|Title||To find better neural network models of human vision, find better neural network models of primate vision|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Jozwik, KM, Schrimpf, M, Kanwisher, N, DiCarlo, JJ|
|Type of Article||preprint|
Specific deep artificial neural networks (ANNs) are the current best models of ventral visual processing and object recognition behavior in monkeys. We here explore whether models of non-human primate vision generalize to visual processing in the human primate brain. Specifically, we asked if model match to monkey IT is a predictor of model match to human IT, even when scoring those matches on different images. We found that the model match to monkey IT is a positive predictor of the model match to human IT (R = 0.36), and that this approach outperforms the current standard predictor of model accuracy on ImageNet. This suggests a more powerful approach for pre-selecting models as hypotheses of human brain processing.