Empirical psychological experimentation (very briefly reviewed here) has provided evidence of top-down conceptual constraints on letter perception. The role hypothesis suggests that these conceptual constraints take the form of structural subcomponents (roles) and relations between subcomponents (r-roles). In this paper, we present a fully-implemented computer model based on the role hypothesis of letter recognition. The emergent model of letter perception discussed below offers a cogent explanation of human letter-perception data — especially with regard to error-making. The model goes beyond simple categorization by parsing a letter-form into its constituent parts. As it runs, the model dynamically builds (and destroys) a context-sensitive internal representation of the letter that it is perceiving. The representation emerges as by-product of a parallel exploration of possible categories. The model is able to successfully recognize (i.e., conceptually parse) many diverse letters at the extremes of their categories.

We use a connectionist network trained with rein- forcement to control both an autonomous robot ve- hicle and a simulated robot. We show that given appropriate sensory data and architectural struc- ture, a network can learn to control the robot for a simple navigation problem. We then investigate a more complex goal-based problem and examine the plan-like behavior that emerges.

We present the results of a simple experiment in lowercase letter recognition. Unlike most psychology studies of letter recognition, we include in our data set letters at the extremes of their categories and investigate the recognition of letters of multiple typefaces. W e are interested in the relationship between the recognition of normal letters and the recognition of non-standard letters. Results provide empirical evidence for top-down conceptual constraints on letter perception in the form of roles and relations between perceptually-based structural subcomponents. A process model based on the hypothesis developed below is currently being implemented.