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Contributed Talk Session: Friday, August 15, 12:00 – 1:00 pm, Room C1.04
Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall

Quantifying the Role of Perceived Curvature in the Processing of Natural Object Images

Laura Mai Stoinski¹, Diego Garcia Cerdas², Florian P. Mahner¹, Parsa Yousefi, Martin N Hebart³; ¹Max Planck Institute for Human cognition and brain sciences, Max-Planck Institute, ²University of Amsterdam, ³Justus Liebig Universität Gießen

Presenter: Laura Mai Stoinski

Curvature has been suggested as a fundamental organizational dimension of object responses. Despite its prominence, there is no consensus on how to define this measure for naturalistic object images. Here, we aimed to quantify the perceived curvature of natural images, clarify its relationship to spatial and temporal patterns of brain activity, and identify what features in an image contribute to perceived curvature. To address this, we collected extensive curvature ratings of 27,961 natural images and tested how they explain neural responses compared to computed curvature measures. Leveraging large-scale fMRI and MEG datasets, perceived curvature best explained broad occipitotemporal patterns in fMRI data and was decodable across an extended time period in MEG. To identify the object-features contributing to people’s perception of curvature, we used an image-generative approach based on deep neural networks, suggesting that people considered the curvature of more global object contours in their judgements. Given the apparent validity of perceived curvature, we offer an image-computable model to quantify perceived curvature for novel images. Together, our results highlight the importance of perceived curvature as a mid-level summary statistic and provide an approach for the automated quantification of perceived curvature in natural object images.

Topic Area: Object Recognition & Visual Attention

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