document: A computational model of learned avoidance behavior in a one-way avoidance experiment We present a computational model of learned avoidance behavior in a one-way avoidance experiment . Our model employs the reinforcement learning paradigm and a temporal-difference algorithm to implement both classically conditioned and instrumentally conditioned components . The role of the classically conditioned component is to develop an expectation of future benefit that is a function of the learning system 's state and action . Competition among the instrumentally conditioned components determines the overt behavior generated by the learning system . Our model displays , in simulation , the reduced latency of the avoidance behavior during learning with continuing trials and the resistance to extinction of the avoidance response . These results are consistent with experimentally observed animal behavior . Our model extends the traditional two-process learning mechanism of Mowrer -LRB- 1947 -RRB- by explicitly defining the mechanisms of proprioceptive feedback , an internal clock , and generalization over the action space
doc_bio_tags: O B I O B I I O O O O O O O O B I O B I I O O O O O O O O O O B I O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O
extractive_keyphrases: computational model learned avoidance behavior reinforcement learning instrumentally conditioned components reduced latency animal behavior proprioceptive feedback internal clock
abstractive_keyphrases: one-way avoidance experiment temporal-difference algorithm classically conditioned components traditional two-process learning mechanism