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Abstract:
Using neural nets to simulate learning and the genetic
algorithm to simulate evolution in a toy world of mushrooms and
mushroom-foragers, we create a competition between two ways of
learning the same information. One way ("sensorimotor toil")
acquires new categories through real-time trial and error
experience, guided by corrective feedback.; the other way
("symbolic theft") acquires new categories from propositions made
up strings of symbols describing the new category. In competition,
symbolic theft always beats sensorimotor toil, and we conjecture
that this is the basis of the adaptive advantage of language.
Because of the symbol grounding problem, however, ground-level
categories must still be learned by toil by all. The changes in
internal representations that occur during the course of learning
are analysed in terms of a compression of within-category distances
and expansion of between-category that allows regions of similarity
space to be separated, bounded and named, then allow the names to
be combined and recombined to describe further categories, grounded
in the existing ones. The compression/expansion effects, called
"categorical perception" (CP), have previously been reported with
categories acquired by sensorimotor toil; we show further CP
effects induced by symbolic theft alone.
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