PhD Thesis now available as postscript (Conn. Models of Cat.)

[Adriaan Tijsseling]

The following PhD thesis is available as postscript via

http://cogito.psy.soton.ac.uk/~at/CALM/Title.ps
http://cogito.psy.soton.ac.uk/~at/CALM/Abstract.ps
http://cogito.psy.soton.ac.uk/~at/CALM/PhD.ps
http://cogito.psy.soton.ac.uk/~at/CALM/Colors.ps

Adriaan Tijsseling

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Abstract
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Connectionist Models of Categorization: A Dynamical View of Cognition

by Adriaan Tijsseling


The functional role of altered similarity structure in categorization 
is analyzed. 'Categorical Perception' (CP) occurs when equal-sized 
physical differences in the signals arriving at our sensory receptors 
are perceived as smaller within categories and larger between 
categories (Harnad, 1987). Our hypothesis is that it is by modifying 
the similarity between internal representations that successful 
categorization is achieved. This effect depends in part on the 
iconicity of the inputs, which induces a similarity preserving 
structure in the internal representations. Categorizations based on the 
similarity between stimuli are easier to learn than contra-iconic 
categorization; it is mainly to modify the latter in the service of 
categorization that the characteristic compression/separation of CP 
occurs.
	This hypothesis was tested in a series of neural net simulations of 
studies on category learning in human subject. The nets are first 
pre-exposed to the inputs and then given feedback on their performance. 
The behavior of the resulting networks was then analyzed and compared 
to human performance.
	Before it is given feedback, the network discriminates and 
categorizes input based on the inherent similarity of the input 
structure. With corrective feedback the net moves its internal 
representations away from category boundaries. The effect is that 
similarity of patterns that belong to different categories is 
decreased, while similarity of patterns from the same category is 
increased (CP). Neural net simulations make it possible to look inside 
a hypothetical black box of how categorization may be accomplished; it 
is shown how increased attention to one or more dimensions in the input 
and the salience of input features affect category learning.
	Moreover, the observed 'warping' of similarity space in the service 
of categorization can provide useful functionality by creating compact, 
bounded chunks (Miller, 1965) with category names that can then be 
combined into higher-order categories described by the symbol strings 
of natural language and the language of thought (Greco, Cangelosi, & 
Harnad, 1997). The dynamic models of categorization of the kind 
analyzed here can be extended to make them powerful models of 
neuro-symbolic processing (Casey, 1997) and a fruitful territory for 
future research.