Evolution, IQ, and Domain General Mechanisms (DGPM)
PSYCHOLOGICAL MECHANISMS (DGPM)
DOMAIN-SPECIFIC PSYCHOLOGICAL MECHANISMS (DSPM)
DSPM SOLVES SPECIFIC PROBLEM OF SURVIVAL OR REPRODUCTION THAT WAS RECURRENT
IN THE EEA.
DGPM DOES NOT SOLVE SPECIFIC PROBLEM OF SURVIVAL OR REPRODUCTION;
DGPM IS AIMED AT SOLVING NON-RECURRENT, COMPLEX, NOVEL PROBLEMS IN AN
ADAPTIVE MANNER BY ACHIEVING EVOLVED MOTIVE DISPOSITIONS.
cognitive architecture evolved to solve
problems our ancestors faced during the Pleistocene.
humans, the situations our ancestors encountered as
Pleistocene hunter-gatherers define the array of adaptive problems our
cognitive mechanisms were designed to solve” (Cosmides & Tooby, 1994,
mind is made up of many domain-specific mechanisms= “modules.”
are designed to solve problems that range from
“solicitation of assistance from one’s parents, to language
acquisition, to modeling the spatial distribution of local objects, to
coalition formation and cooperation, to the deduction of intentions on
the basis of facial expressions, to avoiding incest…, to the
interpretation of threats, to mate selection, to object recognition” (Cosmides
& Tooby, 1994, p. 88).
are “dedicated intelligences” that receive characteristic inputs and
produce characteristic output.
operation is mandatory (i.e., they are automatically triggered in the
presence of appropriate environmental stimulation), fast, and
unconscious (but their output may be conscious). (= implicit processing
have a built-in sense of relevance about what information is needed to
solve an adaptive problem.
are sensitive to correlated features of the evolutionary environment.
1: Waist-to-hip ratio is an easily perceivable cue correlated with the
ability to have future offspring (Singh, 1993).
2: Facial recognition module.
3: Spatial information module—rotating figures in space, etc.
adapt to recurrent problems in the EEA (= the Environment of
Evolutionary Adaptedness—the environment humans evolved in which
presented the problems solved by the set of human adaptations).
When the environment
recurrent problems, the optimal
solution is to develop domain-specific cognitive and psychological
mechanisms specialized to handle specific types of input and generate
certain types of solutions.
conditions that disappear after a single or a few generations may lead
to some temporary change in the frequency of designs, but the associated
selection pressures will disappear or reverse as often as conditions do.
Therefore, it is only those conditions that recur, statistically
accumulating across many generations, that lead to the construction of
complex adaptations (Tooby & Cosmides, 1992, p. 69).
Modular Information processing domains: David Geary,
The Origin of Mind, 2005.
awareness: Represent self as social being and have a sense of
persistence of self through time
schema: Knowledge of one’s own personality and relationships with other
behavior: e.g., postural cues
of Mind: Ability to infer intentions, beliefs, emotional states and
future behavior of individuals; no evidence in monkeys; controversial in
Schema: Knowledge of specific other people and their networks
recognition: Mothers and babies recognize each other by smell;
children able to identify odor of full siblings, not half
siblings or step-siblings
humans classify flora and fauna on basis of morphology, behavior, growth
patterns, and ecological niche;
of anterior temporal cortex disrupts ability to name living but not
non-living things. (Not conclusive)
sensitive to invariant features of physical space.
humans, this includes ability to mentally represent physical objects and
manipulate the objects, as in tool use. These may engage working memory:
via mental maps of routes and landmarks both involve parietal cortex,
but route task also involved hippocampus. Posterior hippocampus of taxi
drivers in London larger than age-matched men. Volume correlated with
time spent as taxi driver (Maguire et al., 2000)
Domain General Mechanisms and Explicit processing
nAble to define resources
and develop strategies (plans) to achieve goals in rapidly changing,
variable, and unpredictable environments.
Fear as an illustration of domain specific and domain general systems
Fear as a modular, domain specific
mechanism: Stimulus of a rapidly
approaching object or a snake automatically triggers fear response. LeDoux’s
Low Road mechanism: Stimulus is processed by amygdala and there is an
automatic fear reaction that occurs before we are consciously aware of doing
it. This is a specialized, modular, domain specific circuit. It is a
nFear as a conscious,
non-modular, domain-general mechanism. LeDoux’s high road mechanism. The
stimulus of the approaching object or the snake travels
to the prefrontal cortex, but it takes a much longer time to get
there than the low road mechanism. When it does, we can respond in a variety
of ways: Is the stimulus really potentially harmful is the noise just a car
backfiring? Should I call 911? Plan some elaborate escape? Reach for my gun?
These all require elaborate cognitive abilities, knowledge of the world,
nExample: Child opening a
jack-in-the-box: At first, she smiles in anticipation. Then she shrinks back
and shows a fear expression when the box is opened and the jack-in-the-box
jumps out. This is the reflexive, modular fear response (the low road). Then
she smiles when she understands that the jack-in-the-box is harmless. This
is a non-reflexive, cognitive appraisal (the high road).
How Domain general mechanisms can evolve:
nProblem: How could domain
general mechanisms evolve if they don’t solve any particular problem?
nAnswer: They could evolve if
they made it easier to attain evolutionary goals like survival and
nBut modules do that as well.
What’s so special about domain general mechanisms?
nAnswer: Domain general
mechanisms allow us to take in non-recurrent and rapidly changing features
of the environment into account. Remember, domain-specific modules evolved
because they solved problems that were recurrent over evolutionary time.
Rapidly approaching objects and snakes were recurrent problems, and humans
evolved modules that respond to them. So there is an automatic, modular,
reflexive, low road fear response to these events. However, the domain
general mechanism allows us to think about the context much more broadly (Is
the object really going to hit me? Is the snake safely confined to a cage?).
And we can try to think up new ways to deal with these problems beyond just
reflexively respond by jumping away (Get a gun; call 911; realize there is
no real problem even though we feel the fear).
Hierarchical model of motivation showing relationships between domain-specific
and domain-general mechanisms.
Level 1 EVOLVED MOTIVE DISPOSITIONS: What people naturally want: Safety,
love, sex, status
Level 2 PERSONAL STRIVINGS: Particular example of a natural want in a
Level 3 CONCERNS, PROJECTS, TASKS
(Utilize Domain-General Mechanisms)
Level 4 SPECIFIC ACTION UNITS
(Utilize Domain-General Mechanisms)
Evolved Motive Disposition: INTIMACY
Personal Striving: INTIMATE RELATIONSHIP WITH A PARTICULAR PERSON
Concern, Project, Task: Arrange Meeting Improve appearance Get promotion
Action Units: Find phone number Begin dieting Work on weekends
Evolution and General Intelligence
When the environment presents recurrent problems, the optimal solution is to
develop domain-specific cognitive and psychological mechanisms specialized to
handle specific types of input and generate certain types of solutions.
However, the human EEA contained both recurrent and non-recurrent problems.
The argument is that the incredibly rapid radiation of humans resulted in
recurrent situations of novelty and unpredictability.
Three hypotheses on the evolution of general intelligence
nAll hypotheses view
intelligence as important for coping with novelty.
hypotheses are compatible with evolutionary trend toward increased
encephalization (larger brain size, especially
cortex). Humans became the species with the big brain.
n1. The climatic variation
hypothesis proposes that human intelligence was mainly beneficial in
decoupling humans from dependence on
any particular ecology defined by a constant climate or other invariant
features. According to his hypothesis, general intelligence allowed humans
to adapt to rapidly changing climates of the Pleistocene and greatly
increased their range of settlement.
n2. The foraging hypothesis
highlights the advantages to be gained from better methods for extracting
resources from the environment (e.g., managed foraging), and in enlarging
the range of human settlement and supporting larger populations. The
foraging hypothesis is supported by data indicating that humans evolved as
superpredators and manufacturers of highly complex tools by around 50,000
years ago, resulting in a wave of mass extinctions of large animals. These
changes coincide not only with a larger brain but a smaller gastrointestinal
tract and higher metabolism dependent on high quality food made possible by
these improved foraging techniques.
n3. The social competition
hypothesis proposes that after humans achieved ecological dominance,
intelligence evolved because of it was beneficial for between-group and
within-group competition among humans. This hypothesis emphasizes that
cognitively, socially, and behaviorally sophisticated individuals are able
to outmaneuver and manipulate other individuals to gain control of resources
in the local ecology and to gain control of the behavior of other people.
This hypothesis is supported by correlations between brain size and group
size, especially where there are complex social relationships within groups.
Functions of General Intelligence
Discussions of general
intelligence emphasize that intelligence is useful in solving novel problems.
From an evolutionary perspective, a critical function is the attainment of
evolutionary goals in unfamiliar and novel conditions characterized by a minimal
amount of prior knowledge.
nIntelligence is about being
able to solve novel problems. The more teachers have to repeat material, the
less intelligence is required for understanding it.
Research on intelligence has consistently found that more intelligent people
are better at attaining goals in unfamiliar and novel conditions characterized
by a minimal amount of prior knowledge. As Carl Bereiter notes, intelligence is
'what you use when you don't know what to do' (in Jensen, 1998; p. 111). This
highlights the idea that intelligence taps conscious problem solving in novel
situations'situations where past recurrences would be unhelpful except perhaps
by analogy or induction to the new situation.
Discussions of intelligence also emphasize conscious problem solving
rather than the unconscious, automatic processing characteristic of
domain-specific systems designed to solve recurrent adaptive problems.
Contrasts between general intelligence and domain-specific modules
problem solving) involves:
n 1, focused
n 3. is
relatively slow compared as compared to automatic processing
n 4. deals
with information input sequentially, and therefore is able to deal with only
very limited amounts of information at one time,
n 5. and
is unable to execute different mental operations simultaneously
nDomain Specific Modules:
n 1. Don’t
Unconscious, automatic processing
n 3. Are
relatively fast compared as compared to effortful problem solving
n 4. deal
with information input in parallel, and therefore is able to deal with a
huge amount of information at one time,
n 5. are
able to execute different mental operations simultaneously (parallel).
Implicit System Explicit System
Early Evolved Late
May be unique to humans
specific Domain general
capacity Limited by attentional and
working memory resources.
by biology or
Acquisition by culture formal
General intelligence is at the top of a hierarchy of
cognitive mechanisms. It takes in information from a variety of different
sources, combines it, and produces an output. The following figure can be
thought of as showing how general intelligence takes in information from
spatial, numerical, social/verbal, logic/analysis, and causal mechanisms. These
five mechanisms may be thought of as Domain Specific Psychological Mechanisms,
but g is domain general: It is not restricted to a narrow sort of information
and it is not designed to solve a specific problem.
Information from emotional mechanisms also provide input into consciousness
and influence decision making, as shown in the hierarchical model of motivation
at the beginning of these notes.
IQ IS A DOMAIN-GENERAL ADAPTATION. HIGH INTELLIGENCE IS AN ADVANTAGE IN
NOVEL, COMPLEX, CONSTANTLY CHANGING ENVIRONMENTS WHERE PEOPLE MUST ATTEND TO A
MULTIPLICITY OF TASKS AND WHERE LEARNING NEW SKILLS HAS GREAT PAYOFFS.
PEOPLE WITH HIGH IQ ARE ABLE TO USE DSMG'S IN NOVEL WAYS TO SOLVE PROBLEMS;
E.G., READING USES LANGUAGE DSMG'S IN NOVEL MANNER. HIGH-IQ PEOPLE READ BETTER,
USE READING TO SOLVE ENVIRONMENTAL PROBLEMS BETTER AND ATTAIN HIGHER SOCIAL
STATUS (SATISFY EMD).
SPEARMAN'S g = GENERAL INTELLIGENCE, ASSOCIATED WITH PERFORMANCE ON A
WIDE RANGE OF INTELLECTUAL MEASURES VERBAL COMPREHENSION, VERBAL FLUENCY,
NUMBER, SPATIAL VISUALIZATION, MEMORY, REASONING, PERCEPTUAL SPEED
VALIDITY: THE ACCURACY WITH WHICH A MEASURING INSTRUMENT ASSESSES THE
ATTRIBUTE THAT IT IS DESIGNED TO MEASURE IQ IS VALIDATED WITH MEASURES OF SCHOOL
PERFORMANCE 0.5 < r < 0.7
THE IMPORTANCE OF IQ: A SIBLING STUDY
SIBLING STUDIES CONTROL FOR FAMILY INFLUENCES: Socio-Economic Status, PARENTING
PRACTICES, NEIGHBORHOOD, ETC.
ONE SIBLING WITH IQ OF 90-110 = NORMAL
OTHER SIBLING WITH IQ > 110 =
OTHER SIBLING WITH IQ < 90 = DULL
N = 710; BOTH SEXES; AGED
28-36; PARENTS IN TOP 75% OF INCOME;
DULL NORMAL BRIGHT
INCOME IN 1993: $23,600 $33,600 $44,800
DEGREE: 2% 21% 56%
(FIRST CHILD) 45% 21% 10%
NUMBER OF CHILDREN: 1.9 1.4 1.4
BRIGHT SIB 6-1/2 TIMES MORE LIKELY TO MAKE OVER $100,000. BRIGHT SIBS WITHOUT
COLLEGE DEGREE EARNED MORE THAN NORMALS WITHOUT COLLEGE DEGREE
DULL SIB 5 TIMES MORE LIKELY TO BE BELOW THE POVERTY LINE. 16.3% OF DULL SIBS
BELOW POVERTY LINE; THEY TEND TO HAVE MENIAL OCCUPATIONS;
'MARKEDLY HIGHER' DIVORCE RATE THAN NORMAL OR BRIGHT;
DULL WOMEN HAD FIRST CHILD 4 YEARS EARLIER THAN NORMAL OR BRIGHT.
Click on this link to read "The General Intelligence Factor", by Linda
This article appeared in Scientific American, November 1998. It
discusses the importance of IQ in the contemporary world.