Journal Article10.1038/81504
Learning and selective attention.
TL;DR: This work considers statistical and informational aspects of selective attention, divorced from resource constraints, which are evident in animal conditioning experiments involving uncertain predictions and unreliable stimuli.
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Abstract: Selective attention involves the differential processing of different stimuli, and has widespread psychological and neural consequences. Although computational modeling should offer a powerful way of linking observable phenomena at different levels, most work has focused on the relatively narrow issue of constraints on processing resources. By contrast, we consider statistical and informational aspects of selective attention, divorced from resource constraints, which are evident in animal conditioning experiments involving uncertain predictions and unreliable stimuli. Neuromodulatory systems and limbic structures are known to underlie attentional effects in such tasks.
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Citations
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Donald Eric Broadbent
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TL;DR: In this paper, the authors describe a transition between behaviourist learning theory and the modern information processing or cognitive approach to perception and communication skills, and provide a principal starting point for theoretical and experimental work on selective attention.
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Learning to Predict by the Methods of Temporal Differences
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TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
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