Putting people in context: ERP responses to bodies in natural scenes

The N190 is a body-sensitive ERP component that responds to images of human bodies in different poses. In natural settings, bodies vary in posture and appear within complex, cluttered environments, frequently with other people. In many studies, however, such variability is absent. How does the N190 response change when observers see images that incorporate these sources of variability? In two experiments (N = 16 each), we varied the natural appearance of upright and inverted bodies to examine how the N190 amplitude, latency, and the Body-Inversion Effect (BIE) were affected by natural variability. In Experiment 1, we varied the number of people present in upright and inverted naturalistic scenes such that only one body, a subitizable number of bodies, or a “crowd” was present. In Experiment 2, we varied the natural body appearance by presenting bodies either as silhouettes or with photographic detail. Further, we varied the natural background appearance by either removing it or presenting individual bodies within a rich environment. Using component-based analyses of the N190, we found that the number of bodies in a scene reduced the N190 amplitude, but didn’t affect the BIE (Experiment 1). Naturalistic body and background appearance (Experiment 2) also affected the N190, such that component amplitude was dramatically reduced by naturalistic appearance. To complement this analysis, we examined the contribution of spatiotemporal features (i.e., electrode × time point amplitude) via SVM decoding. This technique allows us to examine which timepoints across the entire waveform contribute the most to successful decoding of body orientation in each condition. This analysis revealed that later timepoints (after 300ms) contribute most to successful orientation decoding. These results demonstrate that natural appearance variability affects body processing at the N190 and that later ERP components may make important contributions to body processing in natural scenes.