Abstract:
Anurans (frogs and toads) show quite flexible behavior when confronted
with stationary objects on their way to prey or when escaping from a
threat. Rana computatrix (Arbib, 1987), an evolving computer model of
anuran visuomotor coordination, models complex behaviors such as
detouring around a stationary barrier to get to prey on the basis
of an understanding of anuran prey and barrier recognition, depth
perception, and appropriate motor pattern generation mechanisms
based on sensory perception. Our present analysis of detour behavior
goes beyond other models by incorporating new data from our laboratory
demonstrating a learning component in anuran detour behavior. Building
on earlier work showing how interacting schemas may be used to
analyze a complex environment to generate an appropriate course of
behavior, we turn to the question: How are the relevant schemas
adapted? How are schemas combined to form new schema assemblages
acquired for the system to become more efficient? We describe
the contruction mechanisms and interactions with the environment
that are necessary to achieve higher levels of detour performance.
We have based this article mostly on data about learning to detour
when approaching prey, but the model offers a strategy for learning
to detour in general. Moreover, we have attempted to solve the
problem in a general way so that the model of learning to detour
points the way to a general theory of schema-based learning.