Computational modelling of visual attention

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Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.

Key Points

  • We review recent work on computational models of focal visual attention, with emphasis on the bottom-up, saliency- or image-based control of attentional deployment. We highlight five important trends that have emerged from the computational literature:

  • First, the perceptual saliency of stimuli critically depends on surrounding context; that is, the same object may or may not appear salient depending on the nature and arrangement of other objects in the scene. Computationally, this means that contextual influences, such as non-classical surround interactions, must be included in models.

  • Second, a unique 'saliency map' topographically encoding for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Many successful models are based on such architecture, and electrophysiological as well as psychophysical studies have recently supported the idea that saliency is explicitly encoded in the brain.

  • Third, inhibition-of-return (IOR), the process by which the currently attended location is transiently inhibited, is a critical element of attentional deployment. Without IOR, attention would endlessly be attracted towards the most salient stimulus. IOR thus implements a memory of recently visited locations, and allows attention to thoroughly scan our visual environment.

  • Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. Understanding the interaction between overt and covert attention is particularly important for models concerned with visual search.

  • Last, scene understanding and object recognition strongly constrain the selection of attended locations. Although several models have approached, in an information-theoretical sense, the problem of optimally deploying attention to analyse a scene, biologically plausible implementations of such a computational strategy remain to be developed.

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Figure 1: Flow diagram of a typical model for the control of bottom-up attention.
Figure 2: Recording saliency.
Figure 3: Combined model of attentional selection and object recognition.


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The research carried out in the laboratories of the authors on visual attention is supported by the National Science Foundation, the National Institute of Mental Health and the Office of Naval Research. We thank Alex Pouget for excellent comments and suggestions.

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Supplementary material for Figure 2



These involve neurons that respond to image differences between a small central region and a broader concentric antagonistic surround region.


Visual brain areas involved in the localization of objects and mostly found in the posterior/ superior part of the brain.


Visual brain areas involved in the identification of objects and mostly found in the posterior/ inferior part of the brain.


Expression of attention involving eye movements.


Expression of attention without eye movements, typically thought of as a virtual 'spotlight'.


Spatial difference (for example, detected by centre–surround mechanisms) in light intensity (luminance) in a visual scene.


Spatial difference in colours, computed in the brain using red/green and blue/yellow centre–surround mechanisms.


Property of visual neurons to only respond to certain classes of stimuli (for example, vertically orientated bars).


Smallest difference between two visual stimuli (for example, vertical versus tilted bar) than can reliably (that is, with a given probability of error) be reported by an observer.


A patch of cortex including neurons responding to all orientations and many spatial scales, all for a single location in the visual field.


A filter obtained by taking the difference between a narrow Gaussian distribution (the excitatory centre) and a broader Gaussian distribution with the same mean (the inhibitory surround).


Product of a sinusoidal grating and a two-dimensional Gaussian function.


The response of a filter to a single pulse (Dirac) stimulus.


Very rapid, ballistic eye movement (with speeds up to 800 degrees per second).

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