The Neuropsychology of Attention pp 891-930 | Cite as
Computational Approaches to Attention
Abstract
As the cognitive sciences have evolved, so has the proliferation of theoretical models to account for attention and other cognitive functions. Models are developed to operationalize the processes and mechanisms underlying particular functions and to formalize their properties so as to enable experimental validation. Models developed during the middle of the twentieth century often posited complex relationships among multiple modular processes in an effort to account for cognitive functions like attention but tended to be largely descriptive in nature. They lacked specificity with respect to how elements of the model work under different experimental conditions and tended not to be formalized mathematically. They provided starting points for conceptualizing and study cognitive functions, but generally were underspecified, making it difficult to test the validity of one model versus another. In an effort to overcome this problem, cognitive and behavioral scientists increasingly employed computational approaches. Although initially many of these models provided little more than a mathematical description of processes that could be characterized less formally at a purely conceptual level, they did provide a valuable way of identifying specific parameters and constraints that affect the and parameters underlying attentional operations.
Keywords
Connectionist Model Statistical Learning Theory Manual Tracking Competitive Learning Adaptive Resonance TheoryReferences
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