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How Humans Evolved General Intelligence [Copy link] 中文

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Post time 2014-7-12 01:05:10 |Display all floors
Good paper:

The Journal of General Psychology, 2005, 132 (1), 5–40

The Evolution of Domain-General Mechanisms in Intelligence and Learning
DAN CHIAPPE
KEVIN MACDONALD
Department of Psychology
California State University, Long Beach

ABSTRACT. For both humans and animals, domain-general mechanisms are fallible
but powerful tools for attaining evolutionary goals (e.g., resources) in uncertain, novel
environments that were not recurrent features of the environment of evolutionary adapt-
edness. Domain-general mechanisms interact in complex ways with domain-specific,
information-encapsulated modules,  most importantly by manipulating information
obtained from various modules in attempting to solve novel problems. Mechanisms of
general intelligence, particularly the executive functions of working memory, underlie
analogical reasoning as well as the decontextualization processes that are central to
human thought. Although there is a variety of evolved,  special purpose learning
devices, learning is also characterized by domain-general mechanisms that are able to
achieve evolutionary goals by making novel and serendipitous associations with envi-
ronmental cues.


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Post time 2014-7-12 01:06:57 |Display all floors
Link to full article:

http://www.csulb.edu/~kmacd/iq.pdf#page=1&zoom=130,-191,548

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Post time 2014-7-12 01:24:41 |Display all floors
Interesting quote from paper:

Associations between brain size and innovation have been found among both
mammals and birds. Reader and Laland (2002) found an association between exec-
utive brain ratio (neocortex and striatum volume over brainstem) and innovation,
tool use, and social learning. Their results suggested that there was selection among
primates for “adaptive complex variable strategies, such as inventing new behavior,
social learning, or using tools” (p. 4440). Social learning frequency was indepen-
dent of group size, providing support for ecological (foraging) hypotheses for brain
evolution in primates. Similarly, Lefebvre, Whittle, Lascaris, and Finkelstein (1997)
found a link between relatively larger forebrain structures and the frequency of
opportunistic foraging innovations for various avian orders. To count as a foraging
innovation, the behaviors had to be noted by field observers as being highly unusu-
al for the species; for example, using automatic sensors to open bus station doors
(House sparrow), using cars as nutcrackers for palm nuts (Common crow), opening
conch shells by dropping them on concrete-filled drums (Osprey), and so on.



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Post time 2014-7-12 01:31:49 |Display all floors
This post was edited by Pleistocene at 2014-7-12 01:33

Another quote:

The animal data fit well with research on humans, which has consistently
found more intelligent people are better at attaining goals in situations of mini-
mal prior knowledge. Of particular importance is fluid intelligence, defined as
“reasoning abilities [consisting] of strategies, heuristics, and automatized systems
that must be used in dealing with ‘novel’ problems, educing relations, and solv-
ing inductive, deductive, and conjunctive reasoning tasks” (Horn & Hofer, 1992,
p. 88). Tests of fluid intelligence produce the highest correlations with
g (Carpenter, Just, & Shell, 1990; Carroll, 1993; Duncan, Burgess, & Emslie, 1995).
Tests such as Raven’s Progressive Matrices and Cattell’s Culture Fair Test tap the
capacity “to adapt one’s thinking to a new cognitive problem” (Carpenter et al.,
p. 404). This highlights the idea that intelligence taps conscious problem solving
in situations in which past recurrences would be unhelpful, except perhaps by
analogy or by induction, to the new situation.


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Post time 2014-7-12 13:11:10 |Display all floors
Good quote:

Analogical reasoning therefore yields general problem solving schemas—
higher-order categories applicable across a wide range of domains of which the
specific analogs are instances (Holyoak, 1984). This decontextualization “deletes
differences between the analogs while preserving their commonalities” (Holyoak,
p. 208). Such decontextualization plays a role in the generation of new concepts
in science, as when the abstract concept of a wave is used to apply to vastly dif-
ferent domains. “Once a more abstract concept of a wave was established, it
played a role in the further extension [from water waves and sound waves] to light
waves” (Holyoak & Thagard, 1995, p. 23).

The process of creating new categories through analogical reasoning is also
evident in the metaphorical statements that are ubiquitous in natural language,
statements such as “crime is a disease,” “my job is a jail,” and “rumors are weeds”
(Chiappe, 2000; Lakoff & Johnson, 1980). The process of combining concepts in
metaphorical statements leads to the creation of categories that are more abstract
than the source and target concepts involved (Glucksberg, 2001). For example, the
metaphor “rumors are weeds” leads to the creation of the category “undesirable
things that spread quickly and uncontrollably.” Once generated, this category can
be applied to a wide range of novel situations.



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Post time 2014-7-12 14:30:24 |Display all floors
Interesting quote:

Evolutionary analyses of learning emphasize that learning mechanisms
imply a great deal of evolved machinery and that they are often biased in ways
that make certain types of learning easier than others (Garcia & Koelling, 1966;
Öhman & Mineka, 2001; Rescorla, 1988; Rozin & Schull, 1988). A paradigmat-
ic example is taste aversion learning, which is observable in a wide range of
species, including quail, bats, catfish, cows, coyotes, and slugs (Kalat, 1985). If
a rat consumes food and later feels nauseous, then it associates the illness with
the food rather than with other more recent stimuli such as lights and sounds, and
it will make this association over much longer periods of delay than is typical for
other examples of learning. The association of food with poison is greatly influ-
enced by whether the food is unfamiliar to the animal—an indication that taste
aversion learning is an adaptation to nonrecurrent and unpredictable features of
the environment.

This example shows that some types of novelty are sufficiently recurrent to
yield dedicated, domain-specific mechanisms designed to cope with them. Recur-
rent novelty occurs when organisms have been confronted over an evolutionari-
ly significant period with a need to evaluate a specific kind of novel situation,
such as rats evaluating novel foods. Novel food items are a potential resource for
the animal and must not be ignored, even though they are more likely to be dan-
gerous. Novel food items were a recurrent but unpredictable feature of the rat’s
EEA, with the result that the animal has evolved adaptations that minimize the
cost of sampling this novelty. Rats will also preferentially eat novel food that they
have smelled on the breath of another rat (Galef, 1987), thereby minimizing the
danger of trial-and-error learning and demonstrating the utility of specialized
social learning mechanisms that evolved to adapt to recurrent problems involv-
ing specific sources of novelty


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Post time 2014-7-12 19:06:30 |Display all floors
Quote:

Conclusion

Evolutionary psychology has been of great value in placing evolutionary
thinking at the center of cognitive science. However, by erecting an equally one-
sided paradigm in opposition to the standard social science model, it runs the risk
of overemphasizing modularity and ignoring the vast data indicating a prominent
role for domain-general mechanisms in human and animal cognition. As
described here, domain-general mechanisms are not weak “jacks-of-all-trades but
masters of none.” They are extremely powerful but fallible mechanisms that are
the basis for solving a fundamental problem faced by all but the simplest organ-
isms—the problem of navigating constantly changing environments that present
new challenges that have not been recurrent problems in the EEA. Most impor-
tant, the domain-general mechanisms at the heart of human cognition are respon-
sible for the decontextualization and abstraction processes critical to the scien-
tific and technological advances that virtually define civilization.

The processes discussed here are not meant to be an exhaustive examination
of domain generality in cognition and learning, but merely illustrative. We sup-
pose that a great many other processes will yield to the type of analysis presented
here, including other forms of reasoning and induction besides analogical reason-
ing, memory and categorization, developmental plasticity, and large areas of per-
sonality psychology in which, as in the analysis of the fear system presented
heretofore, there is a complex interplay between evolved emotional responses to
specific stimuli as well as the ability to recruit emotional systems to confront
entirely novel dangers and opportunities (Chiappe & MacDonald, 2002).



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