Research Interests:
Computational models and empirical studies of human visual perception and concept learning, including perceptual organization, grouping, and shape.
Selected recent papers:
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Feldman, J. (2025). Simplicity and complexity of probabilistically-defined concepts. In press, Psychological Review.
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Feldman, J. (2024). Probabilistic origins of compositional mental representations. Psychological Review, 131(3), 599-624
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Destler, N., Singh, M. and Feldman, J. (2023). Skeleton-based shape similarity. Psychological Review. 130(6), 1653–1671.
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Feldman, J. and Choi, L.-S. (2022). Meaning and reference from a probabilistic point of view. Cognition, 223, 105058.
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Feldman, J. (2021). Information-theoretic signal detection theory. Psychological Review, 128(5), 976-987.
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Feldman, J. (2021). Mutual information and categorical perception. Psychological Science, 32(8) 1298-1310.
Selected older papers:
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Froyen, V., Feldman, J. and Singh, M. (2015). Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation. Psychological Review, 122(4), 575-597.
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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.
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Feldman, J. (2012) Symbolic representation of probabilistic worlds. Cognition, 123, 61–83.
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Mathy, F. and Feldman, J. (2012) What’s magic about magic numbers? Chunking and data compression in short-term memory. Cognition, 122, 346–362.
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Feldman, J. and Singh, M. (2006) Bayesian estimation of the shape skeleton. PNAS, 103(47), 18014–18019. Shape toolbox software
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Feldman, J. (2000) Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633.