Abstract: When we open our eyes, visual information flows into various parts of our brain, with each region interpreting different aspects of what we are seeing. Using representational similarity analysis, we combine ms-resolution magnetoencephalography (MEG), mm-resolution functional Magnetic Resonance Imaging (fMRI) and convolutional neural network (CNN) representations to identify stages of visual recognition processes happening at the millisecond and millimeter scales. This approach opens the door to large-scale views of the dynamics and algorithms of recognition at the scale of processing steps across the whole human brain.
Research Background: My research program is in the field of Computational Visual Cognition, a framework that strives to identify the substrates of complex visual recognition tasks and to develop models inspired by human perception and cognition. The natural visual environment is composed of three-dimensional objects, with textures, colors, and materials, embedded in an explicit spatial layout. Yet, the human brain understands scenes, places and events quickly and effortlessly, outperforming the most advanced artificial vision system. In the lab, we use multi-disciplinary techniques from behavioral sciences, cognitive neuroscience and computational vision, to identify key principles of human object, scene and space understanding and evaluate the capacity and fidelity of human memory systems for guiding the development of computational and theoretical frameworks in computational cognition. Ultimately, the results of characterizing human perceptual and cognitive abilities and limitations in a natural setting holds promise for inspiring the next generation of artificial vision systems but also gives insights for the understanding of visual and cognitive disorders. Our research programs bring together disciplines such as perceptual science, cognitive science and neuroscience, neuropsychology, photography, architecture, image processing, computer vision and computer graphics.
