An integrated model of eye movements and visual encoding

Salvucci, Dario D.

doi:10.1016/s1389-0417(00)00015-2

Cited by 188 publications

(179 citation statements)

References 30 publications

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“…1C). This temporal relation between foveal analysis and peripheral selection is a core assumption of models of eye movement control in reading (14,(19)(20)(21) and other visual-motor domains (22,23). Finally, Fig.…”

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confidence: 86%

Foveal analysis and peripheral selection during active visual sampling

Ludwig

Davies

Eckstein

2014

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

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Human vision is an active process in which information is sampled during brief periods of stable fixation in between gaze shifts. Foveal analysis serves to identify the currently fixated object and has to be coordinated with a peripheral selection process of the next fixation location. Models of visual search and scene perception typically focus on the latter, without considering foveal processing requirements. We developed a dual-task noise classification technique that enables identification of the information uptake for foveal analysis and peripheral selection within a single fixation. Human observers had to use foveal vision to extract visual feature information (orientation) from different locations for a psychophysical comparison. The selection of to-be-fixated locations was guided by a different feature (luminance contrast). We inserted noise in both visual features and identified the uptake of information by looking at correlations between the noise at different points in time and behavior. Our data show that foveal analysis and peripheral selection proceeded completely in parallel. Peripheral processing stopped some time before the onset of an eye movement, but foveal analysis continued during this period. Variations in the difficulty of foveal processing did not influence the uptake of peripheral information and the efficacy of peripheral selection, suggesting that foveal analysis and peripheral selection operated independently. These results provide important theoretical constraints on how to model target selection in conjunction with foveal object identification: in parallel and independently.A lmost all human visually guided behavior relies on the selective uptake of information, due to sensory and cognitive limitations. On the sensory side, the sampling of visual input by the retinal mosaic of photoreceptors becomes increasingly sparse and irregular away from central vision (1). In addition, fewer cortical neurons are devoted to the analysis of peripheral visual information (cortical magnification) (2, 3). Humans and other animals with so-called foveated visual systems have evolved gazeshifting mechanisms to overcome these limitations. Saccadic eye movements serve to rapidly and efficiently deploy gaze to objects and regions of interest in the visual field. Sampling the environment appropriately with gaze is the starting point of adaptive visual-motor behavior (4, 5).Studies have shown that saccadic eye movements are guided by analysis of information in the visual periphery up to 80-100 ms before saccade execution (6-8). However, active vision typically requires humans not only also to analyze information in the visual periphery to decide where to fixate next (peripheral selection), but also to analyze the information at the current fixation location (foveal analysis). Not much is known about how foveal analysis and peripheral selection are coordinated and interact. In this regard, we need to know (i) whether and to what extent foveal analysis and peripheral selection are constrained by a common bo...

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mentioning

confidence: 86%

Foveal analysis and peripheral selection during active visual sampling

Ludwig

Davies

Eckstein

2014

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

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“…The foveated part of the scene can then be processed in more detail. Thanks to the availability of sophisticated eye tracking technologies, several recent works have confirmed this link between visual attention and eye movements [2,3,4]. Hoffman et al suggested in [5] that saccades to a location in space are preceded by a shift of visual attention to that location.…”

Section: Introductionmentioning

confidence: 97%

Empirical Validation of the Saliency-based Model of Visual Attention

Ouerhani¹,

Wartburg²,

Hügli³

et al. 2003

ELCVIA

119

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Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer vision, the paradigm of visual attention has been widely investigated and a saliencybased model of visual attention is now available that is commonly accepted and used in the field, despite the fact that its biological grounding has not been fully assessed. This work proposes a new method for quantitatively assessing the plausibility of this model by comparing its performance with human behavior. The basic idea is to compare the map of attention -the saliency map -produced by the computational model with a fixation density map derived from eye movement experiments. This human attention map can be constructed as an integral of single impulses located at the positions of the successive fixation points. The resulting map has the same format as the computer-generated map, and can easily be compared by qualitative and quantitative methods. Some illustrative examples using a set of natural and synthetic color images show the potential of the validation method to assess the plausibility of the attention model.

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“…This can be partially explained by the anatomical structure of the human retina. Thanks to the availability of sophisticated eye tracking technologies, several recent works have confirmed this link between visual attention and eye movements [1,2,3]. Thus, eye movement recording is a suitable means for studying the temporal and spatial deployment of visual attention in most situations.…”

Section: Introductionmentioning

confidence: 92%

Model Performance for Visual Attention in Real 3D Color Scenes

Hügli

Jost

Ouerhani

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

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Abstract. Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene. The saliency-based model of visual attention is widely used to simulate this visual mechanism on computers. Though biologically inspired, this model has been only partially assessed in comparison with human behavior. The research described in this paper aims at assessing its performance in the case of natural scenes, i.e. real 3D color scenes. The evaluation is based on the comparison of computer saliency maps with human visual attention derived from fixation patterns while subjects are looking at the scenes. The paper presents a number of experiments involving natural scenes and computer models differing by their capacity to deal with color and depth. The results point on the large range of scene specific performance variations and provide typical quantitative performance values for models of different complexity.

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An integrated model of eye movements and visual encoding

Cited by 188 publications

References 30 publications

Foveal analysis and peripheral selection during active visual sampling

Foveal analysis and peripheral selection during active visual sampling

Empirical Validation of the Saliency-based Model of Visual Attention

Model Performance for Visual Attention in Real 3D Color Scenes

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