Prior scene context reshapes feature reliance during rapid perception

Human perception is shaped by both sensory input and prior knowledge or expectations. But how does prior contextual information influence rapid visual processing? Here, we combined eye tracking with feature-based encoding models across two experiments to predict detection latencies in a core visual task: rapid face detection in natural scenes (N = 38 per experiment). In the first experiment, we manipulated the presence of faceless scene previews. In the second experiment, we additionally restricted peripheral visual input using a moving-window paradigm, thereby increasing reliance on prior information. Across both experiments, prior context facilitated face detection, particularly for challenging images. This facilitation was already evident in the very first eye movement, suggesting that previews shape perceptual strategies from the outset. To quantify what information guided behavior, we modeled detection latencies using a set of image-based predictors capturing (i) sensory information and (ii) a scene-derived spatial prior: the expected face location. Both predictor classes explained latency variation across images. Among sensory predictors, the difference in deep neural network responses induced by the presence of the face provided the strongest out-of-sample prediction of detection latency. Critically, when scene previews were available, the contribution of the spatial prior increased, while reliance on sensory-driven features was generally reduced. Together, these findings indicate that prior scene context shifts the balance of information used for rapid face detection from sensory-driven to expectation-based spatial guidance.