http://cogprints.org/1647/
From Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level Categories
Neural network models of categorical perception (compression of within-category similarity
and dilation of between-category differences) are applied to the symbol-grounding problem
(of how to connect symbols with meanings) by connecting analog sensorimotor projections to
arbitrary symbolic representations via learned category-invariance detectors in a hybrid
symbolic/nonsymbolic system. Our nets are trained to categorize and name 50x50 pixel
images (e.g., circles, ellipses, squares and rectangles) projected onto the receptive field of a
7x7 retina. They first learn to do prototype matching and then entry-level naming for the four
kinds of stimuli, grounding their names directly in the input patterns via hidden-unit
representations ("sensorimotor toil"). We show that a higher-level categorization (e.g.,
"symmetric" vs. "asymmetric") can learned in two very different ways: either (1) directly
from the input, just as with the entry-level categories (i.e., by toil), or (2) indirectly, from
boolean combinations of the grounded category names in the form of propositions describing
the higher-order category ("symbolic theft"). We analyze the architectures and input
conditions that allow grounding (in the form of compression/separation in internal similarity
space) to be "transferred" in this second way from directly grounded entry-level category
names to higher-order category names. Such hybrid models have implications for the
evolution and learning of language.
Cangelosi, Angelo
Greco, Alberto
Harnad, Stevan
Cognitive Psychology
Neural Nets
Angelo
Cangelosi
Alberto
Greco
Stevan
Harnad