Proceedings of the Annual Meeting of the Cognitive Science Society

The ability to detect and analyze a human figure is key to our survival and social interactions. Efficient and robust identification of body parts can help to interpret images when bodies are partially occluded. While previous studies emphasized the configural processing of whole bodies using simplified stimuli, it still remains unclear how spatial contexts about body parts are integrated to resolve ambiguities (e.g., from occlusion) regarding the identities of or spatial relations among body parts. In a series of online experiments, we asked human observers to identify an ambiguous target body part in the presence of another “context” part. Our results showed that humans can use various amounts of spatial context to discount local ambiguities in natural images of pairs of parts, and are sensitive to low- and mid-level cues such as alignment and connectedness. Further simulations using deep convolutional neural networks (DCNNs) exhibited comparable similar sensitivity to spatial context variations, despite being trained solely on local part appearances without explicit prior knowledge of body structure. However, discrepancies between human and model performance were also observed, with humans showing greater sensitivity to spatial relations compared to the models. Our findings suggest that while both humans and models utilize low- and mid-level features for body part recognition, humans possess a stronger prior knowledge of body structure that in- fluences their perception. These results contribute to our understanding of how humans integrate spatial context to resolve ambiguities and provide insights into the computational mechanisms underlying body perception.