A new approach to modeling the influence of image features on fixation selection in scenes.
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.
Affiliation: Psychology Department, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, United Kingdom.Show MeSH
Related in: MedlinePlus
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.
Affiliation: Psychology Department, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, United Kingdom.