Reading Moods by Mouse-Cursor Tracking: Representational Similarity Analysis

Theories of Constructed Emotion and Grounded Cognition suggest that our sensorimotor experiences underpin the formation of emotions. This study explores this premise by examining how movements of a computer cursor can reflect moods of participants. We conducted an experiment where participants engaged in a simple choice-reaching task, with their mouse-cursor movements tracked pixel by pixel. Mood assessments were conducted using the PANAS-X scale before and after the task. Through Intersubject Representational Similarity Analysis, we investigated the correlation between the patterns of mouse movements and self-reported moods. Our findings reveal a significant association between negative emotions, such as fear and hostility, and certain movement patterns, e.g., randomness and deviations from a direct path. Furthermore, our machine learning-based Representational Similarity Analysis (ML-RSA) underscores the value of second-order similarity measures, revealing meaningful alignments between sensorimotor behaviors and emotional states across distinct measurement domains. These findings highlight the potential of cursor-tracking as a tool for exploring the interplay between emotion and action.