Research Interests:

Our research focuses on two schemes. (1) The neural machenisms of perceptual learning and decision. We investigate the plasticity of visual cortex and higher level brain regions during perceptual learning. We aim to explore the interaction between learning and decision-making processes in the human brain. (2) The modulation effect of reward learning on perception, attention, memory, emotion, and executive control, and its influence on human behavior. We combine behavioral measurement, eye tracking, brain imaging techniques (fMRI, EEG, TMS), and computational models in our investigations.

Selected Publications:

1. Li, T., Wang, X., Pan,J., Feng, S., Gong, M., Wu, Y., Li, G., Li, S.*, & Yi, L.* (2017) Reward learning modulates the attentional processing of faces in children with and without autism spectrum disorder. Autism Research (in press) (*co-corresponding authors)

2. Gong, M.*, Jia, K.*, & Li, S. (2017). Perceptual competition promotes suppression of reward salience in behavioral selection and neural representation. Journal of Neuroscience (in press) (*co-first authors)

3. Jia, K., & Li, S. (2017). Motion direction discrimination training reduces perceived motion repulsion. Attention, Perception, & Psychophysics, 79:878–887.

4. Gong, M., Yang, F., & Li, S. (2016). Reward association facilitates distractor suppression in human visual search. European Journal of Neuroscience, 43:942-953.

5. Li, Y., & Li, S. (2015). Contour integration, attentional cuing, and conscious awareness: An investigation on the processing of collinear and orthogonal contours. Journal of Vision, 15(16):10, 1–16.

6. Xue, X., Zhou, X., & Li, S. (2015). Unconscious reward facilitates motion perceptual learning. Visual Cognition, 23(1-2), 161-178.

7. Yang, F., Wu, Q., & Li, S. (2014). Learning-induced uncertainty reduction in perceptual decisions is task-dependent. Frontiers in Human Neuroscience, 8, 282.

8. Gong, M., & Li, S. (2014). Learned reward association improves visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 40(2): 841-856.

9. Mu, T., & Li, S. (2013). The neural signature of spatial frequency-based information integration in scene perception. Experimental Brain Research, 227(3), 367-377.

10. Li, S., & Yang, F. (2012). Task‐dependent uncertainty modulation of perceptual decisions in the human brain. European Journal of Neuroscience, 36(12), 3732-3739.

11. Li, S.*, Mayhew, S. D.*, & Kourtzi, Z. (2012). Learning shapes spatiotemporal brain patterns for flexible categorical decisions. Cerebral Cortex, 22(10), 2322-2335.(*co-first authors)

12. Mayhew, S. D.*, Li, S.*, & Kourtzi, Z. (2012). Learning acts on distinct processes for visual form perception in the human brain. Journal of Neuroscience, 32(3), 775-786.(†co-first authors)

13. Chen, D.*, Li, S.*, Kourtzi, Z., & Wu, S. (2010). Behavior-constrained support vector machines for fMRI data analysis. IEEE Transactions on Neural Networks, 21(10), 1680-1685.(*co-first authors)

14. Li, S., Mayhew, S. D., & Kourtzi, Z. (2009). Learning shapes the representation of behavioral choice in the human brain. Neuron, 62(3), 441-452.

15. Li, S., Ostwald, D., Giese, M., & Kourtzi, Z. (2007). Flexible coding for categorical decisions in the human brain. Journal of Neuroscience, 27(45), 12321-12330.

16. Li, S., & Wu, S. (2007). Robustness of neural codes and its implication on natural image processing. Cognitive Neurodynamics, 1(3), 261-272.

17. Li, S., & Wu, S. (2005). On the variability of cortical neural responses: a statistical interpretation. Neurocomputing, 65, 409-414.

Lab Website: