high-risk research on hard problems in natural and artificial systems:

tools drawn from cognitive science, machine learning, computer vision, computer graphics, and large-scale neuroimaging, including fMRI, DTI, MEG, EEG

My own (crazy) thoughts about some of our recent work: The organization of knowledge within visual cortex may be best characterized as a multimodal "operating system" for learning about, representing, and processing information relevant to adaptive behaviors. Alternatively, one might argue that this organization necessarily reflects and emerges from common adaptive behaviors of humans (e.g., food selectivity emerges because humans eat frequently and so on...). However, the consistency of organization for these functional structures across individuals suggests otherwise; instead, pointing towards a core set of constraints that enable a specific higher-order knowledge structure that is inherently anchored in real-world behaviors. Exploring these ideas further will require new ways of thinking about visual cortex.

news

πŸ“ NEW PAPER: A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex. J Neurosci. JN-RM-1822-22. Link

πŸ“ NEW PAPER: Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features. J of Vis. 23(8). Link

πŸŽ‰ CONGRATULATIONS to Nadine for successfully defending her PhD thesis "Bridging the gap from human vision to computer vision" (April 25, 2023)

πŸ“ NEW PAPER: Selectivity for food in human ventral visual cortex. Commun Biol. 6, 175. Link | github

πŸ“ NEW PAPER: Why is human vision so poor in early development? The impact of initial sensitivity to low spatial frequencies on visual category learning. PLoS ONE. 18(1): e0280145. Link | github