Jacob Feldman - Home

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Research Interests:

Computational models and empirical studies of human visual perception and concept learning, including perceptual organization, grouping, and shape.

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Selected recent papers:

• Feldman, J. (2025). Simplicity and complexity of probabilistically-defined concepts. In press, Psychological Review.

• Feldman, J. (2024). Probabilistic origins of compositional mental representations. Psychological Review, 131(3), 599-624

• Destler, N., Singh, M. and Feldman, J. (2023). Skeleton-based shape similarity. Psychological Review. 130(6), 1653–1671.

• Feldman, J. and Choi, L.-S. (2022). Meaning and reference from a probabilistic point of view. Cognition, 223, 105058.

• Feldman, J. (2021). Information-theoretic signal detection theory. Psychological Review, 128(5), 976-987.

• Feldman, J. (2021). Mutual information and categorical perception. Psychological Science, 32(8) 1298-1310.

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Selected older papers:

• Froyen, V., Feldman, J. and Singh, M. (2015). Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation. Psychological Review, 122(4), 575-597.

• Pantelis, P. C., Baker, C., Cholewiak, S., Sanik, K., Weinstein, A., Wu, C.-C., Tenenbaum, J. B. and Feldman, J. (2014) Inferring the intentional states of autonomous virtual agents. Cognition , 130, 360–379.

• Feldman, J. (2012) Symbolic representation of probabilistic worlds. Cognition , 123, 61–83.

• Mathy, F. and Feldman, J. (2012) What’s magic about magic numbers? Chunking and data compression in short-term memory. Cognition, 122, 346–362.

• Feldman, J. and Singh, M. (2006) Bayesian estimation of the shape skeleton. PNAS , 103(47), 18014–18019. Shape toolbox software

• Feldman, J. (2000) Minimization of Boolean complexity in human concept learning. Nature , 407, 630–633.