Cognitive Science 39 (6):1172-1215 (2015)

Abstract
Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments
Keywords Evolutionary psychology  Neural networks  Psychology  Learning  Computer simulation  Attention
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DOI 10.1111/cogs.12196
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References found in this work BETA

Contextual Cueing of Visual Attention.Marvin M. Chun - 2000 - Trends in Cognitive Sciences 4 (5):170-178.
Alcove-an Exemplar-Based Connectionist Model of Category Learning.Jk Kruschke & Rm Nosofsky - 1991 - Bulletin of the Psychonomic Society 29 (6):475-475.

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