title: Occasion setting: a neural network approach.
abstract: Classical conditioning data show that a conditioned stimulus (CS) can act either as a simple CS--eliciting conditioned responses (CRs) by signaling the occurrence of an unconditioned stimulus (US)--or as an occasion setter--controlling the responses generated by another CS. In this article, the authors apply a simple extension of a network model of conditioning, originally presented by N. A. Schmajuk and J. J. DiCarlo (S-D; 1992), to the description of these 2 different CS functions. In the model, CS inputs are connected to the CR output both directly and indirectly through a hidden unit layer that codes configural stimuli. In this framework, a CS acts as (a) a simple stimulus through its direct connections with the output units and as (b) an occasion setter through its indirect configural connections via the hidden units. Computer simulations demonstrate that the network accounts for a large part of the data on occasion setting.
authors.affiliation: Department of Psychology: Experimental, Duke University, Durham, North Carolina 27708-0086, USA. nestor@acpub.duke.edu
authors.forename: N A
authors.initials: NA
authors.lastname: Schmajuk
authors.forename: J A
authors.initials: JA
authors.lastname: Lamoureux
authors.forename: P C
authors.initials: PC
authors.lastname: Holland
mesh_terms: D000818:Animals; D010856:Columbidae; D003198:Computer Simulation; D003214:Conditioning, Classical / Q000502:physiology*; D004193:Discrimination Learning; D008959:Models, Neurological; D009415:Nerve Net; D011582:Psychological Theory; D051381:Rats