Figure 1.

Experimental design. A, Trial design. Following a variable intertrial interval, participants received an electrical target pulse at 1 of 10 intensity levels, which they either detected or missed. At the same time, the fixation disk changed its brightness to serve as the visual matching cue, which signaled target presence (white) or target absence (dark gray). In the matching task, participants compared their somatosensory percept to the matching cue and decided whether the two modalities matched or not (left box). In the direct report task, participants ignored the identity of the matching cue and merely decided whether they had detected a target pulse or not (right box). After a brief delay, they reported their decision by making a saccade to one of two peripherally presented, color-coded response cues. The selected cue briefly increased in size, signaling that the response was logged, and the next trial began. B, Experimental regressors. EEG responses were modeled with seven different GLMs that were compared using BMS. Each experimental GLM contained an intercept regressor and one of six experimental regressors modeling stimulus intensity, detection probability, target detection (hit vs miss), expected uncertainty, matching reports (match vs mismatch), and matching cues (white vs dark gray). An additional null model contained the intercept regressor only. Small black circles represent individual trials of 1 participant. Please note that although the detection probability regressor is computed from the detection regressor, the two models differ in their behavior within intensity levels: for example, looking at the predictions for trials at intensity level 5, the detection probability model predicts the same activation level for all targets of that intensity level, regardless of whether they were detected or missed, whereas the detection model predicts categorically higher activity for detected than missed trials. Thus, the detection model assumes a nonlinear, all-or-none response for detected stimuli, whereas the detection probability model assumes a graded response. Further note the intensity-biased distribution of trials in the detection regressor, which leads to correlations between models and prohibits classical GLM analysis.