Quantifying Low-Level Motor Effects of Emotion: Validating Fitts' Law in Affective Contexts

Emotional states influence a wide range of cognitive processes, such as attention, memory, and decision-making, and play an increasingly recognized role in human-computer interaction (HCI). Although most prior research has focused on high-level effects of emotion, the impact of incidental emotional states on fine-grained motor behaviors such as pointing and selection remains understudied. Addressing this gap, the present study examines how affective priming with validated emotional stimuli modulates user performance in a Fitts’ Law-based pointing paradigm. By systematically varying task difficulty, we quantitatively assess the effects of emotion on core performance measures: movement time, reaction time, error rate, and throughput. Our findings demonstrate that emotion can modulate low-level motor interactions in digital environments, with important implications for the design of adaptive, emotion-aware interfaces. These results advance the theoretical understanding of the interplay between emotion, cognition, and motor control and offer actionable insights to develop more robust and personalized interactive systems.