Social Perception and Interaction Database—A Novel Tool to Study Social Cognitive Processes With Point-Light Displays

Introduction: The ability to detect and interpret social interactions (SI) is one of the crucial skills enabling people to operate in the social world. Multiple lines of evidence converge to indicate the preferential processing of SI when compared to the individual actions of multiple agents, even if the actions were visually degraded to minimalistic point-light displays (PLDs). Here, we present a novel PLD dataset (Social Perception and Interaction Database; SoPID) that may be used for studying multiple levels of social information processing. Methods: During a motion-capture session, two pairs of actors were asked to perform a wide range of 3-second actions, including: (1) neutral, gesture-based communicative interactions (COM); (2) emotional exchanges (Happy/Angry); (3) synchronous interactive physical activity of actors (SYNC); and (4) independent actions of agents, either object-related (ORA) or non-object related (NORA). An interface that allows single/dyadic PLD stimuli to be presented from either the second person (action aimed toward the viewer) or third person (observation of actions presented toward other agents) perspective was implemented on the basis on the recorded actions. Two validation studies (each with 20 healthy individuals) were then performed to establish the recognizability of the SoPID vignettes. Results: The first study showed a ceiling level accuracy for discrimination of communicative vs. individual actions (93% ± 5%) and high accuracy for interpreting specific types of actions (85 ± 4%) from the SoPID. In the second study, a robust effect of scrambling on the recognizability of SoPID stimuli was observed in an independent sample of healthy individuals. Discussion: These results suggest that the SoPID may be effectively used to examine processes associated with communicative interactions and intentions processing. The database can be accessed via the Open Science Framework (https://osf.io/dcht8/).