Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry

Koch, Christof; Ullman, Shimon

doi:10.1007/978-94-009-3833-5_5

Cited by 2,345 publications

(2,395 citation statements)

References 41 publications

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“…Such propensity of some visual features to attract attention defines in part the phenomenon of visual saliency. Here we assert, as others (James 1890;Treisman and Gelade 1980;Koch and Ullman 1985;Itti and Koch 2001) that saliency is drawn from a variety of factors. At the lowest levels, color opponencies, unique orientations and luminance contrasts create the effect of visual pop-out (Treisman and Gelade 1980;Wolfe et al 1998).…”

Section: Introductionsupporting

confidence: 60%

Computational modeling and exploration of contour integration for visual saliency

Mundhenk

Itti

2005

Biol Cybern

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We propose a computational model of contour integration for visual saliency. The model uses biologically plausible devices to simulate how the representations of elements aligned collinearly along a contour in an image are enhanced. Our model adds such devices as a dopamine-like fast plasticity, local GABAergic inhibition and multi-scale processing of images. The fast plasticity addresses the problem of how neurons in visual cortex seem to be able to influence neurons they are not directly connected to, for instance, as observed in contour closure effect. Local GABAergic inhibition is used to control gain in the system without using global mechanisms which may be non-plausible given the limited reach of axonal arbors in visual cortex. The model is then used to explore not only its validity in real and artificial images, but to discover some of the mechanisms involved in processing of complex visual features such as junctions and end-stops as well as contours. We present evidence for the validity of our model in several phases, starting with local enhancement of only a few collinear elements. We then test our model on more complex contour integration images with a large number of Gabor elements. Sections of the model are also extracted and used to discover how the model might relate contour integration neurons to neurons that process end-stops and junctions. Finally, we present results from real world images. Results from the model suggest that it is a good current approximation of contour integration in human vision. As well, it suggests that contour integration mechanisms may be strongly related to mechanisms for detecting end-stops and junction points. Additionally, a contour integration mechanism may be involved in finding features for objects such as faces. This suggests that visual cortex may be more information efficient and that neural regions may have multiple roles.

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Section: Introductionsupporting

confidence: 60%

Computational modeling and exploration of contour integration for visual saliency

Mundhenk

Itti

2005

Biol Cybern

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“…According to this perspective, visual selection may be either stimulus or goal driven as a product of response time. As in most current models of visual selection (Cave & Wolfe, 1990;Folk et al, 1992;Itti & Koch, 2000;Koch & Ullman, 1985;Theeuwes, 1991Theeuwes, , 1992Theeuwes, , 1994Wolfe, 1994;Wolfe, Cave, & Franzel, 1989), it is assumed that visual selection is the result of the output provided by some common salience map that combines input from different modules. The pattern of activity in this map may give rise to a single location that is most likely to be selected (Itti & Koch, 2000;Koch & Ullman, 1985).…”

Section: Discussionmentioning

confidence: 99%

The Role of Stimulus-Driven and Goal-Driven Control in Saccadic Visual Selection.

Zoest¹,

Donk²,

Theeuwes³

2004

Journal of Experimental Psychology: Human Perception and Perfor

265

244

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Four experiments were conducted to investigate the role of stimulus-driven and goal-driven control in saccadic eye movements. Participants were required to make a speeded saccade toward a predefined target presented concurrently with multiple nontargets and possibly 1 distractor. Target and distractor were either equally salient (Experiments 1 and 2) or not (Experiments 3 and 4). The results uniformly demonstrated that fast eye movements were completely stimulus driven, whereas slower eye movements were goal driven. These results are in line with neither a bottom-up account nor a top-down notion of visual selection. Instead, they indicate that visual selection is the outcome of 2 independent processes, one stimulus driven and the other goal driven, operating in different time windows.

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“…And fourth, locations of groups relative to each other may be represented conceptually by predicates like above (x, y) that express categorical spatial relations (Logan & Sadler, 1996). Location can be selected in the third and fourth senses by applying visual routines that are outside the current model (Cave & Wolfe, 1990;Koch & Ullman, 1985;Logan, 1995).…”

Section: The Wedding Of Code and Tvamentioning

confidence: 99%

The CODE theory of visual attention: An integration of space-based and object-based attention.

Logan

1996

Psychological Review

281

344

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This article presents a theory that inte~ates space-based and object-based approaches to visual attention. The theory puts together M. P. van Oeffelen and P. G. Vos's ( 1982Vos's ( , 1983) COntour DEtector (CODE) theory of perceptual grouping by proximity with C. Bundesen's (1990) theory of visual attention (TVA). CODE provides input to TVA, accounting for spatially based between-object selection, and TVA converts the input to output, accounting for feature-and category-based withinobject selection. CODE clusters nearby items into perceptual groups that are both perceptual objects and regions of space, thereby integrating object-based and space-based approaches to attention. The combined theory provides a quantitative account of the effects of grouping by proximity and dis~nce between items on reaction time and accuracy data in 7 empirical situations that shaped the current literature on visual spatial attention.For the last decade the attention literature has been embroiled in a debate over the nature of visual spatial attention that focuses on the "thing" that attention selects (e.g., Baylis & Driver, 1993;Driver & Baylis, 1989;Duncan, 1984;Egly, Driver, & Rafal, 1994;Kramer & Jacobson, 1991;Vecera & Farah, 1994). Advocates of space-based attention argue that attention selects regions of space independent of the objects they contain. Attention is like a spotlight illuminating a region of space. Objects that fall within the beam are processed; objects that fall outside it are not (Eriksen & Eriksen, 1974;Eriksen & St. James, 1986;Posner, 1980;Posner & Cohen, 1984;Treisman & Gelade, 1980;Treisman & Gormican, 1988). Advocates of object-based attention argue that attention selects objects rather than regions of space. Selection is spatial because objects necessarily occupy regions of space, but objects rather than the regions themselves are the things that are selected (Kahneman & Henik, 1981;Kahneman & Treisman, 1984;Kahneman, Treisman, & Gibbs, 1992;Pylyshyn & Storm, 1988). Objectbased theories assume that attention only selects regions of space that are occupied by objects, whereas space-based theories assume that attention can select empty regions of space (cf. Yantis, 1992).The purpose of this article is to propose a theory of visual spatial attention that integrates space-based and object-based views. The theory takes a computational approach to the problem, characterizing attention in terms of representations and the processes that operate on them. It differs from most ap- proaches to attention by being concerned with the representation of space and the representation of objects, incorporating a theory of perceptual organization and a theory of selection. The resolution of the controversy derives from the theory's assumptions about representation.The article begins by describing five important questions that face any theory of visual spatial attention. The answers proposed by the new theory are presented by way of describing the theory. The theory is applied to seven important paradigms that shaped the cur...

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Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry

Cited by 2,345 publications

References 41 publications

Computational modeling and exploration of contour integration for visual saliency

Computational modeling and exploration of contour integration for visual saliency

The Role of Stimulus-Driven and Goal-Driven Control in Saccadic Visual Selection.

The CODE theory of visual attention: An integration of space-based and object-based attention.

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