The Selective Tuning Model for Visual Attention

Complexity analysis leads to the conclusion that if attention tunes the visual processing architecture task-directed processing is enabled and a solution to signal interference otherwise present in the converging feedforward pathways is provided. Selective tuning takes two forms: spatial selection is realized by inhibition of irrelevant connections; and feature selection is realized by inhibition of the units, which compute irrelevant features. Only a very brief summary is presented here (a more detailed account is in Tsotsos et al.1).

[1]  John K. Tsotsos An inhibitory beam for attentional selection , 1994 .

[2]  Ken Nakayama,et al.  Attentional requirements in a ‘preattentive’ feature search task , 1997, Nature.

[3]  John K. Tsotsos Analyzing vision at the complexity level , 1990, Behavioral and Brain Sciences.

[4]  B. Motter Neural correlates of attentive selection for color or luminance in extrastriate area V4 , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  B. C. Motter Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. , 1993, Journal of neurophysiology.

[6]  John Duncan,et al.  A neural basis for visual search in inferior temporal cortex , 1993, Nature.

[7]  Jeffrey D. Schall,et al.  Neural basis of saccade target selection in frontal eye field during visual search , 1993, Nature.

[8]  B. C. Motter,et al.  Neural correlates of feature selective memory and pop-out in extrastriate area V4 , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[9]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

[10]  John K. Tsotsos Triangles, Pyramids, Connections and Attentive Inhibition , 1999 .

[11]  D C Van Essen,et al.  Information processing in the primate visual system: an integrated systems perspective. , 1992, Science.

[12]  D C Rees,et al.  A structural basis for recognition of A.T and T.A base pairs in the minor groove of B-DNA. , 1998, Science.

[13]  R. Desimone Complexity at the neuronal level , 1990, Behavioral and Brain Sciences.

[14]  G A Orban,et al.  Attention-dependent suppression of metabolic activity in the early stages of the macaque visual system. , 2000, Cerebral cortex.

[15]  Leslie G. Ungerleider,et al.  Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. , 1998, Science.

[16]  E. DeYoe,et al.  A physiological correlate of the 'spotlight' of visual attention , 1999, Nature Neuroscience.

[17]  D. Heeger,et al.  Spatial attention affects brain activity in human primary visual cortex. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[18]  G. Caputo,et al.  Attentional selection by distractor suppression , 1998, Vision Research.

[19]  John K. Tsotsos,et al.  The selective tuning model of attention: psychophysical evidence for a suppressive annulus around an attended item , 2003, Vision Research.

[20]  Eileen Kowler,et al.  Attentional interference at small spatial separations , 1999, Vision Research.

[21]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

[22]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[23]  R. Desimone,et al.  Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.

[24]  John K. Tsotsos,et al.  An Attentional Prototype for Early Vision , 1992, ECCV.

[25]  Seong-Whan Lee,et al.  Biologically Motivated Computer Vision , 2002, Lecture Notes in Computer Science.