Neurally-constrained modeling of human gaze strategies in a change blindness task
Fig 5
Effect of model parameters on change detection success.
A. Change in model performance (success rates, % correct) with varying the relative interval of the images and blanks, measured in units of time bins (Δt = 25 ms/time bin; Table 1), while keeping the total image+blank interval constant (at 50 time bins). Positive values on the x-axis denote larger image intervals, as compared to blanks, and vice versa, for negative values. Blue points: Data; gray curve: sigmoid fit. B. Same as in panel (A), but with varying the maximum decay factor (γ; Eq 2). Curves: Sigmoid fits. C. Same as in panel (A) but with varying the firing rate prior (μf) for image pairs with the lowest (blue; bottom 33rd percentile) and highest (red; top 33rd percentile) magnitudes of firing rate changes. Curves: Smoothing spline fits. Colored squares: μf corresponding to the center of area of the two curves. D. Same as in panel (A), but with varying the mean fixation duration (μFD; measured in time bins, Δt = 25 ms/time bin). (Inset; lower) Variation of μFD with prior ratio of change to no change (P(C:NC)). (Inset; upper) Same as lower inset but with varying threshold decay rate ζ (Table 1). E. Same as in panel (A), but with varying saccade amplitude variance (σ2SA). (Inset) Variation of σ2 SA with the softmax function temperature parameter (T) (see text for details). F. Same as in panel (A), but with varying saccade amplitude variance (σ2SA). (Inset) Variation of σ2 SA with the foveal magnification factor (FMF). Other conventions in B-F are the same as in panel A. Error bars (all panels): s.e.m.