Fig. 5: Latent neural representation of ambivalent feelings and its predictive power on AWE-S ratings.
From: Awe is characterized as an ambivalent affect in the human behavior and cortex
a silhouette coefficients of ambivalent feelings’ cluster in each participant-clip’s latent neural-feeling spaces. Color intensity indicates the statistical significance of the silhouette coefficients. b concept of ‘cortical distinctiveness’ for the reference cluster k, \({\phi }_{{{\rm{k}}}}\). cortical distinctiveness is defined as the average of cosine distances between the reference cluster and the others. In the equation, N is the number of the other clusters, ci is the i-th cluster, and dcos is a cosine distance. As a sensitivity check, we calculated dcos by average and medoid distance indices. c explanatory power of each valence cluster’s cortical distinctiveness value in the linear mixed-effects models. Error bar denotes 95% confidence interval of fixed effects’ estimates. Purple bars show the estimates of ambivalence-related features. Bolded statistics indicate statistically significant results at P < 0.05. d performance gain observed when cortical distinctiveness of ambivalent feelings was added to the behavioral prediction model. e variable importance value of cortical distinctiveness of ambivalent feelings and other behavioral features in the GBM-based prediction. Importance value was scaled from 0 to 1 by the value of cortical distinctiveness of ambivalent feelings. Purple bars show the importance of ambivalence-related features. Results are based on 27 participants’ data in three awe-inducing trials.
