We present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function, that the hippocampus is responsible for developing conjunctive representations binding together stimulus elements into a unitary rep- resentation that can later be recalled from partial input cues. This idea appears problematic, however, because it is contradicted by the fact that hippocampally lesioned rats can learn nonlinear discrimination problems that require conjunctive representations. Our framework accommodates this finding by establishing a principled division of labor between the cortex and hippocampus, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities, while the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework shows that nonlinear discrimination problems are not good tests of hippocampal function, and suggests that tasks involving rapid, incidental conjunctive learning are better. We implement this framework in a computational neural network model, and show that it can account for a wide range of data in animal learning, thus validating our theoretical ideas, and providing a number of insights and predictions about these learning phenomena.

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 6 N. J. Blair, 2000, Psychonomic Bulletin 6 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

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of withincategory information. Accordingly, we predicted that classification learning would produce a deficit in people’s ability to draw novel contrasts—distinctions that were not part of training—compared with feature inference learning. Two experiments confirmed that classification learners were at a disadvantage at making novel distinctions. Eye movement data indicated that this conceptual inflexibility was due to (a) a narrower attention profile that reduces the encoding of many category features and (b) learned inattention that inhibits the reallocation of attention to newly relevant information. Implications of these costs of supervised classification learning for views of conceptual structure are discussed.

this article is to give a comprehensive review of those studies that have used the paradigm to explicitly test the motivational eects of drugs or non-drug treatments or the eects of lesions or genetical manipulations on brain reward mechanisms. In those instances where inconsistent results have been reported, attempts are made to resolve these discrepancies and to provide explanations for the contradicting results. A concluding chapter will provide a synthesis of those ndings that have been reported consistently across dierent studies and tries to highlight the neurochemical and anatomical foundations of brain reward mechanisms as far as this knowledge has been derived from place preference conditioning studies reviewed in this paper

Abstract. Due to the various and dynamic nature of stimuli, decisions of intelligent agents must rely on the coordination of complex cognitive systems. This paper precisely focusses on a general learning architecture for autonomous agents. It is based on a neural network model that enables the specific behaviours of classical conditioning and a biologically inspired attentional phenomenon called latent inhibition. We propose a neural network implementation of an extended model of classical conditioning and present some results. 1

this article should be addressed to Jos Prados, Departament de Psicologia Bsica, Passeig de la Vall dHebron, 171, 08035-Barcelona, Spain. The research reported here was supported by a grant from the Spanish Ministerio de Educacin y Cultura (Proyecto n... PB97-0965). The author is indebted to V.D. Chamizo for help and advice. Also I thank Nuria Vicente for her help with language, and the people from the Behavioral Neuroscience Laboratory of the University of York, who stoically suffered my "Ginger" episodes when writing the present paper

Repeated acquisitions and extinctions in classical conditioning of

this paper was to test whether renewal can be found in human beings in a situation where the context change does not affect acquisition. We used a predictive judgments preparation where fictitious medicines are presented, and the subject has to predict whether they are related to an imaginary illness. Experiment 1 was conducted with the aim of testing our acquisition and extinction procedure. Experiment 2 looked for within subjects renewal, testing whether the return to the acquisition context after receiving extinction in a different but equally familiar context would renew the predicted probability of the medicine causing the illness