Physiological Indicators of The Relation Between Autistic Traits and Empathy: Evidence From Electrocardiogram and Skin Conductance Signals

Abstract

Difficulty in empathy is thought to be one of the problems in people with autism spectrum disorder (ASD), leading to impairment in social abilities and communication. However, despite the recent evidence on the effect of physiological bodily states on affective experiences, the exact role of physiological signals on different aspects of empathy (i.e., cognitive and affective empathy), as well as empathy dysfunction in ASD is yet unknown. To tackle this problem, in this study, 36 neurotypical subjects with different levels of autistic traits, participated in a well-established empathy for pain task, while Electrocardiogram (ECG) and Skin Conductance (SC) signals were recorded. Several features were extracted from each signal. Our results indicated that both cognitive and affective empathy are positively related to a higher level of cardiac activity (e.g., negative correlation with R-R interval) and arousal (e.g., positive correlation with average SC). More importantly, higher level of autistic traits, measured with Autism Quotient (AQ), was negatively correlated with Heart Rate Variability as measured with HRV-RMSSD and variability in tonic SC. Finally, we classified the participants into groups with high and low cognitive empathy, affective empathy, and level of autistic traits and investigated the extent to which machine learning approaches can automatically classify participants based on ECG and SC extracted features. Using a Support Vector Machine, reasonable results were obtained (in the range of. 73 to. 84), proving the possibility of implementing automatic detection systems for classifying subjects with different levels of autistic traits. Our results are suggestive of the effect of bodily simulation on empathy, and how the inability to regulate physiological signals leads to empathy dysfunction in individuals with high autistic traits.