D.D. Bourgin, J.T. Abbott, and T.L. Griffiths. (2021). Recommendation as generalization: Using big data to evaluate cognitive models. Journal of Experimental Psychology: General, 150(7), 1398-1409.
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J.T. Abbott and C. Kemp. (2020). Birds and Words: Exploring environmental influences on folk categorization. Proceedings of the 42nd Annual Conference of the Cognitive Science Society.
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J.C. Peterson, J.T. Abbott and T.L. Griffiths. (2018). Evaluating (and improving) the correspondence between deep neural networks and human representations. Cognitive Science, 42(8), 2648-2669.
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D.D. Bourgin, J.T. Abbott, and T.L. Griffiths. (2018). Recommendation as Generalization: Evaluating Cognitive Models In the Wild. Proceedings of the 40th Annual Conference of the Cognitive Science Society.
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A.E. Skelton, G. Catchpole, J.T. Abbott, J.M. Bosten, and A. Franklin. (2017). Biological origins of color categorization. Proceedings of the National Academy of Sciences, 114(21), 5545-5550.
[pdf] [supporting information]

J.T. Abbott, T.L. Griffiths, and T. Regier. (2016). Focal colors across languages are representative members of colors categories. Proceedings of the National Academy of Sciences, 113(40), 11178-11183.
[pdf] [supporting information]

T.L. Griffiths, J.T. Abbott, and A.S. Hsu. (2016). Exploring human cognition using large image databases. Topics in Cognitive Science, 8(3), 569-588.
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J.C. Peterson, J.T. Abbott, and T.L. Griffiths. (2016). Adapting deep network features to capture psychological representations. Proceedings of the 38th Annual Conference of the Cognitive Science Society.
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J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. (2015). Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122(3), 558-569.
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D.D. Bourgin, J.T. Abbott, K.A. Smith, E. Vul, and T.L. Griffiths. (2014). Empirical evidence for Markov chain Monte Carlo in memory search. Proceedings of the 36th Annual Conference of the Cognitive Science Society.
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Y. Jia, J.T. Abbott, J.L. Austerweil, T.L. Griffiths and T. Darrell. (2013). Visual concept learning: combining machine vision and Bayesian generalization on concept hierarchies. Advances in Neural Information Processing Systems 26.
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J.T. Abbott, J.B. Hamrick, and T.L. Griffiths. (2013). Approximating Bayesian inference with a sparse distributed memory system. Proceedings of the 35th Annual Conference of the Cognitive Science Society.
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J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. (2012). Human memory search as a random walk in a semantic network. Advances in Neural Information Processing Systems 25.
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Y. Jia, J.T. Abbott, J.L. Austerweil, T.L. Griffiths and T. Darrell. (2012). Visually-grounded Bayesian word learning. Technical Report UCB/EECS-2012-202. EECS Department, University of California, Berkeley.
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J.T. Abbott, T. Regier, and T.L. Griffiths. (2012). Predicting focal colors with a rational model of representativeness. Proceedings of the 34th Annual Conference of the Cognitive Science Society.
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J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. (2012). Constructing a hypothesis space from the Web for large-scale Bayesian word learning. Proceedings of the 34th Annual Conference of the Cognitive Science Society.
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J.T. Abbott, K.A. Heller, Z. Ghahramani, and T.L. Griffiths. (2011). Testing a Bayesian measure of representativeness using a large image database. Advances in Neural Information Processing Systems 24.
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J.T. Abbott and T.L. Griffiths. (2011). Exploring the influence of particle filter parameters on order effects in causal learning. Proceedings of the 33rd Annual Conference of the Cognitive Science Society.
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