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

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.

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If first language is rational in the sense that acquisition produces an end-state model of language that is a proper reflection of input and that optimally prepares speakers for comprehension and production, second language is usually not. This paper considers the apparent irrationalities of L2 acquisition, that is the shortcomings where input fails to become intake. It describes how ‘learned attention’, a key concept in contemporary associative and connectionist theories of animal and human learning, explains these effects. The fragile features of L2 acquisition are those which, however available as a result of frequency, recency, or context, fall short of intake because of one of the factors of contingency, cue competition, salience, interference, overshadowing, blocking, or perceptual learning, which are all shaped by the L1. Each phenomenon is explained within associative learning theory and exemplified in language learning. Paradoxically, the successes of L1 acquisition and the limitations of L2 acquisition both derive from the same basic learning principles.

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.

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.

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Research has shown that category learning is affected by (a) attention, which selects which aspects of stimuli are available for further processing, and (b) the existing semantic knowledge that learners bring to the task. However, little is known about how knowledge affects what is attended. Using eyetracking, we found that (a) knowledge indeed changes what features are attended, with knowledgerelevant features being fixated more often than irrelevant ones, (b) this effect was not due to an initial attentional bias toward relevant dimensions but rather emerged as a result of observing category members, and (c) this effect grew even after a learning criterion was reached, that is, despite the absence of error feedback. We argue that models of knowledge-based learning will remain incomplete until they include mechanisms that dynamically select prior knowledge in response to observed category members and which then directs attention to knowledge-relevant dimensions and away from irrelevant ones. Knowledge and Attention in Category Learning 3 How Prior Knowledge Affects Selective Attention During Category Learning:

Category learning often involves selective attention to category relevant information, which may result in learned inattention to category irrelevant information. This learned inattention is a cost of selective attention. In the current research, the cost of attention was used as an indicator of category learning. Participants were given a category learning task, and the amount of supervision given to them was manipulated. Along with behavioral data, recorded eye movements during the task showed signature patterns of learning via a cost of attention. In addition, a simple neural network (perceptron) was able to use these eye-tracking data to predict success in learning. Thus, the observed attentional pattern – the cost of selective attention – was proposed as an

Blocking is a well-studied learning phenomenon in which previous learning inhibits subsequent learning about novel cues. Existing models provide different explanations for blocking and predict different beliefs about novel cues early in the second phase of blocking. Two experiments examined learners ’ beliefs when first encountering novel cues. The results suggest that the introduction of the novel cue in the second phase of a blocking paradigm adds uncertainty and that learners entertain the possibility that novel cues are preventative. A novel computational account is proposed to explain people’s beliefs, because existing models cannot fully account for these findings.