Project Summary/Abstract: Humans are extraordinarily visual animals, allocating a third of their cortex just to
seeing what is in front of them. Visual recognition is supported by a series of hierarchically organized brain
regions known collectively as the ventral visual cortex (VVC). Despite extensive research, we still lack a
computationally precise understanding of how visual information is represented and transformed over stages of
the human VVC. A key barrier has been the limitations of methods like functional MRI (fMRI) which make it
difficult to test a large number of experimental stimuli. The research in this proposal will overcome this barrier by
collecting fMRI responses to hundreds of stimuli, and analyzing these data using deep neural network based
computational models and human interpretable algorithms such as image-synthesis and saliency mapping. In
Aim 1 (K99 phase), I will focus on the category-selective regions of the VVC, that respond preferentially to
images of faces (fusiform face area), scenes (parahippocampal place area), and bodies (extrastriate body area).
I will develop and use new computational methods together with closed-loop experiments to address open
questions such as: Is the hypothesized selectivity for these regions even correct? What is represented in the
intermediate stages of processing? Are there functionally distinct regions within the category-selective regions?
In Aim 2 (R00 phase), I will venture into the ~65% of VVC that lies outside the category-selective regions. I will
develop and apply new data-driven clustering to divide these regions into their native components, and
characterize them individually. Together, this endeavor will reveal the computational and neural basis of visual
recognition in humans with an unprecedented precision. My background in experimental and analytical methods
in monkey and human vision puts me in a unique position to accomplish this proposal which requires a seamless
integration between neuroimaging experiments and state-of-the-art computational modeling. The proposed work
will be initiated in the lab of Prof. Nancy Kanwisher (mentor). During the K99 phase, I will continue to be mentored
by Prof. Kanwisher, and will also advance my expertise with computational modeling under the supervision of
Dr. Jim DiCarlo (co-mentor), and ultra-high-resolution 7T neuroimaging with Dr. Jon Polimeni (collaborator). This
proposed plan will significantly augment my theoretical understanding and experimental abilities, and put me on
a path to independence.