Neurally-constrained modeling of human gaze strategies in a change blindness task
Fig 3
Saccade probabilities and fixated features are similar across good and poor performers.
A. Average saccade probability matrices for the good performers (top; red outline) and poor performers (bottom; blue outline). These correspond to probabilities of making a saccade between different “domains” (1–4), each corresponding to a (non-contiguous) collection of image regions, ordered by frequency of fixations: most fixated regions (domain 1) to least fixated regions (domain 4). Cell (I, j) (row, column) of each matrix indicates the probability of saccades from domain j to domain i. B. Classification accuracy for classifying good versus poor performers based on the saccade probability matrix features, using a support vector machine classifier. Other conventions are as in Fig 1D. Error bars: s.e.m. C. Identifying low-level fixated features across good and poor performers. 112x112 image patches were extracted, centered around each fixation, for each participant; each point in the 112x112 dimensional space represents one such image patch. Principal component analysis (PCA) was performed to identify low-level spatial features explaining maximum variance among the fixated image patches, separately for good and poor performers. D. Top 6 principal components, ranked by proportion of variance explained, corresponding to spatial features explaining greatest variance explained across fixations, for good performers (left panels) and poor performers (right panels). These spatial features were highly correlated across good and poor performers (median r = 0.20, p<0.001, across n = 150 components).