Fig. 1: Overview of methods.
From: Awe is characterized as an ambivalent affect in the human behavior and cortex
a sampling flow. b experimental framework. c conceptual scheme of CEBRA-based learning of latent neural-feeling spaces. CEBRA performs nonlinear dimensionality reduction using contrastive learning, which clusters EEG signals from the same valence types as closely as possible while maximizing the separation between signals from different valences. This method is applied to each individual’s EEG data and key-pressed valence sequences for each video to train a personalized latent neural-feeling space. In the ‘aligned’ condition of the pairwise decoding task, canonical correlation analysis (CCA) is applied to the embeddings of two individuals (or clips) to align their latent embeddings and test the shared representational structure. d framework of pairwise decoding tasks – ‘across participants’ and ‘across clips’.
