REVIEWSCOMPUTATIONAL MODELLING OF
VISUAL ATTENTION
Laurent Itti* and Christof Koch‡
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 andneurobiological understanding of visual attention.
CENTRE–SURROUNDMECHANISMS

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

*Hedco Neuroscience
Building, University ofSouthern California, 3641
Watt Way, Los Angeles,
California 90089-2520, USA.
‡
Computation and Neural
Systems Program, Caltech,
Pasadena, California 91125,
USA. Correspondence to L.I.
e-mail: itti@usc.edu

The most important function of selective visual attention is to direct our gaze rapidly towards objects ofinterest in our visual environment1–9. This ability toorientate rapidly towards salient objects in a clutteredvisual scene has evolutionary significance because it
allows the organism to detect quickly possible prey,mates or predators in the visual world. A two-component framework for attentional deployment has
recently emerged, although the idea dates back to
William James1, the father of American psychology.
This framework suggests that subjects selectively
direct attention to objects in a scene using both bottom-up, image-based saliency cues and top-down,task-dependent cues.
Some stimuli are intrinsically conspicuous or
salient in a given context. For example, a red dinnerjacket among black tuxedos at a sombre state affair, or a
flickering light in an otherwise static scene, automatically and involuntarily attract attention. Saliency is
independent of the nature of the particular task, operates very rapidly and is primarily driven in a bottom-up
manner, although it can be influenced by contextual,
figure–ground effects. If a stimulus is sufficiently

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salient, it will pop out of a visual scene. This suggeststhat saliency is computed in a pre-attentive manner
across the entire visual field, most probably in terms ofhierarchical CENTRE–SURROUND MECHANISMS. The speed ofthis saliency-based form of attention is on the order of
25 to 50 ms per item.
The second form of attention is a more deliberate
and powerful one that has variable selection criteria,
depending on the task at hand (for example, ‘look forthe red, horizontal target’). The expression of this topdown attention is most probably controlled from higher areas, including the frontal lobes, which connect backinto visual cortex and early visual areas. Such volitional
deployment of attention has a price, because the amountof time that it takes — 200 ms or more — rivals thatneeded to move the eyes. So, whereas certain features inthe visual world automatically attract attention and areexperienced as ‘visually salient’, directing attention toother locations or objects requires voluntary ‘effort’.
Both mechanisms can operate in parallel.
Attention implements an information-processing
bottleneck that allows only a small part of the incoming
sensory information to reach short-term memory and

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Box 1 | Neuronal mechanisms for the control of attention
Visual information
The brain regions that participate in the deployment of visual
attention include most of the early visual processing area. A
simplified overview of the main brain areas involved is shownin the figure. Visual information enters the primary visual
Visual cortex
cortex via the lateral geniculate nucleus (not shown), althoughsmaller pathways, for example, to the superior colliculus (SC),also exist. From there, visual information progresses along
Posterior
Inferotemporal
two parallel hierarchical streams. Cortical areas along thecortexparietal cortex‘dorsal stream’ (including the posterior parietal cortex; PPC)
are primarily concerned with spatial localization anddirecting attention and gaze towards objects of interest in thescene. The control of attentional deployment is consequently
Prefrontal
believed to mostly take place in the dorsal stream. Corticalcortexareas along the ‘ventral stream’ (including the inferotemporal
cortex; IT) are mainly concerned with the recognition andMotor andMemory andidentification of visual stimuli. Although probably notother systems
cognition
Superior
directly concerned with the control of attention, these ventralcolliculus
stream areas have indeed been shown to receive attentional
feedback modulation, and are involved in the representationof attended locations and objects (that is, in what passes
Eye movement
through the attentional bottleneck). In addition, several
higher-function areas are thought to contribute to attentional
guidance, in that lesions in those areas can cause a condition of ‘neglect’ in which patients seem unaware of parts of theirvisual environment (see REF. 111 for an overview of the regions involved).
From a computational viewpoint, the dorsal and ventral streams must interact, as scene understanding involves both
recognition and spatial deployment of attention. One region where such interaction has been extensively studied is theprefrontal cortex (PFC). Areas within the PFC are bidirectionally connected to both the PPC and the IT (see REF. 15). So, in
addition to being responsible for planning action (such as the execution of eye movements through the SC), the PFC also
has an important role in modulating, via feedback, the dorsal and ventral processing streams.

DORSAL STREAM

Visual brain areas involved in thelocalization of objects andmostly found in the posterior/
superior part of the brain.
VENTRAL STREAM

Visual brain areas involved in theidentification of objects andmostly found in the posterior/inferior part of the brain.
OVERT ATTENTION

Expression of attention
involving eye movements.
COVERT ATTENTION

Expression of attention without
eye movements, typicallythought of as a virtual ‘spotlight’.

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visual awareness10,11. So, instead of attempting to fully
process the massive sensory input (estimated to be on
the order of 107–108 bits per second at the optic nerve)in parallel, a serial strategy has evolved that achieves
near real-time performance despite limited computational capacity. Attention allows us to break down the
problem of understanding a visual scene into a rapid
series of computationally less demanding, localizedvisual analysis problems. In addition to these orientating
and scene analysis functions, attention is also characterized by a feedback modulation of neural activity for thevisual attributes and at the location of desired or selected targets. This feedback is believed to be essential forbinding the different visual attributes of an object, suchas colour and form, into a unitary percept2,12,13. By thisaccount, attention not only serves to select a location ofinterest but also enhances the cortical representation of
objects at that location. As such, focal visual attention
has been compared to a ‘stagelight’, successively illuminating different players as they take centre stage14.
Finally, attention is involved in triggering behaviour,and is consequently intimately related to recognition,planning and motor control15.
Developing computational models that describe
how attention is deployed within a given visual scene
has been an important challenge for computational
neuroscience. The potential application of these architectures in artificial vision for tasks such as surveillance,
automatic target detection, navigational aids and robotic
control provides additional motivation. Here, we focus

on biologically plausible computational modelling of asaliency-based form of focal bottom-up attention.
Much less is known about the neural instantiation of the
top-down, volitional component of attention16,17. As this
aspect of attention has not been modelled in such detail,it is not our primary focus here.
The control of focal visual attention involves an intricate network of brain areas (BOX 1). In a first approximation, selecting where to attend next is primarily controlled by the DORSAL STREAM of visual processing18,
although object recognition in the VENTRAL STREAM canbias the next attentional shift through top-down control.
The basis of most computational models are the experimental results obtained using the visual search paradigm of Treisman and colleagues, in particular the distinction between pop-out and conjunctive searches
developed in the early 1980s2.
The first explicit, neurally plausible computationalarchitecture for controlling visual attention was proposed by Koch and Ullman19 in 1985 (FIG. 1) (for an earlier related model of vision and eye movements, see
Didday and Arbib20). Koch and Ullman’s model wascentred around a ‘saliency map’, that is, an explicit twodimensional topographical map that encodes stimulus
conspicuity, or saliency, at every location in the visual
scene. The saliency map receives inputs from early visual
processing, and provides an efficient control strategy inwhich the focus of attention simply scans the saliency
map in order of decreasing saliency. Following thisbasic architecture, we now illustrate five essential com-

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Input image
Colours
Red, green, blue, yellow,
etc.

Multiscale
low-level feature
extraction

IntensityOn, off, etc.

Orientations
0°, 45°, 90°,135°, etc.
Other
Motion, junctions
and terminators,
stereo disparity,
shape from shading,
etc.

Attendedlocation

Inhibition of return

Winner-take-all
Centre–surrounddifferences andspatial competition

Saliency map

Feature maps

Featurecombinations

Top-down
attentional bias
and training

Figure 1 | Flow diagram of a typical model for the control of bottom-up attention. This diagram is based on Koch and Ullman’s19 hypothesis that a centralized
two-dimensional saliency map can provide an efficient control strategy for the deployment of attention on the basis of bottom-up cues. The input image is decomposed
through several pre-attentive feature detection mechanisms (sensitive to colour, intensity and so on), which operate in parallel over the entire visual scene. Neurons in
the feature maps then encode for spatial contrast in each of those feature channels. In addition, neurons in each feature map spatially compete for salience, throughlong-range connections that extend far beyond the spatial range of the classical receptive field of each neuron (here shown for one channel; the others are similar).
After competition, the feature maps are combined into a unique saliency map, which topographically encodes for saliency irrespective of the feature channel in which
stimuli appeared salient. The saliency map is sequentially scanned by attention through the interplay between a winner-take-all network (which detects the point ofhighest saliency at any given time) and inhibition of return (which suppresses the last attended location from the saliency map, so that attention can focus onto thenext most salient location). Top-down attentional bias and training can modulate most stages of this bottom-up model (red shading and arrows).

INTENSITY CONTRAST

Spatial difference (for example,
detected by centre–surroundmechanisms) in light intensity(luminance) in a visual scene.
COLOUR OPPONENCY

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

Property of visual neurons to
only respond to certain classes ofstimuli (for example, verticallyorientated bars).

ponents of any model of bottom-up attention. These
are the pre-attentive computation of early visual features across the entire visual scene, their integration toyield a single attentional control command, the generation of attentional scanpaths, the interaction between
COVERT and OVERT attentional deployment (that is, eyemovements) and the interplay between attention andscene understanding.
Pre-attentive computation of visual features

The first processing stage in any model of bottom-up
attention is the computation of early visual features. In
biological vision, visual features are computed in theretina, superior colliculus, lateral geniculate nucleus andearly visual cortical areas21. Neurons at the earliest stages

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are tuned to simple visual attributes such as INTENSITYCONTRAST, COLOUR OPPONENCY, orientation, direction andvelocity of motion, or stereo disparity at several spatial
scales. NEURONAL TUNING becomes increasingly more specialized with the progression from low-level to highlevel visual areas, such that higher-level visual areas
include neurons that respond only to corners or junctions22, shape-from-shading cues23,24 or views of specificreal-world objects25–28.
Early visual features are computed pre-attentively ina massively parallel manner across the entire visual
field (note, however, that we do not imply here thatsuch computation is purely feedforward, as object
recognition and attention can influence it29). Indeed,neurons fire vigorously in these early areas even if the

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REVIEWSPSYCHOPHYSICAL THRESHOLDS

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

A patch of cortex including
neurons responding to allorientations and many spatial
scales, all for a single location in
the visual field.
DIFFERENCE-OF-GAUSSIANS

A filter obtained by taking thedifference between a narrow
Gaussian distribution (theexcitatory centre) and a broader
Gaussian distribution with thesame mean (the inhibitory
surround).
GABOR WAVELET

Product of a sinusoidal grating
and a two-dimensional Gaussianfunction.
IMPULSE RESPONSE FUNCTION

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

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animal is attending away from the receptive field at thesite of recording30, or is anaesthetized31. In addition,
several psychophysical studies, as well as introspection,
indicate that we are not blind to the world outside thefocus of attention. So we can make simple judgments
on objects to which we are not attending32, althoughthose judgments are limited and less accurate thanthose made in the presence of attention2,12,13,33–36. Thus,
although attention does not seem to be mandatory forearly vision, it has recently become clear that attention
can vigorously modulate, in a top-down manner, earlyvisual processing, both in a spatially defined and in a
non-spatial but feature-specific manner37–39. This modulatory effect of attention has been described asenhanced gain30, biased40,41 or intensified33 competition,or enhanced spatial resolution34, or as modulated background activity42, effective stimulus strength43 ornoise44. That attention can modulate early visual processing in a manner equivalent to an increase of stimulus strength43 is computationally an important finding,which directly supports the metaphor of attention as astagelight. Of particular interest from a computational
perspective is a recent study by Lee et al.33 that measured PSYCHOPHYSICAL THRESHOLDS for three simple pattern-discrimination tasks (contrast, orientation andspatial-frequency discriminations) and two spatialmasking tasks (32 thresholds in total). A dual-task paradigm was used to measure thresholds either whenattention was fully available to the task of interest, or
when it was less available because it was engaged elsewhere by a concurrent attention-demanding task. The
mixed pattern of attentional modulation observed in
the thresholds (up to threefold improvement in orientation discrimination with attention, but only 20%
improvement in contrast discrimination) can be quantitatively accounted for by a computational model. Thismodel predicts that attention activates a winner-takeall competition among neurons tuned to different orientations and spatial frequencies within one cortical
33,45
HYPERCOLUMN, a proposition that has recently received
further experimental support46. Because feedback modulation influences the computation of bottom-up features, models of bottom-up attention need to take this
into account. An example of a mixed bottom-up andtop-down model in which attention enhances spatial
resolution47 is discussed later.
Computational models may or may not include
explicit details about early visual feature extraction.
Models that do not are restricted to images for which
the responses of feature detectors can reasonably beguessed. Models that do have the widest applicability to
any visual stimulus, including natural scenes. Computer
implementations of early visual processes are often
motivated by an imitation of biological properties. For
example, the response of a neuron tuned to intensity
centre–surround contrast can be computed by convolving the luminance channel of the input image by a DIFFERENCE-OF-GAUSSIANS (Mexican hat) filter. Similarly, theresponses of orientation-selective neurons are usuallyobtained through convolution by GABOR WAVELETS, which
resemble biological IMPULSE RESPONSE FUNCTIONS48,49.

Another interesting approach consists of implementing
detectors that respond best to those features that arepresent at the locations visited by observers while freeviewing images50,51. For instance, Zetzsche et al.50,52
showed using an eye-tracking device how the eyes preferentially fixate regions with multiple superimposedorientations such as corners, and derived nonlinear
operators that specifically detect those regions.
Irrespective of the method used for early featuredetection, several fundamental computational principles have emerged from both experimental and modelling studies. First, different features contribute withdifferent strengths to perceptual saliency53, and this relative feature weighting can be influenced according to
the demands of the task through top-down modulation38,54 and through training55–58. Second, at a given
visual location, there is little evidence for strong interactions across different visual modalities, such as
colour and orientation53. This is not too surprisingfrom a computational viewpoint, as one would otherwise expect these interactions to also be subject totraining and top-down modulation, and this would
result in the ability to learn to detect conjunctive targets efficiently, which we lack2,59. Within a given broadfeature dimension, however, strong local interactions
between filters sensitive to different properties of thatfeature (for example, between different orientations
within the broad orientation feature) have been precisely characterized, both in physiology60 and psychophysics45. Less evidence exists for within-featurecompetition across different spatial scales45.
Last and most importantly, what seems to matter inguiding bottom-up attention is feature contrast rather
than local absolute feature strength61. Indeed, not only
are most early visual neurons tuned to some type of
local spatial contrast (such as centre–surround or orientated edges), but neuronal responses are also strongly
modulated by context, in a manner that extends far
beyond the range of the classical receptive field (cRF)62.
In a first approximation, the computational consequences of non-classical surround modulation are
twofold. First, a broad inhibitory effect is observed whena neuron is excited with its preferred stimulus but that
stimulus extends beyond the neuron’s cRF, comparedwith when the stimulus is restricted to the cRF and thesurrounding visual space is either empty or contains
non-preferred stimuli63–65. Second, long-range excitatory
connections in V1 seem to enhance the responses of orientation-selective neurons when stimuli extend to form
a contour66,67. These interactions are thought to be crucial in perceptual grouping68,69. The net result is that
activity in early cortical areas is surprisingly sparse whenmonkeys are free-viewing natural scenes70, comparedwith the vigorous responses that can be elicited by smalllaboratory stimuli presented in isolation.
So, the computation of early visual features entails
more than localized operations limited to the cRF ofvisual neurons, as local responses crucially depend on
longer-range contextual influences. To explicitly
demonstrate this idea with a computer model, Itti etal.71 compared purely local spatial frequency ‘richness’

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REVIEWS(as measured by computing local Fourier components
with a magnitude above a certain threshold) with asaliency measure that included broad non-classicalsurround inhibition. They designed images with uniformly rich spatial frequency content (using colour
speckle noise), but which contained a perceptuallysalient target. Although the target was undifferentiatedfrom its surround in terms of spatial frequency content, it was correctly detected by the mechanism that
included contextual competition.
Pre-attentive mechanisms that extract early visualfeatures across the entire visual scene should not be
overlooked in future modelling efforts. Indeed, it has
recently become clear that early vision is far from beinga passive and highly prototyped image-processing frontend that can be accurately modelled by linear filtering
operations. Perceptually, whether a given stimulus issalient or not cannot be decided without knowledge ofthe context in which the stimulus is presented. So, computationally, one must also account for nonlinear interactions across distant spatial locations, which mediate
contextual modulation of neuronal responses.
Saliency

We have seen how the early stages of visual processing
decompose the incoming visual input through an
ensemble of feature-selective filtering processes
endowed with contextual modulatory effects. The question that arises next is how to control a single attentional
focus based on multiple neuronal networks that encode
the incoming sensory signals using multiple representations. To solve this problem, most models of bottom-up
attention follow Koch and Ullman19 and hypothesizethat the various feature maps feed into a unique ‘saliency’ or ‘master’ map2,19. The saliency map is a scalar, twodimensional map whose activity topographically represents visual saliency, irrespective of the feature
dimension that makes the location salient. That is, anactive location in the saliency map encodes the fact that
this location is salient, no matter whether it corresponds
to a red object in a field of green objects, or to a stimulus
moving towards the right while others move towards
the left. On the basis of this scalar topographical representation, biasing attention to focus onto the mostsalient location is reduced to drawing attention towards
the locus of highest activity in the saliency map.
Computationally, an explicit representation ofsaliency in a dedicated map reinforces the idea thatsome amount of spatial selection should be performed
during pre-attentive feature detection. Otherwise, thedivergence from retinal input to many feature maps
could not be followed by a convergence into a saliency
map without ending up with a representation in thesaliency map that is as complex, cluttered and difficult
to interpret as the original image. On the basis of thisdivergence, selection and convergence process, a location is defined as salient if it wins the spatial competition in one or more feature dimensions at one or more
spatial scales. The saliency map then encodes an aggregate measure of saliency that is not tied to any particular feature dimension, thereby providing an efficient

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control strategy for focusing attention to salient locations without consideration of the detailed feature
responses that made those locations salient.
Not surprisingly, many successful models of thebottom-up control of attention are built around asaliency map. What differentiates the models, then, isthe strategy used to prune the incoming sensory input
and extract saliency. In an influential model largely
aimed at explaining visual search experiments, Wolfe54
hypothesized that the selection of relevant features for agiven search task could be performed top-down,
through spatially defined and feature-dependent
weighting of the various feature maps. Saliency is thencomputed in this model as the likelihood that a target
will be present at a given location, based both on bottomup feature contrast and top-down feature weight. Thisview has recently received experimental support fromthe many studies of top-down attentional modulation
mentioned earlier.
Tsotsos and colleagues72 implemented attentional
selection using a combination of a feedforward bottomup feature extraction hierarchy and a feedback selective
tuning of these feature extraction mechanisms. In thismodel, the target for attention is selected at the top
level of the processing hierarchy (the equivalent of asaliency map), on the basis of feedforward activationand possibly additional top-down biasing for certain
locations or features. That location is then propagatedback through the feature extraction hierarchy, throughthe activation of a cascade of winner-take-all networks
embedded within the bottom-up processing pyramid.
Spatial competition for saliency is thus refined at eachlevel of processing, as the feedforward paths that do notcontribute to the winning location are pruned (resulting in the feedback propagation of an ‘inhibitory
beam’ around the selected target).
Milanese and colleagues73 used a relaxation process
to optimize an energy measure consisting of fourterms: first, minimizing inter-feature incoherence
favours those regions that excite several feature maps;
second, minimizing intra-feature incoherence favours
grouping of initially spread activity into small numbers of clusters; third, minimizing total activity in each
map enforces intra-map spatial competition for saliency; and last, maximizing the dynamic range of each
map ensures that the process does not converge
towards uniform maps at some average value.
Although the biological plausibility of this process
remains to be tested, it has yielded a rare example of amodel that can be applied to natural colour images.
Itti et al.71,74 consider a purely bottom-up model, in
which spatial competition for saliency is directly modelled after non-classical surround modulation effects.
The model uses an iterative spatial competition schemewith early termination. At each iteration, a feature map
receives additional inputs from the convolution of itself
by a large difference-of-Gaussians filter. The result ishalf-wave rectified, a nonlinear process that ensuresthat the locations losing the competition are entirely
eliminated. The net effect of this competitive process issimilar to a winner-take-all process with limited

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REVIEWSa Stimulus appears in RF

b Stimulus brought into RF by saccade

RF

RF2
FP2

FP

RF1
FP1

Stable stimulus

Recent onset stimulus

50 Hz
200 msV
H

20°

Figure 2 | Recording saliency. Once a purely computational hypothesis, the idea that saliency
might be explicitly encoded by specific neurons in the cortex has recently received experimental
support from many electrophysiological studies77–82. How can one design an experiment thatspecifically tests whether a neuron responds to the saliency of a stimulus, rather than to the mere
presence of that stimulus in the visual environment? In a particularly interesting experiment,
Gottlieb and colleagues80, recording from the lateral intraparietal sulcus of the awake monkey,
found neurons that responded to visual stimuli only when those stimuli were made salient (byrapidly flashing them on a computer screen), but not otherwise. Their experiment cleverly used
the retinotopic nature of the receptive fields of these neurons to bring a stimulus into their
receptive field (RF) through a SACCADIC EYE MOVEMENT. a | In the control condition, with the eyes
stable at the fixation point (FP), a stimulus is presented in the RF of the neuron being recorded
from. The response elicited by this stimulus could be simply visual, or indicate the saliency of thisstimulus suddenly appearing in the visual field. b | To differentiate between these possibilities, two
additional experiments were designed to be identical for the neuron of interest: a stimulus entered
the RF through a saccade. However, a vigorous response was observed only when the stimulus
had been made salient shortly before the beginning of the trial (by flashing it on and off while it was
still outside the RF of the neuron; ‘recent onset’ condition). (Adapted with permission from REF. 80© (1998) Macmillan Magazines Ltd.)SACCADIC EYE MOVEMENT

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

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inhibitory spread, and allows only a sparse populationof locations to remain active. After competition, all feature maps are simply summed to yield the scalar saliency map at the core of the model. Because it includes acomplete front-end, this model has been widely applied
to the analysis of natural colour scenes. Experimental
results include the reproduction by the model of humanbehaviour in classical visual search tasks (pop-out versus
conjunctive search, and search asymmetries2,74), a
demonstration of very robust saliency computation
with respect to image noise71, the automatic detection of
traffic signs and other salient objects in natural environments58 and the detection of pedestrians in naturalscenes. Finally, the performance of the model at detecting military vehicles in the high-resolution Search2
NATO database of colour rural scenes75 exceeded
human performance, in terms of the estimated numberof locations that need to be visited by the attentional
searchlight before the target is located74.
In view of the numerous models based on a saliency
map, it is important to note that postulating centralized
control based on such a map is not the only computational alternative for the bottom-up guidance of attention. In particular, Desimone and Duncan10 argued thatsaliency is not explicitly represented by specific neurons

and by a saliency map, but instead is implicitly coded ina distributed modulatory manner across the various
feature maps. Attentional selection is then performedon the basis of top-down enhancement of the featuremaps relevant to a target of interest and extinction of
those that are distracting, but without an explicit computation of salience. At least one model successfully
applied this strategy to synthetic stimuli76; note, however, that such top-down biasing (also used in Wolfe’s
Guided Search model to select the weights of various
feature contributions to the saliency map) requires thata specific search task be performed for the model toyield useful predictions.
Although originally a theoretical construct supported by sparse experimental evidence, the idea of aunique, centralized saliency map seems today to be challenged by the apparent existence of multiple areas that
encode stimulus saliency in the visual system of themonkey. These regions include areas in the lateral intraparietal sulcus of the posterior parietal cortex (FIG. 2), thefrontal eye fields, the inferior and lateral subdivisions of
the pulvinar and the superior colliculus77–82.
One possible explanation for this multiplicity could
be that some of the neurons in these areas are indeed
concerned with the explicit computation of saliency, but
are located at different stages along the sensorimotor
processing stream. For example, other functions havealso been assigned to the posterior parietal cortex, suchas that of mapping retinotopic to head-centred coordinate systems and of memorizing targets for eye or arm
movements83,84. So more detailed experimental studies
are needed to reveal subtle differences in the functions
and representations found in these brain areas. Most
probably, the main difference between these brain
regions is the balance between their role in perception
and action15,82. Meanwhile, it is worth noting that, in
addition to the physiological findings just mentioned,
recent psychophysical results also support the idea of an
explicit encoding of saliency in the brain85.
Attentional selection and inhibition of return

The saliency map guides where the attentional stagelightor spotlight86 is to be deployed, that is, to the most salient
location in the scene. One plausible neural architecture
to detect the most salient location is that of a winnertake-all network, which implements a neurally distributed maximum detector19,87. Using this mechanism,however, raises another computational problem: how
can we prevent attention from permanently focusing
onto the most active (winner) location in the saliency
map? One efficient computational strategy, which hasreceived experimental support, consists of transiently
inhibiting neurons in the saliency map at the currently
attended location. After the currently attended location isthus suppressed, the winner-take-all network naturally
converges towards the next most salient location, andrepeating this process generates attentional scanpaths19,71.
Such inhibitory tagging of recently attended locations has been widely observed in human psychophysics as a phenomenon called ‘inhibition of return’(IOR)88,89. A typical psychophysical experiment towww.nature.com/reviews/neuro

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Box 2 | Attention and eye movements
Most of the models and experiments reviewed in this article are concerned with covert
attention, that is, shifts of the focus of attention in the absence of eye movements. In
normal situations, however, we move our eyes 3–5 times per second (that is, 150,000 to
250,000 times every day), to align locations of interest with our foveas. Overt andcovert attention are closely related, as revealed by psychophysical112–115
physiological79,81,83,116 and imaging117,118 studies. The neuronal structures involved
include the deeper parts of the superior colliculus; parts of the pulvinar; the frontal
eye fields in the macaque and its homologue in humans, the precentral gyrus; andareas in the intraparietal sulcus in the macaque and around the intraparietal andpostcentral sulci and adjacent gyri in humans. An example of overlapping
functionality in humans is the study by Hoffman and Subramaniam114. They designed
an experiment in which subjects performed a saccade just preceded by a target
detection task; the greater accuracy found when the target appeared at the endpoint of
the saccade suggests that covert attention had been deployed to that endpoint in
preparation for the saccade.
For models, the addition of eye movements poses several additional computational
challenges. Of particular interest is the need for compensatory mechanisms to shift thesaliency map (typically in retinotopic coordinates) as eye movements occur. Dominey
and Arbib119 proposed a biologically plausible computational architecture that could
perform such dynamic remapping in posterior parietal cortex (PPC). They noted thateye velocity signals have not been found in PPC; however, cells modulated by eye
position have been reported83. They thus devised an iterative scheme to shift thecontents of the saliency map according to the difference between current eye position
and a temporally damped eye position signal. Their algorithm builds a convolution
kernel from the difference between current and damped eye positions, which, whenapplied to the saliency map, translates it in the direction opposite to that difference. A
related approach was proposed by Pouget and Sejnowski84, in which the observed
modulation of neuronal responses in PPC by retinal location and eye position (‘gain
field’ 83) is modelled by a set of basis functions, then used to transform from retinotopic
to head-centred coordinates.
The interaction between overt and covert attention is particularly important formodels concerned with visual search120–122. Further modelling of such interactions
promises a better understanding of many mechanisms, including saccadic suppression,
dynamic remapping of the saliency map and inhibition of return, covert pre-selection oftargets for overt saccades, and the online understanding of complex visual scenes.

evaluate IOR consists of performing speeded local pattern discriminations at various locations in the visual
field; when a discrimination is performed at a location
to which the observer has been previously cued, reaction times are slightly, but significantly, higher than at
locations not previously visited90. These results indicate that visual processing at recently attended locations might be slower, possibly owing to someinhibitory tagging at attended locations. Several
authors have specifically isolated an attentional component of IOR in addition to a motor (response delay)
component91–93.
Computationally, IOR implements a short-term
memory of the previously visited locations and allows
the attentional selection mechanism to focus instead on
new locations. The simplest implementation of IOR
consists of triggering transient inhibitory conductancesin the saliency map at the currently attended location74.
However, this only represents a coarse approximation of
biological IOR, which has been shown to be objectbound, so that it should track and follow moving
objects, and compensate for a moving observer as
well94–97. The frame of reference in which IOR isexpressed is an important issue when the eyes and the

NATURE REVIEWS | NEUROSCIENCE

body move (BOX 2). This frame-of-reference problem
should be accounted for in computational models of
attention. Note that the idea of IOR is not necessarily
contradicted by the recent findings of Horowitz andWolfe98 that visual search seems memoryless: whenelements of a search array were randomly reorganizedat short time intervals while subjects were searching fora specific target, search efficiency was not degraded
compared with when the search array remained stationary. Although these results preclude perfect memorization of all previously attended locations (otherwise,
search on a stable array should be more efficient thanon a constantly changing array), they do not preclude
an explanation in which the positions of the last few
visited items were remembered, in accordance with thelimited lifespan reported for IOR90.
Although simple in principle, IOR is computationally a very important component of attention, in that itallows us — or a model — to rapidly shift the attentional focus over different locations with decreasing
saliency, rather than being bound to attend only to thelocation of maximal saliency at any given time. The
role of IOR in active vision and overt attention poseschallenges that will need to be addressed in more detailby future models (BOX 2).
Attention and recognition

So far, we have reviewed computational modelling andsupporting experimental evidence for a basic architecture concerned with the bottom-up control of attention: early visual features are computed in a set of topographical feature maps; spatial competition for saliency
prunes the feature responses to only preserve a handfulof active locations; all feature maps are then combined
into a unique scalar saliency map; and, finally, thesaliency map is scanned by the focus of attention
through the interplay between winner-take-all and IOR.
Although such a simple computational architecture
might accurately describe how attention is deployedwithin the first few hundreds of milliseconds after thepresentation of a new scene, it is obvious that a more
complete model of attentional control must include
top-down, volitional biasing influences as well. The
computational challenge, then, lies in the integration ofbottom-up and top-down cues, such as to provide
coherent control signals for the focus of attention, andin the interplay between attentional orientating andscene or object recognition.
One of the earliest models that combines object
recognition and attention is MORSEL99, in which attentional selection was shown to be necessary for object
recognition. This model is applied to the recognition ofwords processed through a recognition hierarchy.
Without attentional selection, the representations ofseveral words in a scene would conflict and confuse thatrecognition hierarchy, yielding multiple superimposed
representations at the top level. The addition of a topdown attentional selection process allowed the model todisambiguate recognition by focusing on one word at atime. Another early model that is worth mentioning
here is described in REF. 100.

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Incoming visual scene

Top-down processing
Acquired knowledge
All scene and object classes

Polygons

Bottom-up feature
extraction
Rectangles

SquareDiamond

Salient locations
Sensory input
at current eye position

Eye movement withmaximum information gain

Figure 3 | Combined model of attentional selection and object recognition. Attention
scans the scene to gather as much information as possible that can help discriminate between
several recognition hypotheses. The model has two main components. First, a bottom-up feature
extraction pathway extracts informative image regions from an incoming visual scene (forexample, the corners of the diamond in the present illustration). Second, a trained knowledge
base hierarchically represents object classes and encodes for both expected visual features at a
set of critical points on these objects, and motor commands to move the eyes from one criticalpoint to another. Recognition is then achieved by choosing for the next eye movement the
movement that maximizes information gain, that is, that best prunes the tree of known objects. In
the hypothetical example shown to illustrate this idea, the first eye movement might thus go to the
top corner of the object; finding sharp edges there would then suggest that this is a polygon. The
knowledge base would then direct gaze to the most salient point directly below the currently
fixated point, as that eye movement would best discriminate between the several known
polygons; looking at the orientations of the features there, it becomes clear that the object is adiamond rather than a square or one of several possible rectangles. (Adapted with permission
from REF. 101.)

A very interesting model that uses spatial shifts ofattention during recognition was recently provided by
Schill et al.101. Their model performs scene (or object)recognition, using attention (or eye movements) to
focus on those parts of the scene that are most informative for disambiguating identity. To this end, a hierarchical knowledge tree is built through training. Its leaves
represent identified objects, intermediary nodes represent more general object classes and links between
nodes contain sensorimotor information used for discrimination between possible objects (that is, bottomup feature response to be expected for particular points
in the object and eye movements targeted at thosepoints). During the iterative recognition of an object,
the system programs its next fixation towards the location that will maximize the gain of information about
the object. This permits the model to discriminate
between the current candidate object classes (FIG. 3).
Rybak et al.102 proposed a related model, in which
scanpaths (containing motor control directives stored ina ‘where’ memory and locally expected bottom-up features stored in a ‘what’ memory) are learned for each

8 | MARCH 2001 | VOLUME 2

scene or object to be recognized. When presented with a
new image, the model starts by selecting candidate scanpaths by matching bottom-up features in the image tothose stored in the ‘what’ memory. For each candidate
scanpath, the model deploys attention according to thedirectives in the ‘where’ memory and compares the local
contents of the ‘what’ memory at each fixation with thelocal image features. This model can recognize complex
greyscale scenes and faces in a translation-, rotation- andscale-independent manner.
Deco and Zihl have recently proposed another modelthat combines attentional selection and object recognition47. Their model starts by selecting candidate object
locations in a bottom-up manner through a coarse-scale
analysis of the image. An attentional mechanism scans
the candidate locations in a serial fashion and performs
object recognition at progressively finer scales until a sufficient recognition score is obtained for an object stored
in memory. This model has been successfully applied to
psychophysical experiments that show attentional
enhancement of spatial resolution (see also REFS 34,103 forrelated experiments and modelling).
A more extreme view is expressed by the ‘scanpath
theory’ of Stark104, in which the control of eye movements is almost exclusively under top-down control.
The theory proposes that what we see is only remotely
related to the patterns of activation in our retinas. This issuggested by our permanent illusion of vivid perception
over the entire field of view, although only the central
two degrees of our foveal vision provide crisp samplingof the visual world. Rather, the scanpath theory argues
that a cognitive model of what we expect to see is thebasis for our percept; the sequence of eye movementsthat we make to analyse a scene, then, is mostly controlled by our cognitive model of that scene. This theory
has had several successful applications to robotics control, in which an internal model of a robot’s working
environment was used to restrict the analysis of incoming video sequences to a small number of circumscribedregions important for a given task105.
One important challenge for combined models of
attention and recognition is finding suitable neuronalcorrelates for the various components. Despite the biological inspiration in these architectures, the models
reviewed here do not relate in much detail to biologicalcorrelates of object recognition. Although several biologically plausible models have been proposed for object
recognition in the ventral ‘what’ stream (in particular,
REFS 106,107), their integration with neurobiological models concerned with attentional control in the dorsal‘where’ stream remains an open issue. This integration
will, in particular, have to account for the increasing
experimental support for an object-based spatial focus
of attention108–110.
Summary

We have discussed recent advances in the study of biologically plausible computational models of attention,with a particular emphasis on bottom-up control ofattentional deployment. Throughout this review, wehave stressed five important computational trends thatwww.nature.com/reviews/neuro

REVIEWS
have emerged from the literature. First, saliency isderived from low-level visual features but, more thanabsolute feature strength or other detailed characteristics of the features, what seems to be important for thecomputation of saliency is feature contrast with respect
to the contextual surround. Second, saliency increasingly
seems to be a quantity that is coded explicitly in cortex
and separate from the visual features. This reinforces theonce hypothetical concept of an explicit saliency map.
Furthermore, several models have demonstrated thecomputational usefulness and plausibility of such an
explicit map by successfully reproducing the behaviourof humans and monkeys in search tasks. Meanwhile,neural analogues of the saliency map are being found at
multiple locations in the visual system of the macaque.
This poses a new challenge of how to integrate thesemany maps to yield unitary behaviour. Third, attention
will not shift unless the currently attended (mostsalient) location is somehow disabled (otherwise, anymodel looking for saliency will keep coming back to themost salient location). Inhibition of return is consequently an essential computational component of attention and, indeed, it has been recently described as acomplex, object-based and dynamically adaptive
process that needs to be better modelled. Fourth, covert
attention and eye movements are increasingly believed

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to share a common neuronal substrate. This poses serious computational problems with respect to the frameof reference in which saliency and IOR are computed.
Recent evidence for world-centred and object-centred
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Links
FURTHER INFORMATION Laurent Itti’s lab | Christof Koch’slab | Supplementary material for Figure 2

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

www.nature.com/reviews/neuroONLINE
Bios

Laurent Itti received his M.S. in Image Processing from the Ecole Nationale Superieure des Telecommunications, Paris, in 1994, and Ph.D. in
Computation and Neural Systems from Caltech, Pasadena, California, in 2000. In September 2000, he became assistant professor of Computer
Science at the University of Southern California, Los Angeles, California. His primary research interest is in biologically plausible computational
brain modelling, and in the comparison of model simulations to empirical measurements from living systems. Of particular interest in his laboratory is the development of computational models of biological vision with applications to machine vision.
Christof Koch is the Lois and Victor Troendle Professor of Cognitive and Behavioural Biology at the California Institute of Technology. He obtained
his Ph.D. in Physics from the University of Tübingen, Germany. His research focuses on understanding the biophysical mechanisms underlying
information processing in individual nerve cells as well as the neuronal operations underlying spatial vision, motion, shape perception and visual
attention in the primate visual system, using electrophysiological, brain imaging, psychophysical and computational tools. Together with Dr Francis
Crick, he works on the neuronal basis of visual consciousness.
Summary• We review recent work on computational models of focal visual attention, with emphasis on the bottom-up, saliency- or image-based control ofattentional 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 dependingon 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 haverecently 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 ofsuch a computational strategy remain to be developed.
Links

Laurent Itti’s lab
http://ilab.usc.edu/
Christof Koch’s lab
http://www.klab.caltech.edu/
Supplementary information for Figure 2
http://ilab.usc.edu/itti/nrn/