What and where: A Bayesian inference theory of attention

Chikkerur, Sharat; Serre, T.; Tan, Cheston; Poggio, Tomaso

doi:10.1016/j.visres.2010.05.013

Cited by 180 publications

(148 citation statements)

References 92 publications

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“…In recent years, the predictive processing view of perception has been usefully applied to visual perception (Yuille & Kersten, 2006), attention (Chikkerur, Serre, Tan, & Poggio, 2010;Feldman & Friston, 2010;Rao, 2005), interoception (Seth, 2013), action and decision making (Beck et al, 2008). Of most relevance to the present discussion, the framework has also been applied to highly social aspects of human functioning.…”

Section: Predictive Perceptionmentioning

confidence: 99%

A social Bayesian brain: How social knowledge can shape visual perception

Otten

Seth

Pinto

2017

Brain and Cognition

110

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(2017) A social Bayesian brain: how social knowledge can shape visual perception. Brain and Cognition, 112. pp. 69-77. ISSN 0278-2626 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/61149/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A growing body of research suggests that social contextual factors such as desires and goals, affective states and stereotypes can shape early perceptual processes. We suggest that a generative Bayesian approach towards perception provides a powerful theoretical framework to accommodate how such high-level social factors can influence low-level perceptual processes in their earliest stages. We review experimental findings that show how social factors shape the perception and evaluation of people, behavior, and socially relevant objects or information. Subsequently, we summarize the generative view of perception within the 'Bayesian brain', and show how such a framework can account for the pervasive effects of top-down social knowledge on social cognition. Finally, we sketch the theoretical and experimental implications of social predictive perception, indicating new directions for research on the effects and neurocognitive underpinnings of social cognition. 3Humans are intensely social animals: social interaction in all shapes and sizes forms a core aspect of our existence. The things we know, both consciously (explicitly) and unconsciously (implicitly), about other people helps us to swiftly interpret their behaviour and respond appropriately. In recent years, evidence has mounted that social knowledge shapes not just how we interpret the world around us, but also how we perceive it (see for example Barrett & Bar, 2009). Research from experimental social psychology focussing mainly on visual perception suggests that social contextual factors can subtly but substantially change how a stimulus is processed, which in turn changes the percept that people experience. In this paper, we provide an overview of the ...

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Section: Predictive Perceptionmentioning

confidence: 99%

A social Bayesian brain: How social knowledge can shape visual perception

Otten

Seth

Pinto

2017

Brain and Cognition

110

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“…[18], [38] and [39]). In this paper, we propose a new approach to apply Bayes's theorem for computing depth saliency maps based on features extracted from a depth map.…”

Section: B a Bayesian Approach Of Depth Saliency Map Generationmentioning

confidence: 99%

Computational Model of Stereoscopic 3D Visual Saliency

Wang

daSilva

Le-Callet

et al. 2013

IEEE Trans. on Image Process.

119

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-Many computational models of visual attention performing well in predicting salient areas of 2D images have been proposed in the literature. The emerging applications of stereoscopic 3D display bring additional depth information affecting the human viewing behavior, and require extensions of the efforts made in 2D visual modeling. In this paper, we propose a new computational model of visual attention for stereoscopic 3D still image. Apart from detecting salient areas based on 2D visual features, the proposed model takes depth as an additional visual dimension. The measure of depth saliency is derived from the eye movement data obtained from an eye-tracking experiment using synthetic stimuli. Two different ways of integrating depth information in the modeling of 3D visual attention are then proposed and examined. For the performance evaluation of 3D visual attention models, we have created an eye-tracking database which contains stereoscopic images of natural content and is publicly available along with this paper. The proposed model gives a good performance, compared to that of state-of-the-art 2D models on 2D images. The results also suggest that a better performance is obtained when depth information is taken into account through the creation of a depth saliency map rather than when it is integrated by a weighting method.

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“…This approach, which is related to David Marr's implementational level of analysis (10), has been fruitful, as evidenced by the fact that several mechanistic models have been created that can account for a variety of firing-rate changes seen in a number of studies (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Less work, however, has focused on Marr's "algorithmic/ representational level," which in this context would address how particular physiological changes enable improvements in neural representations that are useful in solving specific "computationallevel" tasks (such as recognizing objects).…”

mentioning

confidence: 99%

Object decoding with attention in inferior temporal cortex

Zhang

Meyers

Bichot

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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135

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Recognizing objects in cluttered scenes requires attentional mechanisms to filter out distracting information. Previous studies have found several physiological correlates of attention in visual cortex, including larger responses for attended objects. However, it has been unclear whether these attention-related changes have a large impact on information about objects at the neural population level. To address this question, we trained monkeys to covertly deploy their visual attention from a central fixation point to one of three objects displayed in the periphery, and we decoded information about the identity and position of the objects from populations of ∼200 neurons from the inferior temporal cortex using a pattern classifier. The results show that before attention was deployed, information about the identity and position of each object was greatly reduced relative to when these objects were shown in isolation. However, when a monkey attended to an object, the pattern of neural activity, represented as a vector with dimensionality equal to the size of the neural population, was restored toward the vector representing the isolated object. Despite this nearly exclusive representation of the attended object, an increase in the salience of nonattended objects caused "bottom-up" mechanisms to override these "top-down" attentional enhancements. The method described here can be used to assess which attention-related physiological changes are directly related to object recognition, and should be helpful in assessing the role of additional physiological changes in the future.macaque | vision | readout | population coding | neural coding P revious work examining how attention influences the ventral visual pathway has shown that attending to a stimulus in the receptive field (RF) of a neuron is correlated with increases in firing rates or effective contrast, increases in gamma synchronization, and decreases in the Fano factor and noise correlation, compared with when attention is directed outside the RF (1-8). However, because these effects are often relatively modest, it has been unclear whether these effects would have a large impact on information contained at the population level when any arbitrary stimulus needs to be represented. Indeed, recent work has suggested that high-level brain areas can represent multiple objects with the same accuracy as single objects even when attention is not directed to a specific object (9), which raises questions about the importance of the attention-related effects that have been reported in previous studies.Another feature of the previous neurophysiology work on attention has been that it has primarily focused on the neural mechanisms that underlie attention (i.e., what neural circuits/ processing underlie the changes seen with attention). This approach, which is related to David Marr's implementational level of analysis (10), has been fruitful, as evidenced by the fact that several mechanistic models have been created that can account for a variety of firing-rate changes seen in ...

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What and where: A Bayesian inference theory of attention

Cited by 180 publications

References 92 publications

A social Bayesian brain: How social knowledge can shape visual perception

A social Bayesian brain: How social knowledge can shape visual perception

Computational Model of Stereoscopic 3D Visual Saliency

Object decoding with attention in inferior temporal cortex

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