Ning Qian, PhD
The research in our laboratory focuses on computational and psychophysical studies of visual perception. Unlike machine vision approaches, we emphasize physiological plausibility of our models because such models have more explanatory and predictive power for understanding biological vision. We have been constructing binocular vision models by analyzing known spatiotemporal receptive-field properties of binocular cells in the visual cortex, and have been applying our models to explain depth perception from horizontal disparity (stereovision), vertical disparity (the induced effect), inter-ocular time delay (the Pulfrich effects), motion field (structure-from-motion), and monocular occlusion (da Vinci stereopsis). We also test new predictions from our models via visual psychophysical experiments.
A recent emphasis of our research is psychophysical investigation of faces. Face perception is essential for social interactions. While traditional face studies have primarily focused on high-level properties of face perception, we take a complementary approach by investigating contributions of low-level processing along multiple, interactive streams to face perception. We have been studying hierarchical face processing from low to high levels by measuring multi-level adaptation aftereffects. We also plan to conduct computational studies of faces.
Finally, we are interested in computational models of motor planning and sensorimotor integration. In particular, we would like to understand synergistic interactions between visual perception and motor control.
Li, Z. and Qian, N. (2015) Solving stereo transparency with an extended coarse-to-fine disparity energy model, Neural Computation, 27:1058-1082.
Qian, N., Jiang, Y., Jiang, Z. P., and Mazzoni, P. (2013) Movement duration, Fitts's law, and an infinite-horizon optimal feedback control model for biological motor systems. Neural Computation, 25, 697-724.
Xu, H., Liu, P., Dayan, P., and Qian, N. (2012) Multi-level visual adaptation: Dissociating curvature and facial-expression aftereffects produced by the same adapting stimuli. Vision Research, 72, 42-53.
Qian, N. and Lipkin, R. M. (2011) A Learning-Style Theory for Understanding Autistic Behaviors. Frontiers Human Neurosci., 5:77:1-17.
Wu, J., Xu, H., Dayan, P., and Qian, N. (2009) The role of background statistics in face adaptation. J. Neurosci., 2009, 29(39):12035-12044.
Qian, N., and Freeman, R.D. (2009) Pulfrich phenomena are coded effectively by a joint motion-disparity process. J Vision, 9(5):24, 1-16.
Xu, H., Dayan, P., Lipkin, R.M., and Qian, N. (2008). Adaptation across the cortical hierarchy: low-level curve adaptation affects high-level facial-expression judgments. J Neurosci 28, 3374-3383.
Assee, A., and Qian, N. (2007). Solving da Vinci stereopsis with depth-edge-selective V2 cells. Vision Res 47, 2585-2602.
Tanaka, H., Krakauer, J.W., and Qian, N. (2006). An optimization principle for determining movement duration. J Neurophysiol 95, 3875-3886.
Chen, Y., and Qian, N. (2004). A coarse-to-fine disparity energy model with both phase-shift and position-shift receptive field mechanisms. Neural Comput 16, 1545-1577.