Eye movements in active visual search: A computable phenomenological model

Keech, T. D.; Resca, Lorenzo

doi:10.3758/app.72.2.285

Cited by 10 publications

(9 citation statements)

References 102 publications

(156 reference statements)

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“…The model presented in [62] embeds at least one parameter suitable to be tuned to obtain different saccade length distributions on static images, although statistics obtained by varying such parameter are still far from those of human data. Closer to our study is the model by Keech and Resca [63] that mimics phenomenologically the observed eye movement trajectories and where randomness is captured through a Monte Carlo selection of a particular eye movement based on its probability; probabilistic modeling of eye movement data has been also discussed in [64]. However, both models address the specific task of conjunctive visual search and are limited to static scenes.…”

Section: Discussionmentioning

confidence: 97%

Ecological Sampling of Gaze Shifts

Boccignone

Ferraro

2014

IEEE Trans. Cybern.

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Abstract-Visual attention guides our gaze to relevant parts of the viewed scene, yet the moment-to-moment relocation of gaze can be different among observers even though the same locations are taken into account. Surprisingly, the variability of eye movements has been so far overlooked by the great majority of computational models of visual attention. In this paper we present the ecological sampling model, a stochastic model of eye guidance explaining such variability. The gaze shift mechanism is conceived as an active random sampling that the foraging eye carries out upon the visual landscape, under the constraints set by the observable features and the global complexity of the landscape. By drawing on results reported in the foraging literature, the actual gaze relocation is eventually driven by a stochastic differential equation whose noise source is sampled from a mixture of α-stable distributions. This way, the sampling strategy proposed here allows to mimic a fundamental property of the eye guidance mechanism: where we choose to look next at any given moment in time, it is not completely deterministic, but neither is it completely random To show that the model yields gaze shift motor behaviors that exhibit statistics similar to those displayed by human observers, we compare simulation outputs with those obtained from eye-tracked subjects while viewing complex dynamic scenes.

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Section: Discussionmentioning

confidence: 97%

Ecological Sampling of Gaze Shifts

Boccignone

Ferraro

2014

IEEE Trans. Cybern.

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“…The model presented in [67] embeds at least one parameter suitable to be tuned to obtain different saccade length distributions on static images. Closer to our study is the model by Keech and Resca [68] that mimics phenomenologically the eye movement trajectories observed in a conjunctive visual search task and where randomness is captured in the model through a Monte Carlo selection of a particular eye movement based on its probability. Probabilistic modeling of eye movement data during a conjunction search task is also discussed in [69].…”

Section: Discussionmentioning

confidence: 99%

“…Further, there is evidence for memory and persistence effects in directions (forward bias, [68]), that may be better modelled by considering a conditional prior in the form pðyðtÞ9yðtÀ1ÞÞ.…”

Section: Discussionmentioning

confidence: 99%

Gaze shift behavior on video as composite information foraging

Boccignone

Ferraro

2013

Signal Processing: Image Communication

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a b s t r a c tThe ability to predict, given an image or a video, where a human might fixate elements of a viewed scene has long been of interest in the vision community. However, one point that is not addressed by the great majority of computational models is the variability exhibited by different observers when viewing the same scene, or even by the same subject along different trials. Here we present a model of gaze shift behavior which is driven by a composite foraging strategy operating over a time varying visual landscape and accounts for such variability.The system performs a deterministic walk if in a neighborhood of the current position of the gaze there exists a point of sufficiently high saliency; otherwise the search is driven by a Langevin equation whose random term is generated by an a-stable distribution.Results of the simulations on complex videos from the publicly available University of South California CRCNS eye-1 dataset are compared with eye-tracking data and show that the model yields gaze shift motor behaviors that exhibits statistics similar to those exhibited by human observers.

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“…Few works have been trying to cope with the variability issue, after the early work by Stark and colleagues [33,43]. Kimura et al [56] have incorporated simple eye-movements patterns as a probabilistic prior; Ho Phuoc et al [48] embed at least one parameter suitable to be tuned to obtain different saccade length distributions on static images, though statistics obtained by varying such parameter are still far from those of human data; others try to capture eye movements randomness [55,87] but limiting to specific tasks such as conjunctive visual search. A few more exceptions can be found, but only in the very peculiar field of eyemovements in reading (see Feng for a discussion [35]).…”

Section: Generation Of Gaze Shiftsmentioning

confidence: 99%

Modelling Task-Dependent Eye Guidance to Objects in Pictures

et al. 2014

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We introduce a model of attentional eye guidance based on the rationale that the deployment of gaze is to be considered in the context of a general actionperception loop relying on two strictly intertwined processes: sensory processing, depending on current gaze position, identifies sources of information that are most valuable under the given task; motor processing links such information with the oculomotor act by sampling the next gaze position and thus performing the gaze shift. In such a framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the payoff of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. The different levels of the actionperception loop are represented in probabilistic form and eventually give rise to a stochastic process that generates the gaze sequence. This way the model also accounts for statistical properties of gaze shifts such as individual scan path variability. Results of the simulations are compared either with experimental data

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Eye movements in active visual search: A computable phenomenological model

Cited by 10 publications

References 102 publications

Ecological Sampling of Gaze Shifts

Ecological Sampling of Gaze Shifts

Gaze shift behavior on video as composite information foraging

Modelling Task-Dependent Eye Guidance to Objects in Pictures

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