Adam N. Sanborn
Affiliations: | 2010- | Psychology | University of Warwick, Coventry, England, United Kingdom |
Area:
PsychologyWebsite:
http://www2.warwick.ac.uk/fac/sci/psych/people/asanborn/Google:
"Adam Sanborn"Mean distance: 13.44 (cluster 23) | S | N | B | C | P |
Cross-listing: PsychTree
Parents
Sign in to add mentor| Thomas L. Griffiths | grad student | 2005-2006 | Brown | |
| Richard M. Shiffrin | grad student | 2001-2007 | Indiana University | |
| (Uncovering mental representations with Markov chain Monte Carlo.) | ||||
| Peter Dayan | post-doc | 2007-2010 | UCL | |
Children
Sign in to add trainee| Yun-Xiao Li | grad student | 2021- | |
| C. Stella Qian | post-doc | 2023- | University of Warwick (UK) |
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Publications
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| Sanborn AN, Heller K, Austerweil JL, et al. (2021) REFRESH: A new approach to modeling dimensional biases in perceptual similarity and categorization. Psychological Review |
| Spicer J, Sanborn AN, Beierholm UR. (2020) Using Occam's razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements. Cognitive Psychology. 122: 101309 |
| Zhu JQ, Sanborn AN, Chater N. (2020) The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments. Psychological Review |
| Lloyd K, Sanborn A, Leslie D, et al. (2019) Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation. Cognitive Science. 43: e12805 |
| Spicer J, Sanborn AN. (2019) What does the mind learn? A comparison of human and machine learning representations. Current Opinion in Neurobiology. 55: 97-102 |
| Hsu AS, Martin JB, Sanborn AN, et al. (2019) Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people. Behavior Research Methods |
| Badham SP, Sanborn AN, Maylor EA. (2017) Deficits in category learning in older adults: Rule-based versus clustering accounts. Psychology and Aging. 32: 473-488 |
| Sanborn AN, Chater N. (2016) Bayesian Brains without Probabilities. Trends in Cognitive Sciences. 20: 883-893 |
| Sanborn AN, Beierholm UR. (2016) Fast and Accurate Learning When Making Discrete Numerical Estimates. Plos Computational Biology. 12: e1004859 |
| Sanborn AN. (2015) Types of approximation for probabilistic cognition: Sampling and variational. Brain and Cognition |