Publications & Presentations

†denotes equal contribution

Peer-Reviewed Conference Proceedings

• Carstensen, D. L., †Favila, S. E., & †Frankland, S. M. (2025). Deep Vision Models Follow Shepard's Universal Law of Generalization. Proceedings of the 47th Annual Meeting of the Cognitive Science Society.

Conference Presentations & Posters

• Carstensen, D. L., †Frankland, S. M., & †Favila, S. E. (Aug 2025). Evidence for Shepard's Law in the Representational Spaces of Deep Vision Models. Poster at Cognitive Computational Neuroscience, Amsterdam, NL.
• Carstensen, D. L., †Frankland, S. M., & †Favila, S. E. (Apr 2025). Generalization Gradients in Deep Vision Models: Insights from Shepard's Universal Law of Generalization. Poster at Cognitive Neuroscience Society, Boston, MA.
• Carstensen, D. L., Manning, J. R., & Mucha, P. (May 2024). Translating Neurophysiological Recordings Into Dynamic Estimates of Conceptual Knowledge and Learning. Poster at Wetterhahn Science Symposium.
• †Jha, K., †Carstensen, D. L., Patel, A., & Manning, J. R. (May 2023). Exploring high-order network dynamics in brains and stock markets. Poster at Wetterhahn Science Symposium.

Invited Talks

• Lab meeting (PI: Iris Groen) (2025). University of Amsterdam, Amsterdam, NL.
• Lab meeting (PI: Thomas Serre) (2025). Brown University, Providence, RI.

Current Research Projects

• Generalization Gradients in Deep Vision Models: Investigating the adherence of DNN embedding spaces to Shepard's universal law of generalization using large-scale naturalistic image datasets. (Favila Lab, Brown University and Mind, Brain, & Computation Lab, Dartmouth College)
• EEG-based Conceptual Knowledge Estimation: Using EEG to dynamically estimate conceptual knowledge during learning. (Contextual Dynamics Lab, Dartmouth College)