Network Analysis Improves Interpretation of Affective Physiological Data

Abstract

Understanding how human physiological responses to a stimulus vary across individuals is critical for the fields of Affective Psychophysiology and Affective Computing. We approach this problem via network analysis. By analyzing individuals' galvanic skin responses (GSRs) to a set of emotionally charged images, we model each image as a network, in which nodes are individuals and two individuals are linked if their GSRs to the given image are similar. In this context, we evaluate several network inference strategies. Then, we group (or cluster) images with similar network topologies, while evaluating a number of clustering choices. We compare the resulting network-based partitions against the known arousal/valence-based ``ground truth'' partition of the image set (which is likely noisy). While our network-based image partitions are statistically significantly similar to the ``ground truth'' partition (meaning that network analysis correctly captures the underlying signal in the data), the network-based partitions outperform the ``ground truth'' partition with respect to latent semantic analysis (meaning that our partitions are more semantically meaningful than the ``ground truth'' partition). Thus, network analysis of affective physiological data appears to improve interpretation of the data.