Figure 5.

Association of the Bayesian surprise in the dACC with expectation of response and stimulus outcomes. Statistical probability maps are obtained with p < 0.0001, uncorrected, and are overlaid on structural images in sagittal, coronal, and axial sections. A, GLM1 was used to model Bayesian surprise or UPE related to P(stop); four categorical types of trials were distinguished according to trial type and outcome: GS, GE, SS, and SE trials. The probability of stop and error trials as estimated by the Bayesian model, or P(stop) and P(error), respectively, and the RT of GS trials were entered as parametric modulators in the model. B, GLM3 was used to isolate activations related to prediction errors associated with response and stimulus outcomes (please see Materials and Methods for details); a single categorical regressor comprised stimulus onset on all trials. Associated with this main regressor were four parametric modulators: Bayesian surprise or UPE of stop (|stimulus outcome − P(stop)|), SPE of error (response outcome − P(error)), stimulus outcome/conflict (stop = 1, go = 0), and response outcome (error = 1, correct = 0), with subsequent modulators orthogonalized with respect to previous ones. Clusters from GLM3 (magenta, blue, and yellow colors) are mostly significant at p < 0.05, corrected.