Fig 4.

Social harm avoidance monitoring experiment (SHAME). (a) Whole brain map depicting regions implicated in painful outcomes of one's errors (one's–others’ painful errors). (b) Linear regression of CI rate. Surface rendering of a human brain highlighting suprathreshold coordinates in which neural responses to one's painful errors explained nurses' CI rate in univariate linear regression. Three subplots (i–iii) are also displayed. Subplot (i) describes the linear relation between CI rate and the average parameter extracted by the anterior middle cingulate cortex (grey area refers to the 95% confidence interval). Subplots (ii) and (iii) refer to data from multivariate pattern analysis (MVPA) (colour-coded according to the machine-learning algorithm used). On the top, the overall proficiency of least absolute shrinkage and selection operator (LASSO) and random forest (RF) classifiers for prediction of the three clinical measures of interest. White circles refer to mean square error (MSE) associated with out-of-subject predictions, superimposed with violin-plots of the permutation-based null distribution of MSE. Subplot (iii) describes the linear regression between nurses' CI rate and the value predicted by each of the two classifiers. aMCC, anterior middle cingulate cortex; CI, contraindication; Doc., documentation; MFG, middle frontal gyrus; Treat. App., treatment application. *P<0.001; ^‡P<0.05 associated with standard parametric analysis (for linear regressions) and permutation-based analysis (for MVPA).