title: What is learned in sequential learning? An associative model of reward magnitude serial-pattern learning.
abstract: A computational model of sequence learning is described that is based on pairwise associations and generalization. Simulations by the model predicted that rats should learn a long monotonic pattern of food quantities better than a nonmonotonic pattern, as predicted by rule-learning theory, and that they should learn a short nonmonotonic pattern with highly discriminable elements better than 1 with less discriminable elements, as predicted by interitem association theory. In 2 other studies, the model also simulated behavioral "rule generalization," "extrapolation," and associative transfer data motivated by both rule-learning and associative perspectives. Although these simulations do not rule out the possibility that rats can use rule induction to learn serial patterns, they show that a simple associative model can account for the classical behavioral studies implicating rule learning in reward magnitude serial-pattern learning.
authors.affiliation: Department of Psychology, Kent State University, Kent, Ohio 44242-0001, USA.
authors.forename: Douglas G
authors.initials: DG
authors.lastname: Wallace
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Fountain
mesh_terms: D000818:Animals; D001245:Association Learning; D004193:Discrimination Learning; D005793:Generalization, Psychological; D008960:Models, Psychological; D051381:Rats; D012201:Reward