Inferential affective tracking reveals the remarkable speed of context-based emotion perception

Abstract

Understanding the emotional states of others is important for social functioning. Recent studies show that context plays an essential role in emotion recognition. However, it remains unclear whether emotion inference from visual scene context is as efficient as emotion recognition from faces. Here, we measured the speed of context-based emotion perception, using Inferential Affective Tracking (IAT) with naturalistic and dynamic videos. Using cross-correlation analyses, we found that inferring affect based on visual context alone is just as fast as tracking affect with all available information including face and body. We further demonstrated that this approach has high precision and sensitivity to sub-second lags. Our results suggest that emotion recognition from dynamic contextual information might be automatic and immediate. Seemingly complex context-based emotion perception is far more efficient than previously assumed.

Keywords: Affect; Context; Emotion; Latency; Speed; Tracking.