Bi, W., Lin, Q., Peng, K., Shah, A. D., & Yildirim, I. Computational modeling reveals dissociable causal and statistical object representations in the human brain during spontaneous visual processing.

Using computational modeling and fMRI, and taking soft objects as a case study, we found evidence for a double dissociation between statistical-based and causality-based representations during spontaneous visual object processing.

Bi, W., Shah, A. D., Wong, K. W., Scholl, B. J., & Yildirim, I. (2025). Computational models reveal that intuitive physics underlies visual processing of soft objects. Nature Communications, 16(1), 6303.

Using computational modeling and psychophysics, we found that perceiving soft objects goes beyond extracting statistical features from sensory inputs; it relies on physics-based representations—constructing and manipulating internal models that explain the visual input in terms of the world’s underlying physical dynamics.

Erdogan, M., Bi, W., Yildirim, I., & Scholl, B. J. (2025). Dynamic point-light cloths generate rich percepts beyond biology. Current Biology.

Even highly sparse stimuli can produce rich percepts of biological motion, which has often been taken to imply a special form of “social perception.” In this work, we show that similar phenomena arise with sparse animations of cloth waving in the wind, suggesting that biological motion is just one instance of a more general visual process.

Wong, K. W., Bi, W., Soltani, A. A., Yildirim, I., & Scholl, B. J. (2023). Seeing soft materials draped over objects: A case study of intuitive physics in perception, attention, and memory. Psychological Science, 34(1), 111-119.

We found that when viewing objects draped with cloth, participants spontaneously inferred the hidden object’s shape by integrating subtle physical interactions among the cloth, gravity, and the unseen geometry. This shows how the seemingly simple act of perceiving (and attending, and remembering) cloth-covered objects involves a surprisingly elaborate analysis of “intuitive physics.”

Xiao, B., Zhao, S., Gkioulekas, I., Bi, W., & Bala, K. (2020). Effect of geometric sharpness on translucent material perception. Journal of vision, 20(7), 10-10.

Using simulated relief objects made of translucent materials with different shapes and optical properties under varied illuminations, we find that the degree of geometric sharpness significantly influences observers' perceived translucency.

Bi, W., Jin, P., Nienborg, H., & Xiao, B. (2019). Manipulating patterns of dynamic deformation elicits the impression of cloth with varying stiffness. Journal of vision, 19(5), 18-18.

We introduced "velocity coherence" and showed that manipulating it — without altering the underlying cloth dynamics — can causally influence perceived cloth stiffness.

Bi, W., Newport, J., & Xiao, B. (2018, August). Interaction between static visual cues and force-feedback on the perception of mass of virtual objects. In Proceedings of the 15th acm symposium on applied perception (pp. 12:1-5). ACM.  Project Page

Using a force-feedback device (Phantom) in conjunction with a game engine (Unity), we examined how material appearance affects the perception of object mass in an AR environment. We find that static visual appearance influences perceived mass, and that this effect runs opposite to the classical “material weight illusion.”

Bi, W., Jin, P., Nienborg, H., & Xiao, B. (2018). Estimating mechanical properties of cloth from videos using dense motion trajectories: Human psychophysics and machine learning. Journal of vision, 18(5), 12-12.

We find that long-range spatiotemporal information across video frames is essential for human estimation of cloth stiffness. A machine learning model trained on dense motion-trajectory features reliably predicts human perceptual judgments.

Bi, W., & Xiao, B. (2016). Perceptual constancy of mechanical properties of cloth under variation of external forces. In Proceedings of the ACM symposium on applied perception (pp. 19-23). ACM.

We investigate how humans maintain perceptual constancy when judging the mechanical properties of cloth across changes in external forces. We interpret the resulting perceptual constancy in terms of the statistical regularities present in optical flow.

Xiao, B., Bi, W., Jia, X., Wei, H., & Adelson, E. H. (2016). Can you see what you feel? Color and folding properties affect visual–tactile material discrimination of fabrics. Journal of vision, 16(3), 34-34.

Using fabrics as stimuli, we measure how observers match what they see (photographs of fabric samples) with what they feel (physical fabric samples).