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

Although perseveration---the inappropriate repetition of previous responses---is quite common among patients with neurological damage, relatively few detailed computational accounts of its various forms have been put forth. A particularly well-documented variety involves the pattern of errors made by "optic aphasic" patients, who have a selective deficit in naming visually-presented objects. Based on our previous work in modeling impaired reading for meaning in deep dyslexia, we develop a connectionist simulation of visual object naming. The major extension in the present work is the incorporation of short-term correlational weights that bias the network towards reproducing patterns of activity that have occurred on recently preceding trials. Under damage, the network replicates the complex semantic and perseverative effects found in the optic aphasic error pattern. Further analysis reveals that the perseverative effects are strongest when the lesions are near or within semanti...

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Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connectionsandthustheinfluenceofsurprisingstimulionpremotoractivity.Thisfindingsubstantiallyadvancesourunderstandingofstriatalfunction and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla--Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity.

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

The concordance between performance and judgements of the causal effectiveness of an instrumental action suggests that such actions are mediated by causal knowledge. Although causal learning exhibits many associative phenomena—blocking, inhibitory or preventative learning, and super-learning—judgements of the causal status of a cue can be changed retrospectively as a result of learning episodes that do not directly involve the cue. In order to explain retrospective revaluation, a modi®ed associative theory is described in which the learning processes for retrieved cue representations are the opposite to those for presented cues, and this theory is evaluated by studies of the role of within-compound associations in retrospective revaluation and blocking. However, this modi®ed theory only applies when the within-compound association represents a contiguous rather than a causal cue relationship. Causal learning and representation is a fundamental form of cognition, if not the fundamental form. Without the capacity to learn about and represent the causal relationships between our actions and their consequences, the mind would be radically disconnected from the world. However detailed and rich our knowledge, however sophisticated and complex our inferences and planning, cognition would be impotent if our thoughts could not be

This study investigated the link between expertise for a prototypedefined category and the face inversion effect (which refers to the decline in performance in recognising faces that are inverted compared to the recognition of faces in their normal upright orientation; e.g., Yin, 1969). We aimed to demonstrate an analogous effect in chequerboards drawn from a familiar category such that participants had acquired a certain expertise with that category. Participants in this study were first presented with a categorisation task in which they were asked to sort a number of chequerboards (in an upright orientation) into two categories. This increased their familiarity with these categories. Then, in the next (study) phase, participants were presented with a set of chequerboards which included exemplars (some upright, some inverted) from one of the two categories that participants were familiar with, plus exemplars (also upright or inverted) from a

The face inversion effect is a reduction in recognition performance for inverted faces compared to upright faces that is greater than that typically observed with other stimulus types (e.g. houses; Yin, 1969). This study investigated the link between second-order relational structure and the face inversion effect suggested by Diamond and Carey (1986). The idea is that expertise gained as a consequence of a great deal of experience with exemplars derived from a familiar category, that possess what Diamond and Carey term second order relational structure, can produce an improved ability to distinguish between and recognise members of this category. Because facial features share the same basic spatial configuration, i.e. eyes are always above the nose and so on, and individual faces vary these spatial arrangements slightly, they have second order relational structure. The argument is that our experience with this structure underpins our ability to recognize faces, and this expertise with faces is lost on inversion because inversion disrupts the ability to exploit second order relational information. In this paper we report two experiments that confirm that we can obtain a strong face inversion effect, and that the magnitude of this effect can be reduced by disrupting the second order relational structure of the faces.