From: Rat performance on visual detection task modeled with divisive normalization and adaptive decision thresholds Journal of Vision. 2011;11(9):1. doi:10.1167/11.9.1 Figure Legend: Various decision criteria. (a) The best fit model (k T, k F, γ, α = 0) assuming that the subject only chooses a single fixed decision criterion for all stimuli (α = 0, see Equation 7). The blue vertical line indicates the results of a pure change in target contrast. Other black lines indicate the influence of target contrast when the flanker contrast is higher. The red curve represents a change in the flanker contrast for a target contrast of 1. Other black curves represent pure changes in flanker contrast if the target contrast is lower. If the data perfectly fit the model, the intersections of the lines would match the observed data. Blue crosses from Figure 1a would fall on the blue line, and red crosses from Figure 2b would fall on the red line. Gray crosses indicate all possible combinations of the target and flanker contrasts (see Figure 1c) and should be located at the intersection of the black lines. The gray contour indicates d′ for each model's best fit to the high-contrast condition; the curve spans all possible decision criterion thresholds. The signal and noise distributions of the model are displayed for three representative stimuli: (b) a low-contrast target alone, (c) a high-contrast target alone, and (d) a high-contrast target with a high-contrast flanker. (e) The best fit model (k T, k F, γ, α = 1) if the subject chooses the optimal decision threshold for each stimulus condition (α = 1). Notice that allowing for the optimal choice for any symmetric distribution results in data that fall on a single diagonal line with a slope of −1 that extends from pure chance behavior to perfect performance. The dots along the line correspond to the model's prediction of performance for the four target contrasts. Each dot represents five overlapping conditions because the model predicts no effect of flankers. However, the data from the rat do not fall on a line; they are spread over a plane. This model is clearly wrong. The poor fit is reflected in the substantial rise in the Bayesian information criterion (BIC). The decision criteria are presented in (f)–(h) using the same stimulus conditions as before. Note that the entire model is refit, and so the signal and noise distributions may vary slightly as well. (i) The best fit model (k T, k F, γ, α) assuming that the subject's decision criterion is the weighted average between a single criterion (a = 0) and the optimal criterion for that stimulus condition (α = 1). The best relative weight (α) is fit to the model. The value of α = 0.79 indicates that the decision criterion is close to the optimal but ∼20% influenced by a global criterion that is modeled as the average of criterion across all conditions. Date of download: 12/23/2017 The Association for Research in Vision and Ophthalmology Copyright © 2017. All rights reserved.