Fig. 4: Optimal dimensionality and decoding performances of latent neural-feeling spaces.
From: Awe is characterized as an ambivalent affect in the human behavior and cortex
a dimensionality selection for CEBRA-driven latent neural-feeling spaces. Hierarchical clustering analysis identified two optimal clusters in terms of decoding performances in the aligned condition. The purple plots indicate dimensionalities with greater performances compared to other ones. b decoding performance of latent embeddings derived from CEBRA, PCA, and FAA across three conditions. Black asterisks show decoding performance differences between analytic frameworks in each condition, while purple asterisks indicate how CEBRA embeddings perform differently across conditions. Each error bar represents 95% confidence intervals of weighted F1 scores from pairwise decoding analysis. For ‘across participants’ tasks, total 90 decoding scores (i.e., all possible participant pairings × number of clips) were used, while for ‘across clips’ tasks, total 36 scores (i.e., all possible clip pairings × number of participants) were included, as shown in the heatmap on the right of each plot. All statistical differences were tested through post-hoc test of linear mixed models including condition or embedding type variable as a regressor. Results are based on six participants’ data in three awe-inducing trials. *PFDR < 0.05; **PFDR < 0.01; ***PFDR < 0.001.
