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

Some recent formal models of Pavlovian and instrumental conditioning contain internal paradoxes that restrict their predictive power. These paradoxes can be traced to an inadequate formulation of how mechanisms of short-term memory and long-term memory work together to control the shifting balance between the processing of expected and unexpected events. Once this formulation is strengthened, a unified processing framework is suggested wherein attentional and orienting subsystems coexist in a complementary relationship that controls the adaptive self-organization of internal representations in response to expected and unexpected events. In this framework, conditioning and attentional constructs can be more directly validated by interdisciplinary paradigms in which seemingly disparate phenomena can be shown to share similar physiological and pharmacological mechanisms. A model of cholinergic-catecholaminergic interactions suggests how drive, reinforcer, and arousal inputs regulate motivational baseline, hysteresis, and rebound, with the hippocampus as a final common path. Extinction, conditioned emotional responses, conditioned avoidance responses, secondary

Recent research on contingency judgment indicates that the judged predictiveness of a cue is dependent on the predictive strengths of other cues. Two classes of models correctly predict such cue interaction: associative models and statistical models. However, these models differ in their predictions about the effect of trial order on cue interaction. In five experiments reported here, college students viewed trial-by-trial data regarding several medical symptoms and a disease, judging the predictive strength of each symptom with respect to the disease. The results indicate that trial order influences the manner in which cues interact, but that neither the associative nor the statistical models can fully account for the data pattern. A possible variation of an associative account is discussed. The ability to detect predictive relationships among envi-ronmental events grants humans and other animals a distinct benefit. Therefore, the mechanisms underlying this ability are of considerable interest. Recent research with humans on judgments of contingencies has shed light on these mecha-nisms. It has suggested two classes of theoretical models that

C. R. Gallistel and J. Gibbon (2000) presented quantitative data on the speed with which animals acquire behavioral responses during autoshaping, together with a statistical model of learning intended to account for them. Although this model captures the form of the dependencies among critical variables, its detailed predictions are substantially at variance with the data. In the present article, further key data on the speed of acquisition are used to motivate an alternative model of learning, in which animals can be interpreted as paying different amounts of attention to stimuli according to estimates of their differential reliabilities as predictors. In autoshaping experiments on pigeons, birds acquire a classically conditioned peck response to a lighted key associated, irrespective of their actions, with the delivery of food (Brown & Jenkins, 1968). As stressed persuasively by Gallistel and Gibbon (2000), there is substantial experimental evidence in favor of a simple quantitative relationship between the speed of acquisition in autoshaping and the three critical variables shown in Figure 1A. The first is I, the length of intertrial interval; the second is T, the time during the trial for which the conditioned stimulus (CS; a light in this case) is presented; and the third is the training schedule, 1/S, which is the fractional number of deliveries per light (for those birds that were only partially reinforced). Here, acquisition speeds are typically measured in terms of the number of trials it takes until a certain behavioral criterion is met, such as pecking during the time the light is illuminated on three out of four

A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.

Human perceptual and cognitive abilities are limited resources. Attention is the mechanism used to allocate such resources in the most effective way. Current technologies, in addition to allowing fast access to information and people, should be designed to support human attentional processes on which they impose further strain. This paper analyses the issues related to the design of systems capable of such support: Attention Aware Systems. We introduce the research aimed at understanding and modelling human attentional processes, including perceptual and cognitive processes as studied in cognitive psychology, as well as rhetorical, aesthetic, and social aspects related to attentional mechanisms. We analyse current approaches to the design of Attention Aware Systems along three major features: detection of user's current attentional state, detection and evaluation of possible alternative attentional states, strategies for focus switch or maintenance. Finally, we discuss the most promising research direction for the development of systems capable of supporting human attentional mechanisms.

Present neural models of classical conditioning all suffer from the same shortcoming: local representation of information (therefore, very precise neural prewiring is necessary). As an alternative we develop two neural models of classical conditioning which rely on distributed representations of information. Both models are of the Hopfield type. In the first model the existence of transmission delays is used to store temporal relations. The second model is based on interactions between spatially separated neural fields. Using tools from statistical mechanics we show that behavioural constraints can be met only if the Hebb rule is extended with inter- or intrasynaptic competition. 2 3 1. Introduction Connectionism has redirected the attention of cognitive scientists to learning and to the neural substrate in which cognitive processes are implemented. Conditioning has become an important field in which ideas from neural networks, behavioural science and neurophysiology are combined. ...

Is attention a purely perceptual process or is it in any way related to motor control? The aim of this article is to show that attention puts similar demands on a cognitive system as motor control and present evidence supporting the view that similar mechanisms operate in the two processes. A computational model of attention is presented that uses habituation as well as classical and instrumental conditioning to explain a number of attentional processes. Evidence from neurophysiology is reviewed that suggest that attention is controlled in a way similar to actions. This view makes it possible to adapt traditional learning theoretical mechanisms to the control of attention. Computer simulations are presented that illustrates the operation of the model.

Abstract: Current thinking about Pavlovian conditioning differs substantially from that of 20 years ago. Yet the changes that have taken place remain poorly appreciated by psychologists generally. Traditional descriptions of conditioning as the acquired ability of one stimulus to evoke the original response to another because of their pairing are shown to be inadequate. They fail to characterize adequately the circumstances producing learning, the content of that learning, or the manner in which that learning influences performance. Instead, conditioning is now described as the learning of relations among events so as to allow the organism to represent its environment. Within this framework, the study of Pavlovian conditioning continues to be an intellectually active area, full of new discoveries and information relevant to other areas of psychology. Pavlovian conditioning is one of the oldest and most systematically studied phenomena in psychology. Outside of psychology, it is one of our best-known findings. But at the same time, within psychology it is badly misunderstood and misrepresented. In the last 20 years, knowledge of the associative processes underlying Pavlovian conditioning has expanded dramatically. The

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