Lights, Camera, Emotion: REELMO’s 1060 Hours of Affective Reports to Explore Emotions in Naturalistic Contexts

Abstract

Emotions are central to human experience, yet their complexity and context-dependent nature challenge traditional laboratory studies. We present REELMO (REal-time EmotionaL responses to MOvies), a novel dataset bridging controlled experiments and naturalistic affective experiences. REELMO includes 1,060 hours of moment-by-moment emotional reports across 20 affective states collected during the viewing of 60 full-length movies, along with additional measures of personality traits, empathy, movie synopses, and overall liking from 161 participants. It also features fMRI data from 20 volunteers recorded while watching the full-length movie Jojo Rabbit. Complemented by visual and acoustic features as well as semantic content derived from deep-learning models, REELMO provides a comprehensive platform for advancing emotion research. Its high temporal resolution, rich annotations, and integration with fMRI data enable investigations into the interplay between sensory information, narrative structures, and contextual factors in shaping emotional experiences, as well as the study of affective chronometry, mixed-valence states, psychological trait influences, and machine learning applications in affective (neuro)science.