title: Parallel neural systems for classical conditioning: support from computational modeling.
abstract: Classical conditioning has been explained by two main types of theories that postulate different learning mechanisms. Rescorla and Wagner (1972) put forth a theory in which conditioning is based on the ability of the US to drive learning through error correction. Alternatively, Mackintosh (1973) put forth a theory in which the ability of the CS to be associated with the unconditioned stimulus is modulated. We have proposed a reconciliation of these two mechanisms as working in parallel within different neural systems: a cerebellar system for US modulation and a hippocampal system for CS modulation. We developed a computational model of cerebellar function in eyeblink conditioning based on the error correction mechanism of the Rescorla-Wagner rule in which learning-related activity from the cerebellum inhibits the inferior olive, which is the US input pathway to the cerebellum (Gluck et al., 1994). We developed a computational model of the hippocampal region that forms altered representations of conditioned stimuli based on their behavioral outcomes (Gluck & Myers, 1993; Myers et al., 1995). Overall, computational modeling and empirical findings support the idea that, at least in the case of eyeblink conditioning, there may be two different neural systems: the cerebellum which mediates US-based error correction and hippocampus which alters representations of CSs.
authors.affiliation: Center for Molecular & Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA. allen@axon.rutgers.edu
authors.forename: M T
authors.initials: MT
authors.lastname: Allen
authors.forename: C E
authors.initials: CE
authors.lastname: Myers
authors.forename: M A
authors.initials: MA
authors.lastname: Gluck
mesh_terms: D000818:Animals; D003214:Conditioning, Classical / Q000502:physiology*; D005795:Generalization, Stimulus; D008959:Models, Neurological; D016571:Neural Networks, Computer; D011817:Rabbits; D051381:Rats