Mapping social relevance in affective scenes: A large-scale multidimensional rating study

Understanding how individuals evaluate social content in affective scenes is crucial for research in emotion, social cognition, and decision-making. However, standardized image databases often lack fine-grained ratings of social dimensions beyond basic emotional content. We present a normative dataset of 296 affective scenes rated by 424 adult participants across six social-affective dimensions: social relevance, emotion sharing, action sharing, interpersonal equality, scene pleasantness, and participatory arousal. Each image was independently coded for objective features such as number of people, face visibility, and gaze direction. All rating dimensions showed excellent interrater reliability. Pleasantness ratings demonstrated strong convergent validity with established valence norms from the International Affective Picture System (IAPS), while arousal ratings differentiated between experienced and participatory arousal, suggesting that the latter captures a distinct affective component. Correlation analyses revealed that social relevance was strongly associated with scene pleasantness and a composite "engagement" factor combining emotion sharing, action sharing, and equality. Notably, the association between pleasantness and social relevance was especially pronounced in scenes depicting single individuals. Linear mixed models further indicated that extraversion showed a tentative positive association with the engagement-social relevance relationship, which did not survive correction for multiple comparisons and requires replication. Other personality traits and empathy did not significantly moderate these relationships. This open-access dataset provides a reliable, multidimensional tool for selecting affective scenes based on both emotional and social-interpersonal features. It supports improved stimulus control and design in a wide range of behavioral and neurocognitive research. By systematically mapping social meaning in static affective scenes, our study advances existing methodological resources and offers empirical insight into the structure of social-affective appraisals. All image codes, mean ratings, and analysis scripts are publicly available via the Open Science Framework.