Connectionist models of categorization a dynamical approach to cognition

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 characteristics 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 behaviour 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 simulation 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, Cangelois, & Harnard, 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.