joshua abbott
joshua.abbott@berkeley.edu  

Computational Cognitive Science Lab
Department of Psychology
5429 Tolman Hall
University of California, Berkeley



me  |  research  |  papers

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

T.L. Griffiths, J.T. Abbott, and A.S. Hsu. Exploring human cognition using large image databases. Topics in Cognitive Science, 8(3), 569-588. 2016.
[pdf]

J.C. Peterson, J.T. Abbott, and T.L. Griffiths. Adapting deep network features to capture psychological representations. Proceedings of the 38th Annual Conference of the Cognitive Science Society, 2016.
[abstract] [pdf]

J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122(3), 558-569. 2015.
[pdf]

D.D. Bourgin, J.T. Abbott, K.A. Smith, E. Vul, and T.L. Griffiths. Empirical evidence for Markov chain Monte Carlo in memory search. Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2014.
[abstract] [pdf]

Y. Jia, J.T. Abbott, J.L. Austerweil, T.L. Griffiths and T. Darrell. Visual concept learning: combining machine vision and Bayesian generalization on concept hierarchies. Advances in Neural Information Processing Systems 26, 2013.
[abstract] [pdf] [supplementary materials]

J.T. Abbott, J.B. Hamrick, and T.L. Griffiths. Approximating Bayesian inference with a sparse distributed memory system. Proceedings of the 35th Annual Conference of the Cognitive Science Society, 2013.
[abstract] [pdf]

J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. Human memory search as a random walk in a semantic network. Advances in Neural Information Processing Systems 25, 2012.
[abstract] [pdf]

Y. Jia, J.T. Abbott, J.L. Austerweil, T.L. Griffiths and T. Darrell. Visually-grounded Bayesian word learning. Technical Report UCB/EECS-2012-202. EECS Department, University of California, Berkeley. 2012.
[abstract] [pdf]

J.T. Abbott, T. Regier, and T.L. Griffiths. Predicting focal colors with a rational model of representativeness. Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012.
[abstract] [pdf]

J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. Constructing a hypothesis space from the Web for large-scale Bayesian word learning. Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012.
[abstract] [pdf]

J.T. Abbott, K.A. Heller, Z. Ghahramani, and T.L. Griffiths. Testing a Bayesian measure of representativeness using a large image database. Advances in Neural Information Processing Systems 24, 2011.
[abstract] [pdf]

J.T. Abbott and T.L. Griffiths. Exploring the influence of particle filter parameters on order effects in causal learning. Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 2011.
[abstract] [pdf]

J.T. Abbott. Relevance feedback and novelty detection under the Bayesian sets framework. Master's Thesis. University of Cambridge, 2010.
[pdf]

J.T. Abbott. Some generalizations on counting binary strings. Congressus Numerantium, Vol. 198, 2009.
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J.T. Abbott. Temporal sequence analysis of bottlenose dolphin vocalizations. Undergraduate Thesis. New College of Florida, 2009.
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J.T. Abbott and T. McGuire. Using graphs and games to generate cap set bounds. Congressus Numerantium, Vol. 189, 2008.
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J.T. Abbott, P.Z. Chinn, T.J. Evans, and A.J. Stewart. Graph adjacency matrix automata. Congressus Numerantium Vol. 188, 2007.
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pdf]