The goal of my research is to investigate, characterize, and model the neural mechanisms that mediate human visual perception and cognition. What allows us to detect the presence of a clump of weeds in a lawn, to recognize an animal hiding behind a bush, or to remember the precise hue and texture of an ocean surface during sunset? A core assumption in my work is that early visual representations have a powerful but underappreciated role in higher cognitive operations, and that higher-level mechanisms of attention and working memory serve to modulate processing at early visual sites to select and maintain task-relevant visual information. Characterizing and modeling the interplay between early visual representations and higher order representations represents a long-term goal of this work. Our work relies on behavioral and psychophysical methods, high-resolution fMRI, and advanced computational approaches for both data analysis and modeling. My lab has developed novel methods for decoding feature-selective responses from patterns of fMRI activity in the human visual cortex (Kamitani & Tong, Nature Neuroscience, 2005; Current Biology, 2006; Tong & Pratte, Annual Review of Psychology, 2012), and shown how these approaches can be used to characterize the neural bases of visual working memory (Harrison & Tong, Nature, 2009; Pratte et al., 2014) and object-based attentional selection (Pratte et al., J Neurophysiology, 2013; Cohen & Tong, Cerebral Cortex, 2015). In ongoing work, we are developing, training, and testing deep convolutional neural networks as potential models for understanding the neural bases of human visual processing.