A Cognitive Model of Saliency, Attention, and Picture Scanning

Cutsuridis, Vassilis

doi:10.1007/s12559-009-9024-9

Cited by 23 publications

(19 citation statements)

References 45 publications

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“…An alternative is to combine bottom-up information with knowledge about where objects are likely to occur, and this is implemented in the contextual guidance model of Torralba et al [25]. In Cutsuridis' model [26], bottom-up saliency is but one of the three parts of the gaze-orienting system, with this initial visual processing combining with neuromodulation and top-down recognition to select targets. Although these and other models combine saliency and target knowledge, it is not known whether target saliency still plays a role (as it would do if saliency and target knowledge were combined) or whether the saliency map is effectively reduced to zero, overridden as a flat landscape over which top-down selection occurs.…”

Section: Saliency In Naturalistic Visual Searchmentioning

confidence: 99%

If Visual Saliency Predicts Search, Then Why? Evidence from Normal and Gaze-Contingent Search Tasks in Natural Scenes

Foulsham

Underwood

2010

Cogn Comput

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The Itti and Koch (Vision Research 40: 1489-1506, 2000 saliency map model has inspired a wealth of research testing the claim that bottom-up saliency determines the placement of eye fixations in natural scenes. Although saliency seems to correlate with (although not necessarily cause) fixation in free-viewing or encoding tasks, it has been suggested that visual saliency can be overridden in a search task, with saccades being planned on the basis of target features, rather than being captured by saliency. Here, we find that target regions of a scene that are salient according to this model are found quicker than control regions (Experiment 1). However, this does not seem to be altered by filtering features in the periphery using a gaze-contingent display (Experiment 2), and a deeper analysis of the eye movements made suggests that the saliency effect is instead due to the meaning of the scene regions. Experiment 3 supports this interpretation, showing that scene inversion reduces the saliency effect. These results suggest that saliency effects on search may have nothing to do with bottom-up saccade guidance.

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Section: Saliency In Naturalistic Visual Searchmentioning

confidence: 99%

If Visual Saliency Predicts Search, Then Why? Evidence from Normal and Gaze-Contingent Search Tasks in Natural Scenes

Foulsham

Underwood

2010

Cogn Comput

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“…First, this shows that specialists were attending to the semantically interesting regions and secondly implies an overriding effect of domain knowledge on saliency. Cutsuridis [56] suggests a 'focus-of-attention' mechanism, within a cognitive model of scene perception, which amplifies processing relevant information and suppresses irrelevant information. In this case, relevant information for the domain specialists is the part of the picture that holds semantic significance.…”

Section: Discussionmentioning

confidence: 99%

See What I’m Saying? Expertise and Verbalisation in Perception and Imagery of Complex Scenes

Humphrey

Underwood

2010

Cogn Comput

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How does describing a previously viewed picture affect our memory for it? Does verbalisation affect our eye movements even when the picture has disappeared? When viewing a photograph, the sequences of eye movements we make ('scanpaths') are influenced by both bottom-up visual saliency and top-down cognitive knowledge. Recognition memory is enhanced and the similarity of scanpaths at encoding and recognition is greater for domain-specific pictures. A similarity in scanpaths is also observed during imagery but to a greatly reduced degree. This study explored whether scanpath similarity could be improved by verbalising one's memory of the picture and whether the previously observed domain-specific advantage was still present when no bottom-up information was available. Specialists and controls were shown a set of photographs, and after each one had to either visualise it or describe it from memory. The stimuli were complex scenes, half of which contained a domain-specific object. Recognition accuracy was increased by post-stimulus verbalisation, and specialists demonstrated an advantage for stimuli that contained domain-relevant information. Saliency influenced both verbal feedback and eye movements but was moderated by domain expertise. Scanpaths were more similar when pictures were described compared to when imagined, and specialists produced more similar scanpaths when describing domain-specific pictures, compared to control pictures and control participants.

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“…This kind of knowledge might be encoded in the hippocampal system. Neurobiologial data on how and where saliency maps can be implemented in the brain can be found in [31,32,29]. The trial-and-error learning processes performed by the model might correspond to the processes taking place in the portions of the basal ganglia dedicated to the control of the eye (striatum and substantia nigra pars reticulata [33]).…”

Section: Actor-critic Component (Top-down Attention)mentioning

confidence: 99%

Learning to Look in Different Environments: An Active-Vision Model Which Learns and Readapts Visual Routines

Ognibene

Pezzulo

Baldassare

2010

From Animals to Animats 11

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Abstract.One of the main claims of the active vision framework is that finding data on the basis of task requirements is more efficient than reconstructing the whole scene by performing a complete visual scan. To be successful, this approach requires that agents learn visual routines to direct overt attention to locations with the information needed to accomplish the task. In ecological conditions, learning such visual routines is difficult due to the partial observability of the world, the changes in the environment, and the fact that learning signals might be indirect. This paper uses a reinforcement-learning actor-critic model to study how visual routines can be formed, and then adapted when the environment changes, in a system endowed with a controllable gaze and reaching capabilities. The tests of the model show that: (a) the autonomouslydeveloped visual routines are strongly dependent on the task and the statistical properties of the environment; (b) when the statistics of the environment change, the performance of the system remains rather stable thanks to the re-use of previously discovered visual routines while the visual exploration policy remains for long time sub-optimal. We conclude that the model has a robust behaviour but the acquisition of an optimal visual exploration policy is particularly hard given its complex dependence on statistical properties of the environment, showing another of the difficulties that adaptive active vision agents must face.

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A Cognitive Model of Saliency, Attention, and Picture Scanning

Cited by 23 publications

References 45 publications

If Visual Saliency Predicts Search, Then Why? Evidence from Normal and Gaze-Contingent Search Tasks in Natural Scenes

If Visual Saliency Predicts Search, Then Why? Evidence from Normal and Gaze-Contingent Search Tasks in Natural Scenes

See What I’m Saying? Expertise and Verbalisation in Perception and Imagery of Complex Scenes

Learning to Look in Different Environments: An Active-Vision Model Which Learns and Readapts Visual Routines

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