Joshua T. Abbott
Joshua T. Abbott
Melbourne School of Psychological Sciences
Verified email at unimelb.edu.au
TitleCited byYear
Random walks on semantic networks can resemble optimal foraging
JT Abbott, JL Austerweil, TL Griffiths
Psychological Review 122 (3), 558-569, 2015
742015
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
Y Jia, J Abbott, J Austerweil, T Griffiths, T Darrell
Advances in Neural Information Processing Systems 26, 2013
482013
Human memory search as a random walk in a semantic network
J Abbott, J Austerweil, T Griffiths
Advances in Neural Information Processing Systems 25, 3050-3058, 2012
41*2012
Adapting Deep Network Features to Capture Psychological Representations
JC Peterson, JT Abbott, TL Griffiths
Proceedings of the 38th Annual Conference of the Cognitive Science Society …, 2016
392016
Biological origins of color categorization
AE Skelton, G Catchpole, JT Abbott, JM Bosten, A Franklin
Proceedings of the National Academy of Sciences 114 (21), 5545-5550, 2017
322017
Focal colors across languages are representative members of color categories
JT Abbott, TL Griffiths, T Regier
Proceedings of the National Academy of Sciences 113 (40), 11178-11183, 2016
292016
Exploring the influence of particle filter parameters on order effects in causal learning
JT Abbott, TL Griffiths
Proceedings of the 33rd Annual Conference of the Cognitive Science Society …, 2011
252011
Constructing a hypothesis space from the Web for large-scale Bayesian word learning
JT Abbott, JL Austerweil, TL Griffiths
Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012
232012
Empirical Evidence for Markov Chain Monte Carlo in Memory Search
DD Bourgin, JT Abbott, TL Griffiths, KA Smith, E Vul
Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2014
222014
Approximating Bayesian inference with a sparse distributed memory system
JT Abbott, JB Hamrick, TL Griffiths
Proceedings of the 35th Annual Conference of the Cognitive Science Society, 2013
182013
Evaluating (and improving) the correspondence between deep neural networks and human representations
JC Peterson, JT Abbott, TL Griffiths
Cognitive science 42 (8), 2648-2669, 2018
152018
Testing a Bayesian Measure of Representativeness Using a Large Image Database
JT Abbott, KA Heller, Z Ghahramani, TL Griffiths
Advances in Neural Information Processing Systems 24, 2321-2329, 2011
122011
Predicting focal colors with a rational model of representativeness
JT Abbott, T Regier, TL Griffiths
Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012
92012
Exploring human cognition using large image databases
TL Griffiths, JT Abbott, AS Hsu
Topics in cognitive science 8 (3), 569-588, 2016
62016
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report
JC Peterson, JT Abbott, TL Griffiths
Proceedings of the Twenty-Sixth International Joint Conference on Artificial …, 2017
42017
Concept acquisition through meta-learning
E Grant, C Finn, J Peterson, J Abbott, S Levine, T Darrell, T Griffiths
NIPS Workshop on Cognitively Informed Artificial Intelligence, 2017
12017
Visually-Grounded Bayesian Word Learning
Y Jia, J Abbott, J Austerweil, T Griffiths, T Darrell
EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS …, 2012
12012
Recommendation as Generalization: Evaluating Cognitive Models In the Wild
DD Bourgin, JT Abbott, TL Griffiths
Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018
2018
Leveraging deep neural networks to capture psychological representations
JC Peterson, JT Abbott, TL Griffiths
arXiv preprint arXiv:1706.02417, 2017
2017
Statistical models of learning and using semantic representations
JT Abbott
UC Berkeley, 2016
2016
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