Toward a Unified Model of Attention in Associative Learning

Abstract

Two connectionist models of attention in associative learning, previously used to model human category learning, are shown to have special cases that are essentially equivalent to N. J. Mackintosh's (1975, Psychological Review, 82, 276–298) classic model of attention in animal learning. The models unify formulas for associative weight change with formulas for attentional change, under a common goal of error reduction. Error-driven attentional shifting accelerates learning of new associations but also protects previously learned associations from retroactive interference. The models are fit to data from a recent experiment in human associative learning (J. K. Kruschke & N. J. Blair, 2000, Psychonomic Bulletin & Review, 7, 636–645), which shows that blocking of learning involves learned inattention. The approach also provides a novel and unifying theory of latent inhibition (the preexposure effect) in terms of blocking. The discussion summarizes how the approach accounts for a variety of other “irrational” phenomena in associative learning, including base rate effects, perseveration of attention through relevance shifts, overshadowing, and the extrapolation of rules near exceptions.