ABSTRACT
Categorical perception is a phenomenon in which people are better able to distinguish between stimuli along a physical continuum when the stimuli come from different categories than when they come from the same category. In a laboratory experiment with human subjects, we find evidence for categorical perception along a novel dimension that is created by interpolating (i.e. morphing) between two randomly selected bezier curves. A neural network qualitatively models the empirical results with the following assumptions: 1) hidden "detector" units become specialized for particular stimulus regions with a topologically structured competitive learning algorithm, 2) simultaneously, associations between detectors and category units are learned, and 3) feedback from the category units to the detectors causes the detectors to become concentrated near category boundaries. The particular feedback used, implemented in an "S.O.S. network," operates by increasing the learning rate of weights connecting inputs to detectors that are neighbors to a detector that produces an improper categorization.
