1) A computational account of topography in the occipitotemporal cortex via domain-general pressures:
Collaborators: Talia Konkle
Doshi, F.R., Konkle, T. (2022) Visual object topographic motifs emerge from self-organization of a unified representational space. bioRxiv, in press, doi:10.1101/2022.09.06.506403. link to paper
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
talk at the Vision Sciences Society 2021 Conference
2) Mechanisms of Contour Integration in Humans and Machines:
Human-like signatures of contour integration in deep neural networks
- Doshi, F., Konkle, T., & Alvarez, G.A. (2021). Human-like signatures of contour integration in deep neural networks. Talk presented at the Vision Sciences Society 2022 Conference
3) 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
4) 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!