Limits...
A new approach to modeling the influence of image features on fixation selection in scenes.

Nuthmann A, Einhäuser W - Ann. N. Y. Acad. Sci. (2015)

Bottom Line: Which image characteristics predict where people fixate when memorizing natural images?To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs).Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors.

View Article: PubMed Central - PubMed

Affiliation: Psychology Department, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, United Kingdom.

Show MeSH

Related in: MedlinePlus

Central bias analysis. (A) Image grid with vectors (in red) connecting the center of the grid cell with the center of the image. (B) Assignment of the resulting eight distinct distance categories to image grid cells. Absolute distance is color-coded such that the color of more distant cells becomes progressively brighter. (C) Frequency of occurrence of categorical distances. (D) Mean fixation probability as a function of distance from scene center. Error bars are 95% binomial proportion confidence intervals, obtained using the score confidence interval.51 In panels (C) and (D) the spacing on the x-axis preserves relative distances between distance categories.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4402003&req=5

fig02: Central bias analysis. (A) Image grid with vectors (in red) connecting the center of the grid cell with the center of the image. (B) Assignment of the resulting eight distinct distance categories to image grid cells. Absolute distance is color-coded such that the color of more distant cells becomes progressively brighter. (C) Frequency of occurrence of categorical distances. (D) Mean fixation probability as a function of distance from scene center. Error bars are 95% binomial proportion confidence intervals, obtained using the score confidence interval.51 In panels (C) and (D) the spacing on the x-axis preserves relative distances between distance categories.

Mentions: To explicitly model the central bias of fixation in the GLMM framework, a central-bias predictor was created as follows. For each cell of the image grid, the distance between the center of the grid cell and the center of the image was determined (red vectors in Fig.2A). This resulted in eight distinct distance categories; each of them comprised either four or eight cells (Fig.2C). By definition of the grid, these categories are not equidistant. In Figure2B image grid cells are numbered according to the distance category they belong to (from 1 = proximal to 8 = distal), while absolute distance is color-coded such that the color of more distant cells becomes progressively brighter. Statistical models included the central-bias predictor as distance from scene center in degrees of visual angle.


A new approach to modeling the influence of image features on fixation selection in scenes.

Nuthmann A, Einhäuser W - Ann. N. Y. Acad. Sci. (2015)

Central bias analysis. (A) Image grid with vectors (in red) connecting the center of the grid cell with the center of the image. (B) Assignment of the resulting eight distinct distance categories to image grid cells. Absolute distance is color-coded such that the color of more distant cells becomes progressively brighter. (C) Frequency of occurrence of categorical distances. (D) Mean fixation probability as a function of distance from scene center. Error bars are 95% binomial proportion confidence intervals, obtained using the score confidence interval.51 In panels (C) and (D) the spacing on the x-axis preserves relative distances between distance categories.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402003&req=5

fig02: Central bias analysis. (A) Image grid with vectors (in red) connecting the center of the grid cell with the center of the image. (B) Assignment of the resulting eight distinct distance categories to image grid cells. Absolute distance is color-coded such that the color of more distant cells becomes progressively brighter. (C) Frequency of occurrence of categorical distances. (D) Mean fixation probability as a function of distance from scene center. Error bars are 95% binomial proportion confidence intervals, obtained using the score confidence interval.51 In panels (C) and (D) the spacing on the x-axis preserves relative distances between distance categories.
Mentions: To explicitly model the central bias of fixation in the GLMM framework, a central-bias predictor was created as follows. For each cell of the image grid, the distance between the center of the grid cell and the center of the image was determined (red vectors in Fig.2A). This resulted in eight distinct distance categories; each of them comprised either four or eight cells (Fig.2C). By definition of the grid, these categories are not equidistant. In Figure2B image grid cells are numbered according to the distance category they belong to (from 1 = proximal to 8 = distal), while absolute distance is color-coded such that the color of more distant cells becomes progressively brighter. Statistical models included the central-bias predictor as distance from scene center in degrees of visual angle.

Bottom Line: Which image characteristics predict where people fixate when memorizing natural images?To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs).Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors.

View Article: PubMed Central - PubMed

Affiliation: Psychology Department, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, United Kingdom.

Show MeSH
Related in: MedlinePlus