generated feelings
and other social information. Angry facial expressions, for example, often generate
fear and behavioral avoidance and can do so in a matter of seconds (Damasio, 2003).
Explicit inferential and attributional biases are also features of folk heuristics, as
least for humans. People often attribute their failures to achieve desired goals, for
instance, to bad luck or biases in other people. The tendency to make attributions of
this type has the benefit of maintaining effort and control-related behavioral strate-
gies in the face of inevitable failures (Heckhausen & Schulz, 1995). Social attribu-
tional biases that favor members of the in-group and derogate members of out-groups
are well-known (Fiske, 2002) and facilitate intergroup competition (Horowitz,
2001). The folk-biological essence allows people to make inferences (e.g., during
the act of hunting) about the behavior of members of familiar species and about the
likely behavior of less familiar but related species (Atran, 1998). Attributions about
causality in the physical world are also common (Clement, 1982).
From an educational perspective, it is important to note that these biases may
provide good enough explanations for day-to-day living and self-serving explana-
tions for social and other phenomena. However, an evolved functional utility in terms
of everyday living in traditional settings does not mean the explanations are neces-
sarily scientifically accurate, as aptly described by Sinatra and Danielson (this vol-
ume; also Shtulman, 2006). In fact, descriptions of psychological, physical, and
biological phenomena are often correct (Wellman & Gelman, 1992), but many of the
explicit explanations of the causes of these phenomena are objectively and scientifi-
cally inaccurate. People can, for instance, describe the trajectory of a thrown object,
but they do not understand the forces that produce this motion (Clement, 1982).
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Mechanisms
Given the wide range of ecological and social niches occupied by humans, it is
unlikely that most folk knowledge is “prepackaged” either fully or unfolds in a pre-
determined manner across development. Rather, the extent of inherent constraint or
developmental plasticity might vary with the temporal and spatial variability in the
associated ecological or social pressures and attendant information patterns that
need to be processed to cope with these (Geary, 2005; Geary & Huffman, 2002).
These tradeoffs are represented in Fig. 9.3. Strong neurobiological and cognitive
Fig. 9.3 The triangle represents the relation between inherent constraint and the influence of
developmental experience on brain organization and cognitive functions. The length of the line
segments with arrows represents the corresponding degree of plasticity. The area above (no inher-
ent constraint) and below (no plasticity) the dashed lines represents extreme views that few theo-
rists posit for humans. The rectangle highlights cost-benefit trade-offs that are predicted to
influence the evolution of brain and cognitive plasticity. Adapted from “Brain and cognitive evolu-
tion: Forms of modularity and functions of mind,” by D. C. Geary and K. J. Huffman, 2002,
Psychological Bulletin, 128, p. 668. Copyright 2002 by the American Psychological Association
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constraints direct attention and behavior toward evolutionarily significant information
patterns, and in doing so, reduce the number of false positives (Gelman, 1990).
The cost is reduced plasticity of these systems. Weak constraints increase the risk of
false positives, but result in enhanced system flexibility. The cost-benefit tradeoffs
should be modulated by the variability of associated information patterns.
The three-dimensional structure of the physical world results in stable informa-
tion patterns that would, in theory, result in the evolution of constrained systems for
detecting and acting on this information (Gallistel, 1990; Shepard, 1994). Of course,
movement in space creates variation in the information to which the organism is
exposed and advantages to systems for remembering location and for navigating,
systems that would be anchored by the more constrained folk physics systems
(O’Keefe & Nadel, 1978). Human social dynamics, in contrast, are necessarily
more dynamic and we would anticipate greater plasticity in folk psychological sys-
tems, but constraints are still needed. As noted, variation in facial expression pro-
vides dynamic information about the individuals’ current emotional state, but would
be missed if not anchored by attentional focus to specific facial features (Baron-
Cohen, 1995; Schyns et al., 2002).
In the context of the tradeoffs associated with constraint and plasticity, the devel-
opmental period provides opportunity to adjust folk systems to the nuances of the
local geography, flora and fauna, and social relationships (Geary, 2004; Geary &
Bjorklund, 2000). The mechanisms for adapting folk systems to local variation
must include behaviors that result in exposure to this variation, as we describe in the
first section. In the second, we provide discussion of the nature of the plasticity of
folk systems during cognitive development.
Behavioral. In theory, children’s self-initiated engagement of the ecological and
social contexts in which they are embedded provides the experiences needed to
flesh out the plastic components of folk domains (Bjorklund & Pellegrini, 2002;
Gopnik & Wellman, 2012; Greenough et al., 1987; Scarr, 1992). These behavioral
biases are common juvenile activities, including social play, exploration of the ecol-
ogy, and experimentation and play with objects (see also Bjorklund & Beers, this
volume; Lancy, this volume; Toub et al., this volume; Pellegrini, this volume).
A critical aspect of these experience-expectant processes is that they result in auto-
matic and effortless modification of plastic features of folk systems and implicit
knowledge.
An example is provided by infants’ early attentional and behavioral biases. They
attend to human faces, movement patterns, and speech in ways that reflect the inher-
ent organizational and motivational structure of the associated folk psychological
systems (Freedman, 1974). These biases evolved because of the evolutionary sig-
nificance of social relationships and result in the re-creation of the microconditions
(e.g., parent–child interactions) associated with the evolution of the corresponding
systems (Caporael, 1997). Attention to and processing of this information provides
exposure to the within-category variation needed to adapt the architecture of these
systems to variation in parental faces, behavior, and so forth (Gelman & Williams,
1998). In this example, these experience-dependent modifications allow infants to
discriminate the voice of their parents from the voice of other people with only
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minimal exposure. When human fetuses (gestation age of about 38 weeks) are
exposed in utero to human voices, their heart-rate patterns suggest they are sensitive
to and learn the voice patterns of their mother and discriminate her voice from that
of other women (Kisilevsky, 2003).
In some ways, the experience-dependent fleshing out of folk systems is similar
to the constructivist view of learning, but with a very important difference (Geary,
1995). A strict constructivist view does not discriminate learning in school from
elaboration of folk knowledge resulting from engagement in evolutionarily expect-
ant activities (see Gray, this volume). In our view, it does not follow that there are
inherent anchors and behavioral biases to guide the learning of algebra or most
other evolutionarily novel academic material in the same way there are anchors and
social biases that allow children to learn about their social world, for instance (see
Geary, 2007; Sweller, 2012, 2015, this volume). As we elaborate below, there may
well be a gray area in which evolutionarily expectant activities, such as play, can be
used to facilitate some aspects of biologically secondary (i.e., evolutionarily novel)
learning, as touched upon in Bjorklund and Beer’s (this volume) and Toub et al.s
(this volume) chapters. It does not follow, however, that all aspects of secondary
learning can be acquired in this way, and determining the strengths as well as limita-
tions of evolved behavioral biases in the promotion of secondary learning has pro-
found implications for how to improve educational outcomes.
Cognitive. Debate regarding the origins of human knowledge have spanned sev-
eral millennia and continue to this day (Carey, 2009; Gelman, 1990; Gelman, 2003;
Gopnik & Wellman, 2012; Newcombe, 2011; Spelke & Kinzler, 2007, 2009;
Spencer et al., 2009). There is some debate regarding whether or not human cogni-
tion and cognitive development is influenced by inherent constraints at all (Spencer
et al., 2009), but the focus of debate largely centers on the extent and nature of any
such constraints, in keeping with the tradeoffs between constraint and plasticity
noted above (Fig. 9.3).
There is agreement that inherent constraints are found for some domains but not
others; they are based on at least implicit concepts (e.g., of quantity or living vs. non-
living things) applied to categories of natural things (vs. man-made) and they support
inferences about causality related to these things. As an example, young children and
even infants discriminate between objects that produce self-generated movement, as
do living organisms, and those that only move when acted upon by some other object
(e.g., movement after being struck by another object) or a person. Moreover, they
implicitly infer that living things have causal intentions—infants and young children
behave as if they expect movement of living things to be directed toward a goal—and
that living things have “innards” that represent their “essence” (e.g., all elephants
have the same essence) and that enable goal-directed behavior (e.g., Gelman, 1990;
Gelman, 2003; Setoh, Wu, Baillargeon, & Gelman, 2013). Nonliving things do not
have an essence per se, but they do have physical properties, such as solidity (two
objects cannot occupy the same space at the same time), that infants appear to
implicitly expect (Spelke, Breinlinger, Macomber, & Jacobson, 1992).
There is also agreement that infants’ and young children’s early conceptual
constraints either become elaborated during development and with experience, or
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they are superseded by more powerful concepts, that is, concepts that provide a
more functional and accurate understanding of the organism or object (Carey, 2009;
Gelman, 1990; Gelman, 2003; Gopnik & Wellman, 2012; Spelke & Kinzler, 2007);
this does not necessarily mean that the original naïve concept disappears, as it may
exist alongside the new conceptual understanding. How to best represent initial con-
straints and associated conceptual change is debated, but regardless of the details
the key idea is that inherent constraints and concepts form the scaffolding for chil-
dren’s emerging understanding of natural things, that is, the physical world, other
species, and our own species. There is not yet a consensus on the extent of core
domains and their organization, but we believe the folk domains shown in Fig. 9.2
are a reasonable approximation.
In any case, an experience-dependent elaboration of nascent folk domains melds
easily with humans’ long developmental period (Bogin, 1999), and children’s self-
initiated behaviors described in the section above. In this view, behavioral and cogni-
tive development coevolved and is co-expressed during childhood. Early constraints
result in attentional and behavioral biases that in turn result in the experiences needed
to adapt these systems to the nuances of local conditions. In keeping with Lancy’s
(this volume) description of the ethnographic record, children’s self- initiated activi-
ties do indeed result in their acquisition of human universals (e.g., language) and
culture-specific competencies needed to be successful in adulthood in traditional
societies without much adult intervention. A fundamental and critical issue is
whether the activities that result in the acquisition of culture-specific competencies
in traditional societies are sufficient for the acquisition of culture-specific compe-
tencies in modern societies.
Variation and the Evolution of Domain General Abilities
Humans can inhabit multiple social and ecological niches, in part because folk sys-
tems can be adapted to variation in local conditions during development. These folk
systems and associated heuristics enable people to effortlessly cope with a variety
of ecological and social demands, as represented by the left section of Fig. 9.4. The
associated folk heuristics are toward the invariant end of the continuum because
developmental adaptation of these systems is in the context of inherent constraints,
that is, the systems (e.g., language) are plastic but only to some extent. If this were
not the case, the folk abilities shown in Fig. 9.2 would not be universal, but they
appear to be. To be sure, elaboration of one folk domain (e.g., folk biology) or
another or the extent to which some attributional biases are favored over others—for
instance, the belief that people from other groups are hostile rather than coopera-
tive—varies from one context to the next, but all of this variation is anchored by the
same skeletal folk structure. These systems, however, are not sufficient for coping
with unexpected (or inexperienced) conditions, that is, novelty. Novelty is defined
as conditions that cannot be coped with using only evolved or learned heuristics. For
instance, most social dynamics are routine and do not require explicit evaluation of
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the behavior and intentions of other people. During times of conflict, however,
behavioral predictability can result in a disadvantage and use of novel arguments or
behavioral strategies (e.g., tactics during large-scale conflicts) can result in an
advantage because competitors will not have readily accessible counter strategies
available to them.
The variant end of the continuum shown in Fig. 9.4 represents conditions that are
not readily accommodated by evolved heuristics. Coping with these conditions
requires explicit problem-solving abilities. Theories regarding the pressures that
drove the evolution of these abilities are debated and beyond the scope of this chap-
ter, but include climatic change (e.g., winter), hunting demands, and social competi-
tion (Alexander, 1989; Ash & Gallup, 2007; Bailey & Geary, 2009; Geary, 2005;
Kaplan, Hill, Lancaster, & Hurtado, 2000; Potts, 1998). The key idea across all of
these models is that there are advantages to being able to anticipate and plan behav-
ioral strategies to cope with novelty and change. Geary (2005) proposed that the
core mechanism for coping with novelty and change is the autonoetic mental model.
These are explicit attention-driven mental representations—supported by working
memory—of situations that are centered on the self and one’s relationship with
other people or one’s access to biological and physical resources and support the
generation and rehearsal of behavioral strategies for gaining access to these
resources. As mentioned earlier, the representations often involve a form of mental
time travel; specifically, simulations of past, present, or potential future states that
Fig. 9.4 The types of cognitive mechanisms that operate on ecological or social information.
These are predicted to vary with the extent to which that information tended to be invariant (result-
ing in evolved heuristics) or variant (resulting in evolved problem-solving mechanisms) during the
species’ evolutionary history and during a typical lifetime. Adapted from “The origin of mind:
Evolution of brain, cognition, and general intelligence,” by D. C. Geary, p. 168. Copyright 2005
by the American Psychological Association
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can be cast as images, in language, or as memories of personal experiences (Paivio,
2007; Suddendorf & Corballis, 1997; Tulving, 2002).
A key component is the ability to create a mental representation of a desired or
fantasized state and to compare this to a mental representation of one’s current situ-
ation. The fantasized world is one in which the individual is able to control social
(e.g., social dynamics), biological (e.g., access to food), and physical (e.g., shelter)
resources in ways that would have enhanced survival or reproductive prospects dur-
ing human evolution. The mental simulation creates a problem space, including an
initial state (one’s current circumstances) and a goal state (the fantasized outcome).
The proposal is that people’s ability to explicitly problem-solve in ways that reduced
the difference between the initial and goal states evolved as a core feature of autono-
etic mental models. The details are described in Geary (2005), but the gist is the
evolution of weak problem-solving methods such as means-ends analyses (Newell
& Simon, 1972) was driven by the competitive advantage that results from the abil-
ity to inhibit evolved or learned heuristics and to then generate and mentally rehearse
more novel social-competitive strategies and to mentally generate the strategies that
support greater control of nonsocial resources, as with constructing tools and shel-
ters and planning hunts.
Despite the extraordinary ability to mentally simulate future conditions and
problem-solve to devise behavioral goal-directed strategies, people’s reasoning
about such conditions is influenced by many documented biases that often result in
incorrect inferences or less-than-optimal solutions (e.g., Evans, 2002; Johnson-
Laird, 1983; Oaksford & Chater, 1998; Tversky & Kahneman, 1974). Some of
these biases result from presenting experimental tasks in evolutionarily novel con-
texts (Cosmides, 1989), and others simply reflect beliefs that are good enough for
day-to- day living, albeit they are often inaccurate from a scientific perspective
(Clement, 1982; Sinatra & Danielson, this volume). In any event, there are indi-
vidual differences in the ability to inhibit evolved or learned heuristics and prior
knowledge and to generate abstract, decontextualized representations of the prob-
lem at hand. There may be even more individual variation in the ability to use
formal logic (e.g., deduction based on a set of premises) to operate on these abstract
representations (Stanovich, 1999). People who are able to do so can eliminate
many reasoning biases and thereby produce more optimal solutions (Stanovich &
West, 2000).
But even so, people who are capable of formal logical reasoning often commit
common reasoning errors (Stanovich & West, 2008). This is because use of formal
logic and explicit problem-solving requires the effortful suppression of heuristics
and prior knowledge and, as a result, people use these systems only when necessary.
This makes sense because folk and learned knowledge and heuristics are typically
good enough for achieving most day-to-day goals, and suppression of these to con-
struct new strategies is only necessary when currently available ones are not effec-
tive (i.e., the conditions are toward the variant end in Fig. 9.4). Individual differences
in the ability to suppress heuristics and prior knowledge and beliefs and engage in
formal logical thinking are independently related to measures of general fluid
intelligence (below), syllogistic reasoning, and cognitive flexibility, that is, openness
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to new ideas and alternative explanations of the same phenomenon (Stanovich &
West, 2000; West, Toplak, & Stanovich, 2008).
The important point for us is that the ability to logically and critically evaluate
evidence and to engage in other forms of formal problem solving, as is necessary for
many aspects of learning in school, does not come easily to most people and requires
the suppression of folk biases (see Sinatra & Danielson, this volume; Sweller, this
volume). In theory, the more evolutionarily novel the academic content—such as
systems of equations in algebra versus understanding the cardinal value of count
words—the more effortful is the learning (Geary, 2007). This is because the larger
the gap between the conceptual base of the academic domain, such as the principles
of natural selection, and the conceptual base and biases that are components of folk
systems, such as folk biology (see Shtulman, 2006; Sinatra & Danielson, this vol-
ume), the more likely folk beliefs will interfere with learning academic content; the
scientifically accurate view of how species change across generations in this case.
For the latter to occur, folk biases must be inhibited and the mechanisms that enable
explicit goal-directed problem solving need to be engaged.
Working Memory, Intelligence, and Evolutionarily
Novel Learning
Geary (2005) suggested the working memory and problem-solving competencies
that support the use of autonoetic mental models define the core of general fluid
intelligence. In other words, more than a century of research on general intelligence
has identified the evolved mechanisms that enable humans to cope with and learn
from evolutionarily novel situations, those toward the variant end of the continuum
in Fig. 9.4, not unlike Cattell’s (1963, p. 3) original description, “Fluid general abil-
ity … shows more in tests requiring adaptation to new situations, where crystallized
skills are of no particular advantage.” The details can be found in Geary (2005): The
point here is that the result is represented by the arrow at the center of Fig. 9.4, that
is, the transfer of information, procedures, and heuristics learned from effortful,
controlled problem solving to long-term memory, including semantic and proce-
dural memory. In keeping with Sweller’s cognitive load theory (2015, this volume),
the eventual result is the learning of new knowledge or problem-solving heuristics
that can thereafter be effortlessly applied to solving the once novel problems.
Our main point is that the ability to learn evolutionarily novel information—
including reading, writing, and arithmetic—is the result of two types of brain and
cognitive plasticity, both of which evolved to enable humans to cope with variation
in ecological and social conditions. The first is the plasticity in folk systems that
enable them to be adapted to local conditions during development. The second
results from the ability to mentally represent and manipulate information in work-
ing memory, which in turn creates mental experiences (e.g., rehearsal of information)
that enable the top-down modification of folk systems (Damasio, 2003; Geary, 2005).
Moreover, the simultaneous activation of multiple folk systems and the representation
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of corresponding information in working memory appear to result in the ability to
link these systems in novel ways (Garlick, 2002; Sporns, Tononi, & Edelman, 2000)
and through this create evolutionarily novel, academic competencies (Geary, 2007).
Academic Development
In contrast to universal folk knowledge, most of the knowledge taught in modern
schools is culturally specific; that is, it does not emerge in the absence of formal
instruction. Geary (1995) termed these competencies biologically secondary
because they are built from the biologically primary folk domains discussed earlier.
We illustrate the relation between folk domains and secondary abilities in the first
section and outline the corresponding premises and principles of evolutionary
educational psychology in the second.
Learning to Read
We assume that the building of secondary abilities and knowledge from folk systems
is possible because of the two forms of plasticity noted above; plasticity in folk
systems themselves and the ability to modify these through top-down processes that
support people’s generation of autonoetic mental models. In this view and in keeping
with Sweller’s (2015, this volume) cognitive load theory, the learning of secondary
knowledge is supported by the ability to explicitly represent information in working
memory and then to use controlled problem solving for learning academic material.
Reading provides an example of how this might work.
We assume that reading and writing systems initially emerged, culturally, from
the motivation to socially communicate with and attempt to influence the behavior
of other people, and if so, they should be built from folk psychological systems
(Geary, 2008a; Mann, 1984; Rozin, 1976). Indeed, the core predictors of children’s
ease of learning to read indicate a strong dependence on language systems (e.g.,
Bradley & Bryant, 1983; Hindson et al., 2005; Mann, 1984; Stevens, Slavin, &
Farnish, 1991; Wagner & Torgesen, 1987). Initially, the critical skills include pho-
nemic awareness—explicit awareness of distinct language sounds—and the ability
to decode unfamiliar written words into these basic sounds (e.g., ba, da). Decoding
requires the explicit representation of the sound in phonemic short-term memory
and the association of this sound and blends of sounds with letters (e.g., b, d) and
letter combinations, respectively (Bradley & Bryant, 1983).
Individual differences in kindergartners’ phonetic processing system (e.g., skill
at discriminating similar sounding phonemes) predict the ease with which they
learn basic word-decoding skills in first grade (Wagner, Torgesen, & Rashotte,
1994). Children who show a strong explicit awareness of basic language sounds are
more skilled than are other children at associating these sounds with the symbol
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system of their written language. Unlike acquiring a natural language, the majority
of children acquire these basic reading competencies most effectively with system-
atic, organized, and teacher-directed explicit instruction on phoneme identification,
blending, and word decoding (e.g., Hindson et al., 2005; Stevens et al., 1991).
Skilled reading also requires fluency and text comprehension. Fluency is the fast
and automatic retrieval of word meanings as they are read, which is related in part
to the frequency with which the word has been encountered or practiced in the past
(Sereno & Rayner, 2003). Text comprehension requires an understanding of the
meaning of the composition and is dependent, in part, on the ability to identify main
themes in the text and distinguish highly relevant from less relevant passages. As
with more basic reading skills, many children require explicit instruction in the use
of these strategies to aid in text comprehension (Connor, Morrison, & Petrella,
2004; Stevens et al., 1991).
If social communication was the motivation for the development of written sys-
tems, then reading comprehension should also be dependent on theory of mind and
other folk psychological domains, at least for genres that involve human relation-
ships (Geary, 2010). Most of these stories involve the re-creation of social relation-
ships, complex patterns of social dynamics, and even elaborate person schema
knowledge for main characters, as is the focus of literary Darwinism (Carroll,
2011). The theme of many of the most popular genres involves the dynamics of mat-
ing relationships (e.g., romance novels) and competition for mates, and often
involves use of autonoetic mental models to build social scenarios. One implication
is that once people learn to read, they engage in this secondary activity because it
allows for the representation of evolutionarily salient themes, particularly the men-
tal representation and rehearsal of social dynamics. Folk biology and folk physics
should also result in some people being interested in biological phenomena (e.g.,
the magazine Natural History) and mechanical things (e.g., the magazine Popular
Mechanics).
The Creation of Culture
All of the academic activities that occur in modern research universities (politics
aside) involve the creation of evolutionarily novel information, especially in engi-
neering and the sciences. In fact, scholars of one kind or another have been building
an unprecedented store of information and knowledge over the past few thou-
sand years (Murray, 2003; Simonton, 2009). Murray’s analysis revealed historical
bursts of creative activity (e.g., the Renaissance) that tended to emerge in wealthier
cultures with mores that supported individual freedom and that socially and finan-
cially rewarded creative expression. The exceptional accomplishments that have
produced the modern world have been made by individuals situated in these cultures
and who have a unique combination of traits; specifically, high fluid intelligence,
creativity (e.g., ability to make remote associations), an extended period of prepara-
tion in which the basics of the domain are mastered, long work hours, advantages in
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certain folk domains, ambition, and sustained output of domain-related products,
such as scientific publications (Ericsson, Krampe, & Tesch-Römer, 1993; Lubinski,
2004; Sternberg, 1999).
These components of accomplishment illustrate the interplay between folk
knowledge, fluid intelligence, motivation, and the generation of secondary knowl-
edge and illustrate why children’s intuitive folk knowledge and learning biases are
not sufficient for secondary learning. Modern physics is one of humanity’s most
significant accomplishments and yet is understood by only a very small fraction of
humanity. One reason is that people’s naïve understanding of physical phenomena is
influenced by the biases that are aspects of folk physics, but differ from the scientific
understanding of the same phenomena (McCloskey, 1983). When asked about the
motion of a thrown ball, most people believe there is a force propelling it forward,
something akin to an invisible engine, and another force propelling it downward.
The downward force is gravity, but there is in fact no force propelling it forward,
once the ball leaves the thrower’s hand (Clement, 1982). The concept of a forward-
force, called “impetus,” is similar to pre-Newtonian beliefs about motion prominent
in the fourteenth to sixteenth centuries. The idea is that the act of starting an object
in motion creates an internal force (impetus) that keeps it in motion until this impetus
gradually dissipates. Although adults and even preschool children often describe the
correct trajectory for a thrown or moving object (e.g., Kaiser, McCloskey, & Proffitt,
1986), reflecting their implicit folk competencies, their explicit explanations reflect
this naïve understanding of the forces acting upon the object.
Careful observation, use of the scientific method (secondary knowledge itself;
Geary, 2012), and use of inductive and deductive reasoning are necessary to move
from an intuitive folk understanding to scientific theory and knowledge. In his mas-
terwork, the Principia (Newton, 1995, p. 13), Newton said as much: “I do not define
time, space, place and motion, as being well known to all. Only I must observe, that
the vulgar conceive those quantities under no other notions but from the relation
they bear to sensible objects.” The “vulgar” only understand physical phenomena in
terms of folk knowledge and Newton went well beyond this. Newton corrected the
pre-Newtonian beliefs about forces acting on objects, but still appears to have relied
on other aspects of folk physical systems to complete this work. His conceptualiza-
tion of objects in motion and the gravitational and rectilinear forces underlying the
pattern of this motion were based on his ability to explicitly use visuospatial sys-
tems to construct geometric representations of motion and then to apply Euclidean
geometry and formal logic to mathematically prove the scientific accuracy of these
representations. The explicit and exacting use of formal logic is associated with
high general fluid intelligence (Stanovich, 1999), as noted. Despite popular stories
and an assumption of an “Aha” insight, Newton devoted an extended period of sus-
tained effort and attention to this work and appears to have been obsessed with
understanding physical phenomena (e.g., Berlinski, 2000; White, 1999).
The point is that Newton’s efforts transformed the physical sciences and at the
same time created a substantial gap between the scientific understanding of gravity
and motion and folk beliefs about these same phenomena. The folk intuitions of the
fourteenth century natural philosophers were no longer sufficient after Newton.
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Fortunately, it is not necessary for students to reconstruct Newton’s efforts; in fact,
few could do so. But, it is necessary that they come to understand the basics of
Newtonian physics. Cognitive and brain imaging studies indicate that giving up
folk-physical intuitions and grasping Newton’s insights about motion do not come
easily, even for college students (Dunbar, Fugelsang, & Stein, 2007; Zimmerman,
2005). The same is true for the theory of evolution, the scientific method, and many
other evolutionarily novel innovations and knowledge (Klahr & Li, 2005; Klahr &
Nigam, 2004; Shtulman, 2006; Sinatra & Danielson, this volume).
Evolutionary Educational Psychology
The innovative contributions of Newton and others have altered the society and
culture in which we live, including substantive increases in the need for formal
education. To live successfully in the modern world, children must now acquire a
wide range of evolutionarily novel knowledge. To make matters worse, the requisite
knowledge is a moving target, because scientific and technological changes are
accruing at an accelerated pace, as is the store of literature, poetry, plays, drama, and
so forth. How then do we best prepare children to be successful in the modern
world? Of course, the modern field of education is focused on this question, but has
not been informed by an evolutionary understanding of cognitive development, nor
considered the question of how folk abilities can be modified to create secondary
competencies. Evolutionary educational psychology is an attempt to bridge evolu-
tionary insights and educational science (Geary, 2007, 2008a). In the sections below,
we outline the basic premises and principles of this approach.
Premises. Evolutionary educational psychology is the study of how educational
interventions interact with children’s folk abilities, biases, and motivations to create
secondary abilities and knowledge. The first premise follows from our discussion of
folk domains (Fig. 9.2); children have inherent but not fully developed attentional,
perceptual, and cognitive systems that support their understanding of universal
social, biological, and physical phenomena. The associated concepts and abilities
support good enough functioning in traditional contexts, but is not the same as a
scientific understanding of the same phenomena.
The second premise is based on the co-evolution of children’s behavioral and
cognitive development as related to the adaptation of folk domains to local condi-
tions; specifically, children’s self-initiated behavioral biases create the same types
of experiences that led to the evolution of folk systems and provide the evolution-
arily expectant experiences needed for the normal development of these competen-
cies (Caporael, 1997; Greenough et al., 1987; Scarr, 1992). A critical point is that
children’s primary behavioral activities are directed toward those features of the
social, biological, and physical worlds that were recurrent, though variable (e.g., in
the facial features of different people), during human evolution, not information
relevant to secondary learning. As Lancy (this volume) describes, children also
attend to and imitate adults and more competent older children and in this way learn
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culture-specific knowledge and skills, such as cooking and hunting (e.g., Blurton
Jones, Hawkes, & O’Connell, 1997). We have no doubt that children have an
evolved motivation to acquire the skills needed to be successful in their culture, but
note the gap between the skills needed to be successful in traditional cultures and
those needed to integrate into a modern, developed economy. Observation of paren-
tal reading may pique children’s interest in books, but playing with books does not
result in the ability to phonetically decode written words in the same way that play-
ing with a bow and arrow contributes to learning how to use this weapon (Gurven,
Kaplan, & Gutierrez, 2006; Toub et al., this volume).
The third premise follows from the first two and the traits of innovators. It is almost
certainly the case that these innovators engaged the cognitive systems that support
autonoetic mental models—attentional control, working memory, fluid intelligence,
explicit problem solving—during the generation of their insights and secondary
knowledge. We do not see how it is possible for students to learn this same knowledge
without explicitly engaging the same systems (Sweller, this volume).
Principles. Innovators generate new knowledge and technical advances by using
fluid intelligence and other less well-understood processes (e.g., creativity) to mod-
ify and link together folk systems in novel ways. The useful advances are retained
across generations through artifacts (e.g., books) and traditions (e.g., apprentice-
ships) and accumulate from one generation to the next. The first principle of evolu-
tionary educational psychology is that the cross-generational accumulation of these
advances has resulted in a more scientifically accurate understanding of the phe-
nomena that are the foci of folk psychology, folk biology, and folk physics. Darwin’s
principles of natural selection and Newton’s theory of gravity and motion resulted
in a gap between people’s folk biological and folk physical knowledge and these
core principles of modern biology and physics (Clement, 1982; Sinatra & Danielson,
this volume). In other words, there is now a substantial and growing gap between
the folk knowledge and heuristics that were sufficient for day-to-day living during
much of human evolution and the knowledge and competencies needed to function
in the modern world.
The second principle is that schools themselves are cultural innovations. They
are not found in traditional societies (Lancy, this volume), and only emerged in
societies in which scientific and cultural advances created gaps between folk knowl-
edge and the competencies needed to be successful in these societies. In this view,
the function of schools is to organize the activities of children, so they can acquire
the secondary competencies needed to close the gap between folk abilities and the
knowledge needed to be successful in the modern world. The third principle is that
secondary competencies are built from primary folk systems, but, unlike the fast
implicit learning that adapts folk systems to local conditions, most secondary learn-
ing will require the effortful engagement of working memory, explicit problem
solving, and fluid intelligence to modify primary systems. As we describe in the
next section and as noted by Bjorklund and Beers (this volume) and Toub et al.
(this volume), this does not mean that children cannot learn some secondary skills
through engagement in primary activities, but we suspect there are limitations to
this approach (Gray, this volume, provides a counter argument). Fourth, children’s
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inherent motivational bias to engage in activities that will adapt folk knowledge to
local conditions will often conflict with the need to engage in activities that will
result in secondary learning. We would then expect the average adolescent to be
more interested in peer relationships than high school algebra.
Implications for Research on Instructional Interventions
For the most part, the premises and principles of evolutionary educational psychol-
ogy are concerned with characterizing the evolved cognitive and motivational biases
that may interfere with the acquisition of secondary knowledge and the implications
of these dispositions for designing effective instructional methods to enhance sec-
ondary learning (see also Sinatra & Danielson, this volume; Sweller, this volume).
As Geary (2008a) has previously pointed out, evolutionary educational psychology
is not ready for translation into school curricula, although as Sweller notes (2015,
this volume) the framework does help to explain many previously documented
instructional effects. More generally, “it provides a theoretical foundation for (a)
conceptualizing children’s learning in school and their motivation to engage in this
learning, (b) generating empirically testable hypotheses about learning and motiva-
tion, and (c) discussing implications for understanding and ultimately improving
educational outcomes” (Authors’ emphasis; Geary, 2008a, p. 179). In this section,
we move beyond a discussion of educational implications by proposing an evolu-
tionarily informed pedagogical framework for generating explicit hypotheses con-
cerning the types of instruction that would most likely improve the acquisition of
secondary knowledge, taking into account: (a) the degree to which the secondary
information is evolutionarily novel; (b) the species-typicality of the contexts and
settings (physical and social) in which these skills are to be learned; and (c) indi-
vidual differences in various cognitive competencies, motivational dispositions,
personality traits, and demographic characteristics that could potentially moderate
the effectiveness of any given instructional approach.
Which Is Better: Explicit Formal Instruction or Discovery
Learning?
The relative effectiveness of different instructional strategies has been hotly debated
in both the academic educational literature and the popular press and touched upon
by many of the chapters in this volume (Bjorklund & Beers, this volume; Gray, this
volume; Lancy, this volume; Sweller, this volume; Toub et al., this volume). Not
infrequently, two opposing types of methods are pitted against one another, as
exemplified by the paradigmatic case of whether direct or explicit instruction leads
to better learning than unstructured discovery learning (cf. Kirschner, Sweller, &
Clark, 2006). Berch (2007) has discussed the limitations of taking such a binary
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approach to these matters, as originally outlined by Newell (1973), who argued that
this is a poor model for doing science. Berch concluded that a more productive
approach would be to examine the conditions under which specific types of instruc-
tional methods are most effective in facilitating the learning of various types of
secondary knowledge for children of differing ages and abilities.
Additionally, Berch (2007) discussed some interesting comments made by David
Klahr subsequent to his earlier and highly controversial report demonstrating the
unequivocal superiority of explicit instruction over discovery learning with respect
to children’s understanding of control variables in carrying out experimental manip-
ulations (CVS) (Klahr & Nigam, 2004). Namely, Klahr acknowledged that based on
a series of such studies he and his colleagues had conducted, the best they could say
was that their “particular specification of learning via explicit instruction worked
better than an extreme form of learning via discovery for learning CVS” (Authors’
emphasis, p. 234). They concluded that “[we] certainly do not know if our CVS
instruction is the ‘best way’ to teach CVS, or if Direct Instruction is the best way
to teach all process skills” (Klahr & Li, 2005, p. 234). Similarly, Geary (2008b)
has previously concluded that “It is unlikely that teacher-directed, peer-assisted, or
self- discovery alone will be the most effective way to learn secondary academic
material” (p. 224), and that “only empirical studies will allow us to determine the
best mix of methods for different academic domains and for different children”
(p. 224). Although this assertion is most certainly true, a more comprehensive evo-
lutionary educational psychology should be able to offer a theoretical framework
from which explicit, testable hypotheses can be generated to guide the design of
such empirical studies.
Elsewhere, Geary (2008a) has argued that the mechanisms he has previously
outlined (Geary, 2005, 2007) “provide a means for generating empirically testable
hypotheses about children’s academic motivation and their ease of learning in
school, as well as equally important hypotheses about the effectiveness of alterna-
tive instructional methods” (Authors’ emphasis, p. 192). In the next section, we
elaborate on how these ideas can contribute to the formation of testable instructional
hypotheses (see also Sweller, 2015, this volume).
Toward an Evolutionarily Informed Pedagogical Framework
Figure 9.5 illustrates what we refer to as an evolutionarily informed pedagogical
framework. As can be observed, there are three axes: (a) The x-axis is the Degree of
Systematic Instruction (DSI), ranging from Low to High, with the lowest form
being unstructured or child-centered and the highest being teach-directed, explicit
instruction; (b) the y-axis is the Classroom Context (CC) reflecting the physical and
social setting along with the goal-related orientation, ranging from species-typical,
real-world problem solving (e.g., how to equally share limited acquired resources
with playmates) (Shaw, this volume) to species-atypical learning for its own sake
(e.g., reading popular novels) (Bjorklund & Bering, 2002); and c) the z-axis is the
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Proximity of Secondary Skills (PSS) to supporting primary systems, ranging from
near (e.g., language and reading) to far (e.g., folk biology and natural selection).
Finally, a number of variables are proposed as factors that will moderate the effects
of the DSI, CC, and PSS, including grade level, sex, and working memory capacity,
among others.
Taken together, this framework can be used to generate testable hypotheses con-
cerning what we refer to as the “Zone of Best Pedagogical Fit.” In other words,
when considering a test of the type of instruction that might be most effective for
improving learning, one should simultaneously consider the nature of the CC, the
PSS, and the multiple factors that could potentially moderate the value of its impact.
For example, following the pedagogical framework outlined above, we have sug-
gested that structured, explicit, teacher-directed instruction should be most effective
when acquiring secondary skills that are remote from supporting primary systems
and that take place in a species atypical, classroom context where the goal is ori-
ented toward acquiring knowledge for its own sake. Note, however, that this frame-
work is not prescriptive; rather, it offers researchers a detailed, systematic, and
multidimensional tool that permits both the generation of specific hypotheses for
empirical testing and a way of organizing and consolidating the outcomes of such
studies to arrive at judgments concerning the Zone of Best Pedagogical Fit.
Among other questions that arise from this framework is the degree to which the
various potential moderating variables influence the effectiveness of any given
instructional method, either alone or in combination with others. For example, if
high working memory capacity is needed for use of a relatively unstructured
Fig. 9.5 Toward an evolutionarily informed pedagogical framework (after Berch, 2008)
this figure will be printed in b/w
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technique (e.g., guided discovery) to learn abstract information that is remote from
its supporting primary systems, its effectiveness is very likely to be different for
students with lower than higher working memory capacity. Another example would
be Gray’s (2013; this volume) assertion that children learn best in mixed-age set-
tings. In a sense, he considers this a more “species-typical setting” than contempo-
rary, age-graded classrooms. Yet the extent to which this hypothesis would hold true
is highly likely to depend on: (a) the remoteness of the to-be-acquired secondary
content from supporting primary systems; (b) the extent to which the principal
instructional methods employed are more or less structured or explicit; and (c) the
variability of the students’ personality traits, cognitive capacities, motivational dis-
positions, or other potential moderating factors. To the best of our knowledge, no
empirical studies testing these ideas have been published in a refereed journal.
As another example, there has been a major push in mathematics education for
students to learn to solve “real-world” mathematics problems concerning everyday
objects and settings in order to motivate them to learn abstract concepts and sym-
bols. On the one hand, it could be argued that trying to concretize abstract concepts
may reduce the remoteness of the secondary knowledge to be acquired (i.e., abstract
symbols), thereby helping engage students’ supporting primary systems; but even if
true, there is evidence that learning from concrete examples as compared with
abstract symbols can limit transferring knowledge to new problems (Kaminski,
Sloutsky, & Heckler, 2006, 2008). On the other hand, use of real-world problems
may stimulate students’ interest in learning abstract mathematics if the problem-
solving contexts evoke children’s evolved motivational biases to engage in activities
such as socializing with peers or intergroup competition. In other words, even a
mathematics problem about a real-world context such as sports should be more
likely to arouse motivational biases if it concerns using to-be-acquired computa-
tional skills for determining the likelihood of a baseball team beating their arch rival
than just calculating the square footage of a major league stadium’s outfield. To the
best of our knowledge, no published studies have been carried out comparing the
motivational effectiveness of employing real-world problem-solving contexts that
differ not by the degree of authenticity of the real-world contexts themselves, but
rather by evolutionarily informed differences in the extent to which these contexts
evoke evolved motivational biases.
In sum, the framework we present here permits us to add a number of postulates
to the premises and principles of evolutionary educational psychology described
earlier. These are shown in Table 9.1 and should be useful in moving the field toward
theoretically informed empirical studies (see also Sweller, this volume; Toub et al.,
this volume).
Conclusion
Humans have the extraordinary ability to create knowledge-based culture sup-
ported by shared beliefs (e.g., of the groups' origins) and rules for social behavior
that in turn enables the formation of large cooperative groups (Baumeister, 2005;
9 Evolution and Development
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Richerson & Boyd, 2005). It is almost certain that children and adults have corre-
sponding learning and motivational mechanisms that support the cross-genera-
tional transfer of these beliefs and other culturally useful knowledge. These
mechanisms include child-initiated play, observational learning, and adults’ use of
stories to convey cultural knowledge to children (Lancy, this volume). Over the
past several millennia, however, groups have increased substantially in size and
economic diversification and some individuals within these groups have discov-
ered better ways of producing food (e.g., agriculture crops), conducting commerce
(e.g., monetary systems), and understanding the natural world (i.e., science). These
advances have provided many benefits, but many of them have outpaced evolu-
tion’s ability to adapt cognitive and motivational systems such that children easily
learn the associated competencies. In other words, cultural innovations and brain
and cognitive evolution are out of sync, creating a gap between what we are moti-
vated to learn and what we easily learn and the competencies needed to live well in
the modern world.
Schools are one of these innovations; schools do not exist in traditional societies
where day-to-day living does not require reading, writing, or arithmetic (Lancy, this
volume). Within the modern world, these are now considered rudimentary compe-
tencies, and we expect all children, not just the elite, to acquire them. The goal of
universal schooling is very recent (<200 years), with respect to evolutionary time
and it is very unlikely that humans have the same cognitive and motivational biases
to support learning to read, write, and do arithmetic in the same way they have
biases that allow them to form and maintain social relationships (i.e., folk psychology).
Yet, learning the three Rs must be based on the ability to adapt folk systems for
acquiring these evolutionarily novel abilities.
Evolutionary educational psychology is the study of how children’s evolved
cognitive, learning, and motivational biases influence their ability and motivation
Table 9.1 Postulates of evolutionary educational psychology
1. The effectiveness of specific forms of instructional methods will be dependent on: a) the
proximity of the secondary skills to their supporting primary systems, b) the classroom
context (i.e., the physical and social setting, and the goal orientation), and c) the moderating
influences of various developmental (e.g., grade level), demographic (e.g., SES), and
individual differences factors (e.g., working memory capacity, academic motivational
disposition, personality traits)
2. The effectiveness of adopting more or less unstructured (i.e., informal, child-centered,
discovery-oriented) approaches for improving the learning of secondary knowledge will be
a direct function of:
(a) the proximity of the to-be-acquired content to its supporting, primary folk domains
(other things being equal)
(b) the extent to which the physical and social setting of the classroom context is
species-typical
(c) the degree to which the problem-solving goal is real-world oriented as contrasted with
learning for its own sake
3. The potential advantage of employing real-world contexts for learning secondary, abstract
knowledge will be a direct function of the degree to which they evoke evolved
motivational biases
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t1.17
t1.18
t1.19
243
to learn novel academic abilities and knowledge in school. As illustrated by the
diversity of opinion across the chapters of this volume, the best approach for melding
evolved biases with educational goals is vigorously debated. At the same time, all
of the authors in this volume agree that there is a value-added to framing educa-
tional goals (among others) within an evolutionary context, and most importantly,
they provide direction for future empirical studies. The ultimate, so to speak, benefit
of this approach will be in its ability to generate testable hypotheses about instruc-
tional approaches and based on these improve the educational outcomes of all chil-
dren. In other words, evolutionary educational psychology will flourish or flounder
based on its contributions to our ability to meet the goals of a universal education.
References
Abrams, D., & Hogg, M. A. (Eds.). (1990). Social identity theory: Constructive and critical
advances. New York, NY: Springer.
Adolphs, R. (2003). Cognitive neuroscience of human social behaviour. Nature Reviews
Neuroscience, 4, 165–178.
Alexander, R. D. (1989). Evolution of the human psyche. In P. Mellars & C. Stringer (Eds.),
The human revolution: Behavioural and biological perspectives on the origins of modern
humans (pp. 455–513). Princeton, NJ: Princeton University Press.
Alexander, R. D. (1990). How did humans evolve? Reflections on the uniquely unique species.
Museum of Zoology (Special Publication No. 1, pp. 1–38). Ann Arbor: The University of
Michigan.
Ash, J., & Gallup, G. G., Jr. (2007). Paleoclimatic variation and brain expansion during human
evolution. Human Nature, 18, 109–124.
Atran, S. (1994). Core domains versus scientific theories: Evidence from systematics and Itza- Maya
folkbiology. In L. A. Hirschfeld & S. A. Gelman (Eds.), Mapping the mind: Domain specificity
in cognition and culture (pp. 316–340). New York, NY: Cambridge University Press.
Atran, S. (1998). Folk biology and the anthropology of science: Cognitive universals and cultural
particulars. Behavioral and Brain Sciences, 21, 547–609.
Bailenson, J. N., Shum, M. S., Atran, S., Medin, D. L., & Coley, J. D. (2002). A bird’s eye view:
Biological categorization and reasoning within and across cultures. Cognition, 84, 1–53.
Bailey, D. H., & Geary, D. C. (2009). Hominid brain evolution: Testing climatic, ecological, and
social competition models. Human Nature, 20, 67–79.
Bandura, A. (1997). Self-Efficacy: The Exercise of Control. San Francisco, CA: Freeman.
Baron-Cohen, S. (1995). Mindblindness: An essay on autism and theory of mind. Cambridge, MA:
MIT Press/Bradford Books.
Barton, R. A., & Dean, P. (1993). Comparative evidence indicating neural specialization for
predatory behaviour in mammals. Proceedings of the Royal Society of London B, 254, 63–68.
Baumeister, R. F. (2005). The cultural animal: Human nature, meaning, and social life. New York,
NY: Oxford University Press.
Berch, D. B. (2007). Instructing evolved minds: Pedagogically primary strategies for promoting
biologically secondary learning. In J. S. Carlson & J. R. Levin (Eds.), Educating the evolved
mind (Psychological perspectives on contemporary educational issues, Vol. 2, pp. 109–118).
Greenwich, CT: Information Age.
Berch, D. B. (2008, November). The emerging field of “evo-edu”: Questions, challenges,
constraints, and caveats. Presented at the Evolution Institute Workshop on Early Childhood
Education, Miami, FL.
9 Evolution and Development
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
244
Berlin, B., Breedlove, D. E., & Raven, P. H. (1966). Folk taxonomies and biological classification.
Science, 154, 273–275.
Berlin, B., Breedlove, D. E., & Raven, P. H. (1973). General principles of classification and
nomenclature in folk biology. American Anthropologist, 75, 214–242.
Berlinski, D. (2000). Newton’s gift. New York, NY: Touchstone.
Bjorklund, D. F., & Bering, J. M. (2002). The evolved child: Applying evolutionary developmental
psychology to modern schooling. Learning and Individual Differences, 12, 347–373.
Bjorklund, D. F., & Ellis, B. J. (2014). Children, childhood, and development in evolutionary
perspective. Developmental Review, 34, 225–264.
Bjorklund, D. F., & Pellegrini, A. D. (2002). The origins of human nature: Evolutionary develop-
mental psychology. Washington, DC: American Psychological Association.
Bloom, P. (1996). Intention, history, and artifact concepts. Cognition, 60, 1–29.
Blurton Jones, N. G., Hawkes, K., & O’Connell, J. F. (1997). Why do Hadza children forage? In
N. L. Segal, G. E. Weisfeld, & C. C. Weisfeld (Eds.), Uniting psychology and biology:
Integrative perspectives on human development (pp. 279–313). Washington, DC: American
Psychological Association.
Bogin, B. (1999). Patterns of human growth (2nd ed.). Cambridge, England: Cambridge University
Press.
Bradley, L., & Bryant, P. E. (1983). Categorizing sounds and learning to read—A causal connec-
tion. Nature, 301, 419–421.
Brothers, L., & Ring, B. (1992). A neuroethological framework for the representation of minds.
Journal of Cognitive Neuroscience, 4, 107–118.
Bugental, D. B. (2000). Acquisition of the algorithms of social life: A domain-based approach.
Psychological Bulletin, 126, 187–219.
Burghardt, G. M. (2005). The genesis of animal play: Testing the limits. Cambridge, MA: Bradford/
MIT Press.
Butler, D., & Suddendorf, T. (2014). Reducing the neural search space for hominid cognition:
What distinguishes human and great ape brains from those of small apes? Psychonomic Bulletin
& Review, 21, 590–619.
Byrne, P., Becker, S., & Burgess, N. (2007). Remembering the past and imagining the future:
A neural model of spatial memory and imagery. Psychological Review, 114, 340–375.
Caporael, L. R. (1997). The evolution of truly social cognition: The core configurations model.
Personality and Social Psychology Review, 1, 276–298.
Caramazza, A., & Shelton, J. R. (1998). Domain-specific knowledge systems in the brain: The
animate-inanimate distinction. Journal of Cognitive Neuroscience, 10, 1–34.
Carey, S. (2009). The origin of concepts. New York, NJ: Oxford University Press.
Carroll, J. (2011). Reading human nature: Literary Darwinism in theory and practice. Albany, NY:
SUNY Press.
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal
of Educational Psychology, 54, 1–22.
Clement, J. (1982). Students’ preconceptions in introductory mechanics. American Journal of
Physics, 50, 66–71.
Connor, C. M., Morrison, F. J., & Petrella, J. N. (2004). Effective reading comprehension instruc-
tion: Examining child X instruction interactions. Journal of Educational Psychology, 96,
682–698.
Cosmides, L. (1989). The logic of social exchange: Has natural selection shaped how humans
reason? Studies with the Wason selection task. Cognition, 31, 187–276.
Damasio, A. (2003). Looking for Spinoza: Joy, sorrow, and the feeling brain. Orlando, FL:
Harcourt.
Darwin, C. R. (1845). Journal of researches into the natural history and geology of the countries
visited during the voyage of H.M.S. Beagle round the world, under the Command of Capt. Fitz
Roy, R.N (2nd ed.). London, England: John Murray.
D.C. Geary and D.B. Berch
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
245
Darwin, C. (1859). The origin of species by means of natural selection. London, England: John
Murray.
Dunbar, R. I. M. (1998). The social brain hypothesis. Evolutionary Anthropology, 6, 178–190.
Dunbar, K., Fugelsang, J., & Stein, C. (2007). Do naïve theories ever go away? In M. Lovett &
P. Shah (Eds.), Thinking with Data: 33rd Carnegie Symposium on Cognition (pp. 193–205).
Mahwah, NJ: Erlbaum.
Dyer, F. C. (1998). Cognitive ecology of navigation. In R. Dukas (Ed.), Cognitive ecology:
The evolutionary ecology of information processing and decision making (pp. 201–260).
Chicago, IL: University of Chicago Press.
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the
acquisition of expert performance. Psychological Review, 100, 363–406.
Evans, J. S. B. T. (2002). Logic and human reasoning: An assessment of the deduction paradigm.
Psychological Bulletin, 128, 978–996.
Feigenson, L., Dehaene, S., & Spelke, E. S. (2004). Core systems of number. Trends in Cognitive
Sciences, 8, 307–314.
Figueiredo, G. M., Leitão-Filho, H. F., & Begossi, A. (1993). Ethnobotany of Atlantic forest
coastal communities: Diversity of plant used in Gamboa. Human Ecology, 21, 419–430.
Figueiredo, G. M., Leitão-Filho, H. F., & Begossi, A. (1997). Ethnobotany of Atlantic forest
coastal communities: II. Diversity of plant uses at Sepetiba Bay (SE Brazil). Human Ecology,
25, 353–360.
Fiske, S. T. (2002). What we know now about bias and intergroup conflict, the problem of the
century. Current Directions in Psychological Science, 11, 123–128.
Fiske, S. T., & Taylor, S. E. (1991). Social cognition (2nd ed.). New York, NY: McGraw-Hill.
Flinn, M. V., Geary, D. C., & Ward, C. V. (2005). Ecological dominance, social competition, and
coalitionary arms races: Why humans evolved extraordinary intelligence. Evolution and
Human Behavior, 26, 10–46.
Freedman, D. G. (1974). Human infancy: An evolutionary perspective. New York, NY: Wiley.
Gallistel, C. R. (1990). The organization of learning. Cambridge, MA: MIT Press/Bradford
Books.
Garlick, D. (2002). Understanding the nature of the general factor of intelligence: The role of
individual differences in neural plasticity as an explanatory mechanism. Psychological Review,
109, 116–136.
Geary, D. C. (1995). Reflections of evolution and culture in children’s cognition: Implications for
mathematical development and instruction. American Psychologist, 50, 24–37.
Geary, D. C. (2004). Evolution and cognitive development. In R. Burgess & K. MacDonald (Eds.),
Evolutionary perspectives on human development (pp. 99–133). Thousand Oaks, CA: Sage.
Geary, D. C. (2005). The origin of mind: Evolution of brain, cognition, and general intelligence.
Washington, DC: American Psychological Association.
Geary, D. C. (2007). Educating the evolved mind: Conceptual foundations for an evolutionary
educational psychology. In J. S. Carlson & J. R. Levin (Eds.), Educating the evolved mind
(Psychological perspectives on contemporary educational issues, Vol. 2, pp. 1–99). Greenwich,
CT: Information Age.
Geary, D. C. (2008a). An evolutionarily informed education science. Educational Psychologist,
43, 279–295.
Geary, D. C. (2008b). Whither evolutionarily educational psychology? Educational Psychologist,
43, 217–226.
Geary, D. C. (2010). Male, female: The evolution of human sex differences (2nd ed.). Washington,
DC: American Psychological Association.
Geary, D. C. (2012). The evolved mind and scientific discovery. In J. Shrager & S. M. Carver
(Eds.), From child to scientist: Mechanisms of learning and development (pp. 87–115).
Washington, DC: American Psychological Association.
9 Evolution and Development
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
246
Geary, D. C., Berch, D. B., & Mann Koepke, K. (Eds.). (2015). Evolutionary origins and early
development of number processing (Mathematical cognition and learning, Vol. 1). San Diego,
CA: Elsevier Academic Press.
Geary, D. C., & Bjorklund, D. F. (2000). Evolutionary developmental psychology. Child
Development, 71, 57–65.
Geary, D. C., & Huffman, K. J. (2002). Brain and cognitive evolution: Forms of modularity and
functions of mind. Psychological Bulletin, 128, 667–698.
Gelman, R. (1990). First principles organize attention to and learning about relevant data: Number
and animate-inanimate distinction as examples. Cognitive Science, 14, 79–106.
Gelman, S. A. (2003). The essential child: Origins of essentialism in everyday thought. New York,
NY: Oxford University Press.
Gelman, R., & Williams, E. M. (1998). Enabling constraints for cognitive development and learn-
ing: Domain-specificity and epigenesis. In D. Kuhl & R. S. Siegler (Vol. Eds.), Cognition,
perception, and language, Vol. 2 (pp. 575–630). W. Damon (Gen. Ed.), Handbook of child
psychology (5th Ed.). New York, NY: Wiley.
Gigerenzer, G., Todd, P. M., & ABC Research Group (Eds.). (1999). Simple heuristics that make
us smart. New York, NY: Oxford University Press.
Gopnik, A., & Wellman, H. M. (2012). Reconstructing constructivism: Causal models, Bayesian
learning mechanisms, and the theory theory. Psychological Bulletin, 138, 1085–1108.
Grant, P. R. (1999). Ecology and evolution of Darwin’s finches. Princeton, NJ: Princeton University
Press.
Gray, P. (2013). Free to learn: Why unleashing the instinct to play will make our children happier,
more self-reliant, and better students for life. New York, NY: Basic Books.
Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child
Development, 58, 539–559.
Gurven, M., Kaplan, H., & Gutierrez, M. (2006). How long does it take to become a proficient
hunter? Implications for the evolution of extended development and long life span. Journal of
Human Evolution, 51, 454–470.
Hamilton, W. D. (1964). The genetical evolution of social behaviour. II. Journal of Theoretical
Biology, 7, 17–52.
Harter, S. (2006). The self. In N. Eisenberg (Vol. Ed.), Social, emotional, and personality develop-
ment, Vol. 3 (pp. 505–570). W. Damon & R. M. Lerner (Gen. Eds.), Handbook of child psychol-
ogy (6th Ed.). New York, NY: Wiley.
Heckhausen, J., & Schulz, R. (1995). A life-span theory of control. Psychological Review, 102,
284–304.
Hewstone, M., Rubin, M., & Willis, H. (2002). Intergroup bias. Annual Review of Psychology, 53,
575–604.
Hindson, B., Byrne, B., Shankweiler, D., Fielding-Barnsley, R., Newman, C., & Hine, D. W.
(2005). Assessment and early instruction of preschool children at risk for reading disability.
Journal of Educational Psychology, 97, 687–704.
Horowitz, D. L. (2001). The deadly ethnic riot. Berkeley, CA: University of California Press.
Huffman, K. J., Nelson, J., Clarey, J., & Krubitzer, L. (1999). Organization of somatosensory cor-
tex in three species of marsupials, Dasyurus hallucatus, Dactylopsila trivirgata, and
Monodelphis domestica: Neural correlates of morphological specializations. Journal of
Comparative Neurology, 403, 5–32.
Humphrey, N. K. (1976). The social function of intellect. In P. P. G. Bateson & R. A. Hinde (Eds.),
Growing points in ethology (pp. 303–317). New York, NY: Cambridge University Press.
Johnson-Frey, S. H. (2003). What’s so special about human tool use? Neuron, 39, 201–204.
Johnson-Laird, P. N. (1983). Mental models. Cambridge, England: Cambridge University Press.
Kahneman, D., & Tversky, A. (1982). The simulation heuristic. In D. Kahneman, P. Slovic, &
A. Tversky (Eds.), Judgment uncertainty: Heuristics and biases (pp. 201–208). Cambridge,
England: Cambridge University Press.
D.C. Geary and D.B. Berch
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
247
Kaiser, M. K., McCloskey, M., & Proffitt, D. R. (1986). Development of intuitive theories of
motion: Curvilinear motion in the absence of external forces. Developmental Psychology, 22,
67–71.
Kaminski, V. M., Sloutsky, V. M., & Heckler, A. F. (2006). Do children need concrete instantia-
tions to learn an abstract concept? In R. Sun & N. Miyake (Eds), Proceedings of the 27th
Annual Conference of the Cognitive Science Society (pp. 411–416). Mahwah, NJ: Lawrence
Erlbaum.
Kaminski, J. A., Sloutsky, V. M., & Heckler, A. F. (2008). The advantage of abstract examples in
learning math. Science, 230, 454–455.
Kaplan, H., Hill, K., Lancaster, J., & Hurtado, A. M. (2000). A theory of human life history
evolution: Diet, intelligence, and longevity. Evolutionary Anthropology, 9, 156–185.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does
not work: An analysis of the failure of constructivist, discovery, problem-based, experiential,
and inquiry-based teaching. Educational Psychologist, 41, 75–86.
Kisilevsky, B. S., Hains, S. M. J., Lee, K., Xie, X., Huang, H., Ye, H. H., … Wang, Z. (2003).
Effects of experience on fetal voice recognition. Psychological Science, 14, 220–224
Klahr, D., & Li, J. (2005). Cognitive research and elementary science instruction: From the labora-
tory, to the classroom, and back. Journal of Science Education and Technology, 14, 217–238.
Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction:
Effects of direct instruction and discovery learning. Psychological Science, 15, 661–667.
Kuhlmeier, V. A., & Boysen, S. T. (2002). Chimpanzees (Pan troglodytes) recognize spatial and object
correspondences between a scale model and its referent. Psychological Science, 13, 60–63.
Leslie, A. M. (1987). Pretense and representation: The origins of “theory of mind”. Psychological
Review, 94, 412–426.
Leslie, A. M., Friedman, O., & German, T. P. (2004). Core mechanisms in “theory of mind”.
Trends in Cognitive Sciences, 8, 528–533.
Lockman, J. J. (2000). A perception-action perspective on tool use development. Child
Development, 71, 137–144.
Lubinski, D. (2004). Introduction to the special section on cognitive abilities: 100 years after
Spearman’s (1904) “’general intelligence’, objectively determined and measured”. Journal of
Personality and Social Psychology, 86, 96–111.
Macfarlan, S. J., Walker, R. S., Flinn, M. V., & Chagnon, N. A. (2014). Lethal coalitionary aggres-
sion and long-term alliance formation among Yanomamö men. Proceedings of the National
Academy of Sciences USA, 111, 16662–16669.
Maguire, E. A., Burgess, N., Donnett, J. G., Frackowiak, R. S. J., Frith, C. D., & O’Keefe, J. (1998).
Knowing where and getting there: A human navigational network. Science, 280, 921–924.
Malt, B. C. (1995). Category coherence in cross-cultural perspective. Cognitive Psychology, 29,
85–148.
Mann, V. A. (1984). Reading skill and language skill. Developmental Review, 4, 1–15.
Markus, H. (1977). Self-schemata and processing information about the self. Journal of Personality
and Social Psychology, 35, 63–78.
McCloskey, M. (1983). Intuitive physics. Scientific American, 248, 122–130.
Meck, W. H., & Church, R. M. (1983). A mode control model of counting and timing processes.
Journal of Experimental Psychology: Animal Behavior Processes, 9, 320–334.
Medin, D. L., & Atran, S. (1999). Folkbiology. Cambridge, MA: MIT Press.
Medin, D. L., & Atran, S. (2004). The native mind: Biological categorization and reasoning in
development and across cultures. Psychological Review, 111, 960–983.
Medin, D. L., Ross, N. O., Atran, S., Cox, D., Coley, J., Proffitt, J. B., & Blok, S. (2006).
Folkbiology of freshwater fish. Cognition, 99, 237–273.
Milner, A. D., & Goodale, M. A. (1995). The visual brain in action. New York, NY: Oxford
University Press.
Mithen, S. (1996). The prehistory of the mind: The cognitive origins of art and science. New York,
NY: Thames and Hudson.
Murdock, G. P. (1981). Atlas of world cultures. Pittsburgh, PA: University of Pittsburgh Press.
9 Evolution and Development
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
248
Murray, C. (2003). Human accomplishment: The pursuit of excellence in the arts and sciences, 800
B.C. to 1950. New York, NJ: HarperCollins.
Newcombe, N. S. (2011). What is neoconstructivism? Child Development Perspectives, 5,
157–160.
Newell, A. (1973). You can’t play 20 questions with nature and win: Projective comments on
papers of this symposium. In W. G. Chase (Ed.), Visual information processing (pp. 283–308).
New York, NY: Academic.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
Newton, I. (1995). The principia. Amherst, NY: Prometheus Books. [Translated by A. Motte].
(Original work published in 1687).
O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. New York, NY: Oxford
University Press.
Oaksford, M., & Chater, N. (1998). Rationality in an uncertain world: Essays on the cognitive
science of human reasoning. East Sussex, England: Psychology Press.
Paivio, A. (2007). Mind and its evolution: A dual coding theoretical approach. Mahwah, NJ:
Erlbaum.
Pinker, S. (1994). The language instinct. New York, NY: William Morrow.
Potts, R. (1998). Variability selection in hominid evolution. Evolutionary Anthropology, 7, 81–96.
Povinelli, D. J. (2000). Folk physics for apes: The chimpanzees theory of how the world works.
New York, NY: Oxford University Press.
Richerson, P. J., & Boyd, R. (2005). Not by genes alone: How culture transformed human evolution.
Chicago, IL: University of Chicago Press.
Rozin, P. (1976). The evolution of intelligence and access to the cognitive unconscious. In J. M.
Sprague & A. N. Epstein (Eds.), Progress in psychobiology and physiological psychology
(Vol. 6, pp. 245–280). New York, NY: Academic.
Scarr, S. (1992). Developmental theories of the 1990s: Developmental and individual differences.
Child Development, 63, 1–19.
Schneider, D. J. (1973). Implicit personality theory: A review. Psychological Bulletin, 79,
294–309.
Schyns, P. G., Bonnar, L., & Gosselin, F. (2002). Show me the features! Understanding recognition
from the use of visual information. Psychological Science, 13, 402–409.
Sereno, S. C., & Rayner, K. (2003). Measuring word recognition in reading: Eye movements and
event-related potentials. Trends in Cognitive Sciences, 7, 489–493.
Setoh, P., Wu, D., Baillargeon, R., & Gelman, R. (2013). Young infants have biological expecta-
tions about animals. Proceedings of the National Academy of Sciences USA, 110,
15937–15942.
Shepard, R. N. (1994). Perceptual-cognitive universals as reflections of the world. Psychonomic
Bulletin & Review, 1, 2–28.
Shtulman, A. (2006). Qualitative differences between naïve and scientific theories of evolution.
Cognitive Psychology, 52, 170–194.
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review,
63, 129–138.
Simonton, D. K. (2009). Varieties of (scientific) creativity: A hierarchical model of domain-
specific disposition, development, and achievement. Perspectives on Psychological Science, 4,
441–452.
Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge.
Psychological Review, 99, 605–632.
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10, 89–96.
Spelke, E. S., & Kinzler, K. D. (2009). Innateness, learning, and rationality. Child Development
Perspectives, 3, 96–98.
Spencer, J. P., Blumberg, M. S., McMurray, B., Robinson, S. R., Samuelson, L. K., & Tomblin,
J. B. (2009). Short arms and talking eggs: Why we should no longer abide the nativist- empiricist
debate. Child Development Perspectives, 3, 79–87.
D.C. Geary and D.B. Berch
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
249
Sporns, O., Tononi, G., & Edelman, G. M. (2000). Connectivity and complexity: The relationship
between neuroanatomy and brain dynamics. Neural Networks, 13, 909–922.
Stanovich, K. E. (1999). Who is rational? Studies of individual differences in reasoning. Mahwah,
NJ: Erlbaum.
Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the
rationality debate? Behavioral and Brain Sciences, 23, 645–726.
Stanovich, K. E., & West, R. F. (2008). On the relative independence of thinking biases and
cognitive ability. Journal of Personality and Social Psychology, 94, 672–695.
Sternberg, R. J. (Ed.). (1999). Handbook of creativity. Cambridge, England: Cambridge University
Press.
Stevens, R. J., Slavin, R. E., & Farnish, A. M. (1991). The effects of cooperative learning and
direct instruction in reading comprehension strategies on mean idea identification. Journal of
Educational Psychology, 83, 8–16.
Suddendorf, T., & Corballis, M. C. (1997). Mental time travel and the evolution of the human
mind. Genetic, Social, and General Psychology Monographs, 123, 133–167.
Sweller, J. (2012). Human cognitive architecture: Why some instructional procedures work and
others do not. In K. R. Harris, S. Graham, & T. Urdan (Eds.), Educational psychology hand-
book (Theories, constructs, and critical issues, Vol. 1, pp. 295–325). Washington, DC: American
Psychological Association.
Sweller, J. (2015). In academe, what is learned, and how is it learned? Current Directions in
Psychological Science, 24, 190–194.
Tinbergen, N. (1963). On aims and methods of ethology. Zeitschrift für Tierpsychologie, 20,
410–433.
Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53,
1–25.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science,
185, 1124–1131.
Wagner, R. K., & Torgesen, J. K. (1987). The nature of phonological processing and its causal role
in the acquisition of reading skills. Psychological Bulletin, 101, 192–212.
Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1994). Development of reading-related phono-
logical processing abilities: New evidence of bidirectional causality from a latent variable lon-
gitudinal study. Developmental Psychology, 30, 73–87.
Wellman, H. M. (2014). Making minds: How theory of mind develops. New York, NY: Oxford
University Press.
Wellman, H. M., Fang, F., Liu, D., Zhu, L., & Liu, G. (2006). Scaling of theory-of-mind under-
standings in Chinese children. Psychological Science, 17, 1075–1081.
Wellman, H. M., Fang, F., & Peterson, C. C. (2011). Sequential progressions in a theoryofmind
scale: Longitudinal perspectives. Child Development, 82, 780–792.
Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: Foundational theories of core
domains. Annual Review of Psychology, 43, 337–375.
Wellman, H. M., & Liu, D. (2004). Scaling of theoryofmind tasks. Child Development, 75,
523–541.
West, R. F., Toplak, M. E., & Stanovich, K. E. (2008). Heuristics and biases as measures of critical
thinking: Associations with cognitive ability and thinking dispositions. Journal of Educational
Psychology, 100, 930–941.
White, M. (1999). Isaac Newton: the last sorcerer. New York, NY: Basic Books.
Zimmerman, C. (2005). The development of scientific reasoning skills: What psychologists con-
tribute to an understanding of elementary science learning. Report to the National Research
Council’s Board of Science Education, Consensus Study on Learning Science, Kindergarten
through Eighth Grade. Retrieved from http://www7.nationalacademies.org/bose/Corinne_
Zimmerman_Final_Paper.pdf.
9 Evolution and Development
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
... During 'window periods of human development' (also known as 'sensitive periods'), for limited topics, the brain evolved to instinctively, automatically, and seemingly effortlessly store knowledge gained from experience in LTM (Pinker 2007;Geary 2002;Geary and Berch 2016). As one example, creating speech is cognitively incredibly complex, but simply by exposure, children become fluent in speaking the language they hear spoken around them. ...
... Children also learn automatically in limited additional topics, including facial recognition, conventions of social relationships (evolutionary psychologists term these 'folk psychology'), and a practical 'folk physics' and 'folk biology' of how things work (Geary 2002;Geary and Berch 2016). . Over thousands of generations, these drives evolved to promote survival in difficult primitive environments. ...
... Because reading, writing, and mathematics did not assist in prehistoric survival, their learning did not evolve to be automatic. Non-instinctive learning can be achieved but nearly always requires effort, attention, rehearsal, and practice at recall (Geary 2002;Geary and Berch 2016). Learning that requires effort is termed evolutionarily secondary. ...
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Chemistry educator Alex H. Johnstone is perhaps best known for his insight that chemistry is best explained using macroscopic, submicroscopic, and symbolic perspectives. But in his writings, he stressed a broader thesis, namely that teaching should be guided by scientific research on how the brain learns: cognitive science. Since Johnstone’s retirement, science’s understanding of learning has progressed rapidly. A surprising discovery has been when solving chemistry problems of any complexity, reasoning does not work: students must apply very-well-memorized facts and algorithms. Following Johnstone’s advice, we review recent discoveries of cognitive science research. Instructional strategies are recommended that cognitive studies have shown help students learn chemistry.
... At the same time, not all forms of compatibility will reflect the idiosyncratic life history of the student. Our learning management systems should also align with any natural "tunings" or innate content biases that are representative of all human learners (Geary, 2002;Geary & Berch, 2016). The capacity to learn and remember is a product of evolution, guided by the criterion of fitness. ...
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... Political and social sciences describe a similar phenomenon, as powerful actors compete to frame problems that make their own interests and priorities more visible and influential [29]. Moreover, human beings have shared thought patterns, resulting from millions of years of evolution, that may need to be overcome intentionally in many of today's societies if we are to serve a greater good-such as improving the health and life experiences of all individuals [30]. Metaphors can align groups and cultures behind common ways of thinking and engender new cultural norms [31]. ...
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As human beings, we all have blind spots. Most obvious are our visual blind spots, such as where the optic nerve meets the retina and our inability to see behind us. It can be more difficult to acknowledge our other types of blind spots, like unexamined beliefs, assumptions, or biases. While each individual has blind spots, groups can share blind spots that limit change and innovation or even systematically disadvantage certain other groups. In this article, we provide a definition of blind spots in medical education, and offer examples, including unfamiliarity with the evidence and theory informing medical education, lack of evidence supporting well-accepted and influential practices, significant absences in our scholarly literature, and the failure to engage patients in curriculum development and reform. We argue that actively helping each other see blind spots may allow us to avoid pitfalls and take advantage of new opportunities for advancing medical education scholarship and practice. When we expand our collective field of vision, we can also envision more “adjacent possibilities,” future states near enough to be considered but not so distant as to be unimaginable. For medical education to attend to its blind spots, there needs to be increased participation among all stakeholders and a commitment to acknowledging blind spots even when that may cause discomfort. Ultimately, the better we can see blind spots and imagine new possibilities, the more we will be able to adapt, innovate, and reform medical education to prepare and sustain a physician workforce that serves society’s needs.
... Cognitive load theory explains how learning occurs and how educational technology can facilitate learning [3]. The information that we learn can be either primary or secondary [4]. Humans are evolutionarily programmed to acquire primary knowledge, such as speaking, without assistance. ...
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Objectives: The study of biological materials under a microscope is known as histology, which is one of the most challenging subjects for students. Our objective was to develop a learning tool that can reduce the extrinsic load of studying histology and make learning enjoyable and flexible. We used augmented reality (AR) to create a cellphone application called Dental AR. With Dental AR, students can use their cellphones as dynamic flashcards to hide or reveal the annotations of a histology slide. Our application enables students to study, practice, and self-test oral histology knowledge at their own pace. Methods: We used Unity3D with Vuforia to develop Dental AR. To generate a set of target images, oral histology glass slides were scanned and converted to digital images. Annotated versions of the slides were used as output for the corresponding target images. To understand user experiences and satisfaction with Dental AR, first-year dentistry students were invited to complete an online survey. Results: Dental AR was successfully developed and released on both the Apple and Google Play online app stores. The survey of dentistry students indicated overall satisfaction with Dental AR and willingness to use similar applications in other subjects. Conclusions: Dental AR can be used for in-class activities, gamification, and providing students with practice questions to study and self-test outside the classroom. This application can be expanded in the future to incorporate more target images, videos, and interactive components to make learning histology less challenging and more enjoyable.
Chapter
Beliefs play a central role in our lives. They lie at the heart of what makes us human, they shape the organization and functioning of our minds, they define the boundaries of our culture, and they guide our motivation and behavior. Given their central importance, researchers across a number of disciplines have studied beliefs, leading to results and literatures that do not always interact. The Cognitive Science of Belief aims to integrate these disconnected lines of research to start a broader dialogue on the nature, role, and consequences of beliefs. It tackles timeless questions, as well as applications of beliefs that speak to current social issues. This multidisciplinary approach to beliefs will benefit graduate students and researchers in cognitive science, psychology, philosophy, political science, economics, and religious studies.
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An evolutionary perspective on academic learning and schooling more generally helps us to understand why learning comes effortlessly in some domains (e.g., language) but only with extensive instruction and cognitive effort in others (e.g., mathematics); why many students’ self-concepts are more strongly influenced by physical traits and social relationships than by academic achievement; and why many problematic social behaviors, including bullying, persist in school settings. The articles in this special issue provide cutting edge reviews and empirical studies informed by this perspective and help to solidify the foundation for the nascent field of evolutionary educational psychology.
Article
Cognitive load theory is an instructional theory based on our knowledge of evolutionary psychology and human cognitive architecture. It can be used to provide instructional guidelines for the acquisition of all aspects of a second language by adults and some aspects, primarily reading and writing, of a first language by both children and adults. The theory assumes that knowledge can be divided into biologically primary knowledge that we have evolved to learn easily without conscious effort and biologically secondary knowledge that we have not specifically evolved to learn but can acquire with explicit instruction and conscious effort. Learning to listen to and speak a first language are biologically primary with all other aspects of language learning being biologically secondary. A general cognitive architecture governs the acquisition of biologically secondary knowledge. That architecture includes a working memory that is limited in capacity and duration when dealing with novel information but with no known limits when dealing with familiar information transferred from long-term memory. This architecture governs how we learn and accordingly, also determines the effectiveness of instructional designs.
Article
Human cognitive architecture has evolved throughout history, thus facilitating the processing of certain types of knowledge that emerged early on in evolution and that have an adaptive benefit (e.g., recognizing faces or food). Despite its complexity, primary knowledge is processed almost effortlessly, as opposed to secondary knowledge which developed later during the course of evolution and which requires extra cognitive resources and motivation for processing (e.g., “academic” knowledge, such as mathematics or grammar). Primary knowledge also constitutes the basis for secondary knowledge. Using primary knowledge to encourage individuals to invest in a task that is not motivating has therefore been used in recent studies as a promising avenue of research. This study presents 3 experiments in which university students had to complete statistics exercises — statistics being renowned as a difficult discipline typically disliked by students. The task presented problem-solving exercises which were identical in structure but which differed in content, by referring to either primary or secondary types of knowledge. Primary knowledge content, particularly when presented first, enhanced performance and efficiency while maintaining motivation during problem solving. Participants appeared to be unaware of this positive effect. By contrast, secondary knowledge content had a negative effect on performance and seemed to reduce motivation when presented first. These findings suggest that the use of easy-to-process primary knowledge can enhance learning — simply by manipulating task content and presentation order.
Article
Primary knowledge is the knowledge for which our cognitive architecture has evolved so that we acquire it quickly and effortlessly. We are intrinsically motivated to process it. Secondary knowledge is the knowledge for which our cognitive architecture has not had enough time to evolve: it requires time, cognitive resources and is hardly motivating. This study proposed to test these evolutionary characteristics using the experimental paradigm of logical reasoning. We conducted five experiments (n = 720) varying (i) the content of syllogisms (primary or secondary knowledge), (ii) the presentation order of the knowledge types, (iii) the added extrinsic cognitive load, and (iv) the type of syllogism. Results showed that primary knowledge increased performance, emotional and cognitive investment and decreased perceived cognitive load. Second, presenting primary knowledge first would encourage participants to be motivated throughout the task, while presenting secondary knowledge first would undermine their motivation. Third, secondary knowledge seemed to lead to a feeling of conflict that consumed cognitive resources. All together, these results suggested that primary knowledge should be taken into account and not left aside because it is something “already learned”.
Book
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I am pleased to announce the publication of Patterns of Human Growth, 3rd edition Barry Bogin 2021. The book may be purchased in paper or as an eBook from Cambridge University Press. Here is the description from the book cover. This new, completely revised and updated edition provides a synthesis of the forces that shaped the evolution of the human growth pattern, the biocultural factors that direct its expression, the intrinsic and extrinsic factors that regulate individual development and the biomathematical approaches that are needed to analyze and interpret human growth. After covering the history, philosophy and basic biological principles of human development, the book turns to the evolution of the human life cycle. The concepts and methods of Life History Biology are integrated here and throughout the book. Later chapters explore the physiological, environmental and cultural reasons for population variation in growth, and the genetic and endocrine factors that regulate individual development. The central place of Social-Economic-Political-Emotional forces on human development are explored and many historical and current examples are provided. The factors and forces form a hugely complex matrix of extensive interacting variables that shape human growth patterns. Novel and controversial concepts of community effects and strategic growth adjustments are introduced in this new edition. The final chapter integrates all this information into a truly interactive biocultural model of human development. This new edition will continue to be the primary text for students of human growth in the fields of anthropology, economics, psychology, public health, and education.
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Genetic and neurobiological research is reviewed as related to controversy over the extent to which neocortical organization and associated cognitive functions are genetically constrained or emerge through patterns of developmental experience. An evolutionary framework that accommodates genetic constraint and experiential modification of brain organization and cognitive function is then proposed. The authors argue that 4 forms of modularity and 3 forms of neural and cognitive plasticity define the relation between genetic constraint and the influence of developmental experience. For humans, the result is the ontogenetic emergence of functional modules in the domains of folk psychology, folk biology, and folk physics. The authors present a taxonomy of these modules and review associated research relating to brain and cognitive plasticity in these domains.
Book
After his famous visit to the Galápagos Islands, Darwin speculated that “one might fancy that, from an original paucity of birds in this archipelago, one species had been taken and modified for different ends.” This book is the classic account of how much we have since learned about the evolution of these remarkable birds. Based upon over a decade’s research, Grant shows how interspecific competition and natural selection act strongly enough on contemporary populations to produce observable and measurable evolutionary change. In this new edition, Grant outlines new discoveries made in the thirteen years since the book’s publication. Ecology and Evolution of Darwin’s Finches is an extraordinary account of evolution in action.
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The nature of the general factor of intelligence, or g, is examined. This article begins by observing that the finding of a general factor of intelligence appears to be inconsistent with current findings in neuroscience and cognitive science, where specific connections are argued to be critical for different intellectual abilities and the brain is argued to develop these connections in response to environmental stimuli. However, it is then observed that if people differed in neural plasticity, or the ability to adapt their connections to the environment, then those highly developed in one intellectual ability would be hiahly developed in other intellectual abilities as well. Simulations are then used to confirm that such a pattern would be obtained. Such a model is also shown to account for many other findings in the field of intelligence that are currently unexplained. A critical period for intellectual development is then emphasized.
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The goal of this handbook is to provide the most comprehensive, definitive, and authoritative single-volume review available in the field of creativity. The book contains twenty-two chapters covering a wide range of issues and topics in the field of creativity, all written by distinguished leaders in the field. The volume is divided into six parts. The introduction sets out the major themes and reviews the history of thinking about creativity. Subsequent parts deal with methods, origins, self and environment, special topics and conclusions. All educated readers with an interest in creative thinking will find this volume to be accessible and engrossing.