Predicting Affective Responses based on Physiological Data using Regression-based Decoding

Physiological responses have been regarded as better measures of arousal than valence of core affect dimensions. Recently, valence was explained by two valence hypotheses (bipolarity and bivalent), represented as signed and unsigned valence. In this paper, with reference to two valence models and an arousal dimension, we explored whether affective representations can be identified based on physiological responses across participants. By re-analyzing a shared dataset that includes physiological responses and behavioral ratings of affect, we performed a regression-based decoding to predict affective representations of participants based on their physiological responses. Additionally, a one-way repeated measures ANOVA with a trend analysis was conducted to compare the accuracies of two valence models and the arousal dimension. The results revealed that all predictions were significant, indicating that physiological responses can identify valence and arousal. A trend analysis showed that arousal was predicted more accurately than two valence models, suggesting that physiological responses capture arousal better than valence.