Objective: In this rapidly digitizing world, it is becoming ever more important to understand people's online behaviors in both scientific and consumer research settings. The current work tests the feasibility of inferring personality traits from mouse movement patterns as a cost-effective means of measuring individual characteristics.
Method: Mouse movement features (i.e., pauses, fixations, speed, and clicks) were collected while participants (N = 791) completed an online image choice task. We compare the results of standard univariate and three forms of multivariate partial least squares (PLS) analyses predicting Big Five traits from mouse movements. We also examine whether mouse movements can predict a proposed measure of task attentiveness (atypical responding), and how these might be related to personality traits.
Results: Each of the PLS analyses showed significant associations between a linear combination of personality traits (high Conscientiousness, Agreeableness, Openness, and low Neuroticism) and several mouse movements associated with slower, more deliberate responding (less unnecessary clicks and more fixations). Additionally, several click-related mouse features were associated with atypical responding on the task.
Conclusions: As the image choice task itself is not intended to assess personality in any way, our results validate the feasibility of using mouse movements to infer internal traits across experimental contexts.
Keywords: Big Five; PLS; mouse movements; multiverse; personality.
© 2022 The Authors. Journal of Personality published by Wiley Periodicals LLC.