Reading Moods by Mouse-Cursor Tracking: Representational Similarity Analysis

Abstract

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.