Situational descriptions of behavioral procedures: the in situ testbed.

We demonstrate the In Situ testbed, a system that aids in evaluating computational models of learning, including artificial neural networks. The testbed models contingencies of reinforcement rising an extension of Mechner's (1959) notational system for the description of behavioral procedures. These contingencies are input to the model under test. The model's output is displayed as cumulative records. The cumulative record can then be compared to one produced by a pigeon exposed to the same contingencies. The testbed is tried with three published models of learning. Each model is exposed to up to three reinforcement schedules (testing ends when the model does not produce acceptable cumulative records): continuous reinforcement and extinction, fixed ratio, and fixed interval. The In Sitt testbed appears to be a reliable and valid testing procedure for comparing models of learning.