Journal
NEURON
Volume 79, Issue 5, Pages 1025-1034Publisher
CELL PRESS
DOI: 10.1016/j.neuron.2013.06.034
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Funding
- National Eye Institute [EY019684]
- National Institute of Mental Health [MH66990]
- National Science Foundation Center for the Science of Information [CCF-0939370]
- Office Of The Director
- EPSCoR [919440] Funding Source: National Science Foundation
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During natural vision, humans categorize the scenes they encounter: an office, the beach, and so on. These categories are informed by knowledge of the way that objects co-occur in natural scenes. How does the human brain aggregate information about objects to represent scene categories? To explore this issue, we used statistical learning methods to learn categories that objectively capture the co-occurrence statistics of objects in a large collection of natural scenes. Using the learned categories, we modeled fMRI brain signals evoked in human subjects when viewing images of scenes. We find that evoked activity across much of anterior visual cortex is explained by the learned categories. Furthermore, a decoder based on these scene categories accurately predicts the categories and objects comprising novel scenes from brain activity evoked by those scenes. These results suggest that the human brain represents scene categories that capture the co-occurrence statistics of objects in the world.
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