Validation of out-of-sample inter-subject ensemble moment-to-moment estimates of predicted response outcome (PRO) within the dACC based upon neural activations falling outside the medial frontal cortex (mFC).

Scatterplots depict the group-level effects computed using linear mixed-effects models which model random effects subject-wise. Bold red lines depict group-level fixed-effects of the models’ predictions of the true PRO. Bold gray lines depict significant subject-level effects whereas light gray lines depict subject-level effects that were not significant. Valence. The fixed effect (R2 = .039) is significant (p0: β = 0). Random effects significantly improve effect-size (p0: observed responses generated by fixed-effects only). Arousal. The effect (R2 = .031) is significant (p0: β = 0). Random effects significantly improve effect-size (p0: observed responses generated by fixed-effects only). (TIF)</p

Facial electromyography sensitivity analysis in the prediction of normative valence.

(A) Valence prediction effect-size, measured as adjusted R2, as a function of the polar extremes of affectively valent stimuli used to construct the prediction, plotted separately for facial EMG signals recorded from the corrugator supercilii (blue) and zygomaticus major (red). Polar-extremity is reported as a factor of the standard deviation of the normative valence scores used to threshold stimuli for exclusion from the prediction. The symbols represent thresholds for which the plotted effect-size is statistically significant. (B) The fraction of the total number of image stimuli remaining in the set after thresholding. The symbol denotes the minimum threshold level for which the corrugator signal significantly predicted normative valence score of the remaining stimuli. (TIF)</p

Validation of inter-subject ensemble moment-to-moment estimates of expected value of control (EVC) within the dACC based upon neural activations falling outside the medial frontal cortex (mFC) and selection of optimal EVC parameters.

Q Performance depicts the median action-value advantage of on-policy control versus a random policy. Policy Error depicts median squared error between the on-policy action and the optimal action. Gray cells depict the cells selected as the parameters for this experiment (see Main Manuscript Methods: Control Performance Evaluation Monitoring). Note, all parameter combinations in the Q Performance represent significant action-value advantages for on-policy control (p0: μ1-μ2 = 0). Valence. Selected parameters: discount factor, γ = 0.9; fraction of action, fa = 0.2. Arousal. Selected parameters: discount factor, γ = 1.0; fraction of action, fa = 0.2. (DOCX)</p

Implicit induction stimuli class counts.

(DOCX)</p

Figure data, models, and activation maps.

Raw data files, Matlab formatted general linear model binary files, and NIFTI formatted neuroimaging files needed to reconstruct the main effects displayed within each figure of the main manuscript as well as each supporting figure. (ZIP)</p

Neural encodings of affect processing.

Color gradations indicate the group-level t-scores of the encoding parameters (red indicating positive valence or high arousal, blue indicating negative valence or low arousal). T-scores are presented only for those voxels in which encoding parameters survived global permutation testing (p (TIF)</p

Comparison of support vector machine predictions based upon whole-brain gray matter versus Gram-Schmidt reduced dimensionality features.

Gram-Schmidt dimensionality reduction projects the original whole-brain gray matter features (n~30,000–40,000) onto an orthogonal basis in which the coordinate dimension is less than or equal to the number of sample features (n≤90). We report the effect size of the reduced dimensional predictions in explaining predictions in the original feature space using a linear mixed-effects model in which random effects are modeled subject-wise. Gray symbols depict individual trials. The bold red line depicts the group-level effect. Bold gray lines depict significant subject-level effects whereas light gray lines depict subject-level effects that were not significant. Valence. The fixed effect (R2 = .71) is significant (p0: β = 0). Random effects significantly improve effect-size (p0: observed responses generated by fixed-effects only). Arousal. The fixed effect (R2 = .72) is significant (p0: β = 0). Random effects significantly improve effect-size (p0: observed responses generated by fixed-effects only). (TIF)</p

Validation of psychophysiological measures as predictors of normative scores of the implicit induction stimulus set.

(A) Facial electromyography based prediction of normative valence scores of the stimulus set (thresholded .4σ, see S6 Fig). The group-level fixed effect (R2 = .0002) of zygomaticus major, zEMG, differences between pre- and post-stimulus rectified signals is not significant (p = .78; t-test; h0: β = 0). The group-level fixed effect (R2 = .0007) of corrugator supercilii, cEMG, is significant (p = .024; t-test; h0: β = 0). Random effects did not significantly improve effect-size (p0: observed responses generated by fixed-effects only). (B) Electrodermal activity based prediction of normative arousal scores of the full (i.e., unthresholded) stimulus set. The group-level fixed effect (R2 = .002) of the skin conductance response, SCR, beta-series is significant (p0: β = 0). Random effects did not significantly improve effect-size (p>0.05; likelihood ratio test; h0: observed responses generated by fixed-effects only). In both panels, gray symbols represent individual trials, bold gray lines depict significant subject-level effects, and light gray lines depict subject-level effects that were not significant. (TIF)</p

Clusters of age and sex related interactions with the performance monitoring model fixed effects separated by affect property.

CoM: Center of Mass. Direct access to these cluster maps is available via our Open Science Framework repository (see Main Manuscript: Source Code and Data Availability). (DOCX)</p