Fig 4. A Bayesian model of gaze strategies for change detection.

A. Schematic showing a typical fixation across the pair of images (A, A’) and an intervening blank. B. Detailed steps for modeling change detection (see text for details). (Clockwise from top left) At each fixation, a Cartesian variable resolution (CVR) transform is applied to mimic foveal magnification, followed by a saliency map computation to determine firing rates at each location. Instantaneous evidence for change versus no change (log-likelihood ratio, log L(t)) is computed across all regions of the image. An inverse CVR transform is applied to project the evidence back into the original image space, where noisy evidence is accumulated, (sequential probability ratio test, drift-diffusion model). The next fixation point is chosen using a softmax function applied over the accumulated evidence (E_t). To model human saccadic biases, a distribution of saccade amplitudes and turn angles is imposed on the evidence values prior to selecting the next fixation location (polar plot inset). C. A representative gaze scan path following model simulation (cyan arrows). Colored squares: specific points of fixation (see panel D). Grid: Fine divisions over which the image was sub-divided to facilitate evidence computation. Green (1), blue (2) and red (3) squares denote first (beginning of simulation), intermediate (during simulation) and last (change detection) fixation points, respectively. D. Evidence accumulated as a function of time for the same three representative regions as in panel D; each color and number denotes evidence at the corresponding square in panel C. When the model fixated on the green or blue squares (in panel C), the accumulated evidence did not cross the threshold for change detection. As a result, the model continued to scan the image. When the model fixated on the red square (in the change region), the accumulated evidence crossed threshold (horizontal, dashed gray line) and the change was detected.