A computational account of topography in the occipitotemporal cortex:
Collaborators: Talia Konkle
Here’s a talk on this project, presented at the Vision Sciences Society 2021 Conference
- Doshi, F., & Konkle, T. (2021). Organizational motifs of cortical responses to objects emerge in topographic projections of deep neural networks. Journal of Vision, 21(9), 2226-2226. link
Does human vision directly leverage perceptual features as optimal proxies for intuitive physical reasoning?
Conwell, C., Doshi, F., Alvarez, G.A.(2019). Shared Representations of Stability in Humans, Supervised, & Unsupervised Neural Networks. In Shared Visual Representations in Human and Machine Intelligence. SVRHM workshop at NeurIPS 2019. pdf
Conwell, C., Doshi, F., Alvarez, G.A.(2019). Human-Like Judgments of Stability Emerge from Purely Perceptual Features: Evidence from Supervised and Unsupervised Deep Neural Networks. In Proceedings of the 3rd Conference on Cognitive Computational Neuroscience (CCN), 2019. pdf
What representations explain capacity limits in visual working memory?
- Doshi, F., Pailian, H., & Alvarez, G. A. (2020). Using Deep Convolutional Neural Networks to Examine the Role of Representational Similarity in Visual Working Memory. Journal of Vision, 20(11), 149-149.link
Also check out Hrag’s talk here!