The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were no...

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

Major biases and stereotypes in group judgments are reviewed and modeled from a recurrent connectionist perspective. These biases are in the areas of group impression formation (illusory correlation), group differentiation (accentuation), stereotype change (dispersed vs. concentrated distribution of inconsistent information), and group homogeneity. All these phenomena are illustrated with well-known experiments, and simulated with an autoassociative network architecture with linear activation update and delta learning algorithm for adjusting the connection weights. All the biases were successfully reproduced in the simulations. The discussion centers on how the particular simulation specifications compare with other models of group biases and how they may be used to develop novel hypotheses for testing the connectionist modeling approach and, more generally, for improving theorizing in the field of social biases and stereotype change. Petite, attractive, intelligent, WSF, 30, fond of music, theatre, books, travel, seeks warm, affectionate, fun-loving man to share life’s pleasures with view to lasting relationship. Send photograph. Please no

The phasic firing of dopamine neurons has been theorized to encode a reward-prediction error as formalized by the temporal-difference (TD) algorithm in reinforcement learning. Most TD models of dopamine have assumed a stimulus representation, known as the complete serial compound, in which each moment in a trial is distinctly represented. We introduce a more realistic temporal stimulus representation for the TD model. In our model, all external stimuli, including rewards, spawn a series of internal microstimuli, which grow weaker and more diffuse over time. These microstimuli are used by the TD learning algorithm to generate predictions of future reward. This new stimulus representation injects temporal generalization into the TD model and enhances correspondence between model and data in several experiments, including those when rewards are omitted or received early. This improved fit mostly derives from the absence of large negative errors in the new model, suggesting that dopamine alone can encode the full range of TD errors in these situations.

The siphon withdrawal reflex of Aplysia undergoes differential classical conditioning with cutaneous stimulation of the siphon or mantle shelf as the discriminative conditioned stimuli (CS+ and CS-) and shock to the tail as the unconditioned stimulus (US). The reflex has proved to be useful for analyzing the neural mechanisms of conditioning. To test the generality of this ex-perimental system, we have begun to compare the properties of conditioning in Aplysia with those of conditioning in verte-brates. We first examined the effect of the interstimulus interval (ISI) by varying the time between presentation of the CS+ and the US in different groups of animals. Significant differential conditioning was obtained when the onset of the CS+ preceded the onset of the US by 0.5 set, and marginal conditioning was obtained when the IS1 was 1.0 sec. By contrast, no significant conditioning occurred when the CS+ preceded the US by 2, 5,

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Through a series of experiments we illustrate how the sequential order in which consumers receive information can influence the way this information is processed and affect consumers ’ decisions. Specifically, when participants initially receive in-formation regarding brand/quality or price/quality associations, these associations can block consumers ’ attention to more relevant quality-determining physical at-tributes. Moreover, this process of attention blocking can carry-over to affect quality judgements pertaining to similarly branded or priced products beyond the product in which blocking was initiated. This implies that consumers judgements of quality may be heavily dependent on “first impressions ” which develop into brand and price heuristics.

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A study of the combined influence of prior knowledge and stimulus dimensionality on category learning was conducted. Subjects learned category structures with the same number of necessary dimensions but with more or fewer additional, redundant dimensions and with either knowledge-related or knowledge-unrelated features. Minimal-learning models predict that all subjects, regardless of condition, either should learn the same number of dimensions or should respond more slowly to each dimension. Despite similar learning rates and response times, subjects learned more features in the high-dimensional than in the low-dimensional condition. Furthermore, prior knowledge interacted with dimensionality, increasing what was learned, especially in the highdimensional case. A second experiment confirmed that the participants did, in fact, learn more features during the training phase, rather than simply inferring them at test. These effects can be explained by direct associations

This paper describes a computational framework for theoretical models of classical conditioning. It is based on the engineering technique of Kalman filtering, which describes how systems can infer regularities in noisy and changing environments based on the results of partial experiments. The resulting class of conditioning theories has similarities with three existing (and apparently incompatible) ones; those due to Rescorla and Wagner (1972), Mackintosh (1975) and Pearce and Hall (1980). As in the Rescorla-Wagner rule, learning is dependent on errors in prediction of the unconditioned stimulus, but, as in the other rules, the amount of learning that accrues to each conditioned stimulus is a function of its own conditioning history.