Expectation generation and its effect on subsequent pain and visual perception
Fig 3
Computational model of cue-based expectation generation.
(A) Simulations of the expectation model: Mapping of cue values, V (10 per cue), to weights for expectation computation, based on the two free parameters of the model: k and b. When k = 1 (black line), inliers and outliers are equally weighted. When k < 1 (cold colors), inliers are over-weighted, and when k > 1 (hot colors), outliers are over-weighted. When b < 0 (left panels), values below the mean are over-weighted, when b > 0 (right panels) values above the mean are over-weighted, and when b = 0 (middle panel), values are equally weighted. The dashed line represents the cue mean. (B) Correlations between the observed and predicted (based on the computational model) expectation ratings were very high across participants. Each line represents a single participant, and each dot represents a single trial. Data from different participants are presented in different colors. (C) The weight function for each modality, based on the median k and b values across the group. Weight functions of individual participants are overlaid with thin, gray lines. The dashed line represents equal rating of all cue values (V). Note that panels A and C are based on a symmetric cue consisting of equally distributed values between 20 and 60.