Computational Cognitive Science Lab

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F Foundations
P Perception
E Education
CI Causal Induction
CD Cognitive Development
PR Probabilistic Reasoning
RPM Rational Process Models
S&C Similarity and Categorization
SML Statistical Models of Language
NBM Nonparametric Bayesian Models
CEIL Cultural Evolution and Iterated Learning
DMRL Decision Making and Reinforcement Learning

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By Abbott, J.
S&C	Bourgin, D., Abbott, J. T., & Griffiths, T. L. (2021). Recommendation as generalization: Using big data to evaluate cognitive models. Journal of Experimental Psychology: General, 150, 1398–1409. (pdf)
P S&C	Peterson, J. C., Abbott, J. T., & Griffiths, T. L. (2018). Evaluating (and improving) the correspondence between deep neural networks and human representations. Cognitive Science, 42, 2648-2669. (pdf)
S&C	Bourgin, D. D., Abbott, J. T., & Griffiths, T. L. (2018). Recommendation as generalization: Evaluating cognitive models in the wild. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
F P	Griffiths, T. L., Abbott, J. T., & Hsu, A. S. (2016). Exploring human cognition using large image databases. Topics in Cognitive Science, 8(3), 569-588. (pdf)
P S&C	Abbott, J. T., Griffiths, T. L., & Regier, T. (2016). Focal colors across languages are representative members of color categories. Proceedings of the National Academy of Sciences, 113(40), 11178-11183. (pdf)
P S&C	Peterson, J. C., Abbott, J. T., & Griffiths, T. L. (2016). Adapting deep network features to capture psychological representations. Proceedings of the 38th Annual Conference of the Cognitive Science Society. (pdf) (Winner of the Computational Modeling Prize in Perception/Action)
RPM SML	Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2015). Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122, 558-569. (pdf)
RPM SML	Bourgin, D. D., Abbott, J. T., Griffiths, T. L., Smith, K. A., & Vul, E. (2014). Empirical evidence for Markov chain Monte Carlo in memory search. Proceedings of the 36th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Jia, Y., Abbott, J. T., Austerweil, J. L., Griffiths, T. L., & Darrell, T. (2013). Visual concept learning: Combining machine vision and Bayesian generalization on concept hierarchies. Advances in Neural Information Processing Systems, 26. (pdf)
PR RPM	Abbott, J. T., Hamrick, J. B., & Griffiths, T. L. (2013). Approximating Bayesian inference with a sparse distributed memory system. Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pdf)
RPM SML	Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2012). Human memory search as a random walk in a semantic network. Advances in Neural Information Processing Systems, 25. (pdf)
P S&C	Abbott, J. T., Regier, T., & Griffiths, T. L. (2012). Predicting focal colors with a rational model of representativeness. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
P S&C	Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (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. (pdf)
PR S&C	Abbott, J. T., Heller, K. A., Ghahramani, Z., & Griffiths, T. L. (2011). Testing a Bayesian measure of representativeness using a large image database. Advances in Neural Information Processing Systems, 24. (pdf)
CI RPM	Abbott, J. T., & Griffiths, T. L. (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. (pdf)