Descriptive Models of Emotion - Learning Useful Abstractions from Physiological Responses during Affective Interactions

Supervised recognition of emotions from physiological signals has been widely accomplished to measure affective interactions. Less attention is, however, placed upon learning descriptive models to characterize physiological responses. In this work we delve on why and how to learn discriminative, complete and usable descriptive models based on physiological signals from emotion-evocative stimuli. By satisfying these three properties, we guarantee that the target descriptors can be expressively adopted to understand the physiological behavior underlying multiple emotions. In particular, we explain why classification and unsupervised learning models do not address these properties, and point new directions on how to adapt existing learners to met them based on theoretical and empirical evidence.