Ecological Sampling of Gaze Shifts

Boccignone, Giuseppe; Ferraro, Mario

doi:10.1109/tcyb.2013.2253460

Cited by 29 publications

(59 citation statements)

References 60 publications

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“…Then the distribution of first fixations pooled over a sufficiently large number of trials or observers would be centered on the positions a, b, and c, and would coincide with the distribution of second fixations, and so on. This behavior would be consistent with a model of gaze shifts based on a random walk guided by the local properties of the scene [20,21]. Repeated iterations of the model would yield similar distributions with the same scene, whether starting from the first, second, or subsequent fixations, and would yield different distributions with different scenes, precisely as observed.…”

Section: Discussionsupporting

confidence: 75%

“…Fixation data from each scene were pooled over observers to reflect the systematic effects of scene structure and viewing bias [47] rather than random variations between observers [21].…”

Section: E Gaze Analysismentioning

confidence: 99%

“…At best, in a nonparametric framework, local features explain approximately 60% of the variance in point of gaze in free viewing of gray-scale images [10]. The remaining variance is usually attributed to the effects of search strategy and other cognitive factors [3,4,19], in either a deterministic or random way [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Influence of local scene color on fixation position in visual search

Amano

Foster

2014

J. Opt. Soc. Am. A

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Where observers concentrate their gaze during visual search depends on several factors. The aim here was to determine how much of the variance in observers' fixations in natural scenes can be explained by local scene color and how that variance is related to viewing bias. Fixation data were taken from an experiment in which observers searched images of 20 natural rural and urban scenes for a small target. The proportion R 2 of the variance explained in a regression on local color properties (lightness and the red-green and yellow-blue chromatic components) ranged from 1% to 85%, depending mainly on how well those properties were consistent with observers' viewing bias. When viewing bias was included in the regression, values of R 2 increased, ranging from 62% to 96%. By comparison, local lightness and local lightness contrast, edge density, and entropy each explained less variance than local color properties. Local scene color may have a much stronger influence on gaze position than is generally recognized, capturing significant aspects of scene structure on target search behavior.

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Section: Discussionsupporting

confidence: 75%

“…Fixation data from each scene were pooled over observers to reflect the systematic effects of scene structure and viewing bias [47] rather than random variations between observers [21].…”

Section: E Gaze Analysismentioning

confidence: 99%

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Influence of local scene color on fixation position in visual search

Amano

Foster

2014

J. Opt. Soc. Am. A

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“…This type of movements can also be found in other situations, like birdflights, and these models are also known as visual foraging or Levy-flight models (for more information see [1][2][3][4]). It is also related to the general problem of novelty detection (see [5]).…”

Section: Extreme Value Statistics and Information Geometrymentioning

confidence: 90%

Eye movements and information geometry

Lenz

2016

J. Opt. Soc. Am. A

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The human visual system uses scanning processes to gather visual information. These scanning processes can roughly be divided into two different types: small movements around fixation points and larger movements between fixation points. The processes are often modeled as random walks and recent models based on heavy tail distributions, also known as Levý flights, have been used in these investigations. In contrast to these approaches we do not model the stochastic processes, but we will show that the step length of the movements between fixation points follow generalized Pareto distributions (GPD). We will use general arguments from the theory of extreme value statistics to motivate the usage of the GPD and show empirically that the GPDs provide good fits for measured eye tracking data. In the framework of information geometry the GPDs with a common threshold form a two-dimensional Riemann manifold with the Fisher information matrix as a metric. We compute the Fisher information matrix for the GPDs and introduce a feature vector describing a GPD by its parameters and different geometrical properties of its Fisher information matrix. In our statistical analysis we use eye tracker measurements in a database with 15 observers viewing 1003 images under free-viewing conditions. We use standard software from toolboxes in Matlab ® with their standard parameter settings and show that a naive Bayes classifier using the eigenvalues of the Fisher information matrix provides a high classification rate identifying the 15 observers in the database.

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“…Thus, in this respect such term should not be generally intended as cognitive or conscious decision. In a foraging metaphor (see [12] for an in-depth technical presentation and a recent paper by Wolfe [116] relating foraging to visual search), the eye is a forager that feeds on valuable information. The forager, moment to moment, is confronted with the choice between "feed" -that is, performing local intensive exploration (fixational eye movements) of the current patch of the attentional landscape -, or "fly", by making an extensive relocation (saccade) toward a new patch.…”

Section: Introductionmentioning

confidence: 99%

Modelling Task-Dependent Eye Guidance to Objects in Pictures

et al. 2014

Self Cite

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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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Ecological Sampling of Gaze Shifts

Cited by 29 publications

References 60 publications

Influence of local scene color on fixation position in visual search

Influence of local scene color on fixation position in visual search

Eye movements and information geometry

Modelling Task-Dependent Eye Guidance to Objects in Pictures

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