On the control of attentional processes in vision

被引:9
|
作者
Tsotsos, John K. [1 ]
Abid, Omar [1 ]
Kotseruba, Iuliia [1 ]
Solbach, Markus D. [1 ]
机构
[1] York Univ, Toronto, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Vision; Attention; Control; Cognitive program; Selective tuning; NEURAL MECHANISMS; CORTICAL CIRCUITS; EMERGENT FEATURES; EXECUTIVE CONTROL; VISUAL-ATTENTION; COMPLEXITY; BRAIN; CORTEX; INTELLIGENCE; COMPUTATION;
D O I
10.1016/j.cortex.2021.01.001
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
The study of attentional processing in vision has a long and deep history. Recently, several papers have presented insightful perspectives into how the coordination of multiple attentional functions in the brain might occur. These begin with experimental observations and the authors propose structures, processes, and computations that might explain those observations. Here, we consider a perspective that past works have not, as a complementary approach to the experimentally-grounded ones. We approach the same problem as past authors but from the other end of the computational spectrum, from the problem nature, as Marr's Computational Level would prescribe. What problem must the brain solve when orchestrating attentional processes in order to successfully complete one of the myriad possible visuospatial tasks at which we as humans excel? The hope, of course, is for the approaches to eventually meet and thus form a complete theory, but this is likely not soon. We make the first steps towards this by addressing the necessity of attentional control, examining the breadth and computational difficulty of the visuospatial and attentional tasks seen in human behavior, and suggesting a sketch of how attentional control might arise in the brain. The key conclusions of this paper are that an executive controller is necessary for human attentional function in vision, and that there is a 'first principles' computational approach to its understanding that is complementary to the previous approaches that focus on modelling or learning from experimental observations directly. (c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页码:305 / 329
页数:25
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