Adam Sanborn is a cognitive psychologist interested in how rational people's behaviour is: whether the biases that people show correspond to normative statistical models and approximations to statistical models. He has studied these ideas in various areas of cognition, including categorization, perception, decision making, learning, reasoning, and intuitive physics.
He received his PhD in Cognitive Science and Psychological and Brain Sciences from Indiana University in 2007. From 2007 to 2010 he worked as a postdoc at the Gatsby Computational Neuroscience Unit of University College London, and since he has been at the University of Warwick.
Adam teaches on PS113 Statistical Methods in Psychology, PS366 Implications and Applications of Behavioural Science, PS906 Experimental Design and Data Collection, and PS922 Issues in Psychological Science. He is the department tutor for the MSc in Behavioural and Economic Sciences, and the 2nd Year Group Coordinator for the BSc in Psychology. He is the lead organizer for the MathPsych/ICCM 2017 conference.
Enquiries to join the lab as postdocs or PhD students are welcome. Warwick offers a number of postgraduate scholarships to home and international students on a competitive basis. If you are interested in joining the lab as a postdoctoral research fellow, we are happy to support your fellowship applications to funding bodies such as the ESRC, EPSRC, Leverhulme Trust, and British Academy.
Current and Former Students
Mengran Wang, PhD student, 2016-present
Alexandra Surdina, PhD student, 2015-present
Jake Spicer, PhD student, 2015-present
Takao Noguchi, PhD student, 2011-2014
Funding and Awards
Combination Rules in Information Integration, Principle Investigator, Economic and Social Research Council, 2013 – 2016.
From Fluid Intelligence to Crystallised Expertise: An Integrative Bayesian Approach, Partner Investigator, Australian Research Council, 2012 – 2014.
Best Paper in Psychonomic Bulletin and Review for “Exemplar models as a mechanism for performing Bayesian inference”, 2010
Royal Society USA Postdoctoral Research Fellowship, 2007 – 2009
Outstanding Student Paper Award at the Neural Information Processing Systems Conference, 2007
National Science Foundation Graduate Research Fellowship, 2005 – 2007
National Defense Science and Engineering Graduate Fellowship, 2002 – 2005
Indiana University Chancellor’s Fellowship, 2001 – 2002
American Psychological Association Division 20 (Adult Development and Aging) Award for Completed Undergraduate Research, 2001
Sanborn, A.N. & Chater, N. (in press). The sampling brain. Trends in Cognitive Sciences. (manuscript pdf)
Sanborn, A. N. & Griffiths, T. L. (2015). Exploring the structure of mental representations by implementing computer algorithms with people. Raaijmakers, J.G.W., Criss, A.H., Goldstone, R. L., Nosofsky, R. M., & Steyvers, M. (Eds.). Cognitive Modeling in Perception and Memory: A Festschrift for Richard M. Shiffrin. New York: Psychology Press. (manuscript pdf)
Sanborn, A. N. (2015). Bayesian models of cognition. In: Jaeger D., Jung R. (Ed.) Encyclopedia of Computational Neuroscience: Springer New York Heidelberg Dordrecht London.
Sanborn, A. N. (2014). Testing Bayesian and heuristic predictions of mass judgments of colliding objects. Frontiers in Psychology, 5(938), 1-7. (open access link)
Scholten, M., Read, D., & Sanborn, A. N. (2014). Weighing outcomes by time or against time? Evaluation rules in intertemporal choice. Cognitive Science, 38(3), 399-438. (link)
Sanborn, A. N., Hills, T. T., Dougherty, M. R., Thomas, R. P., Yu, E. C., & Sprenger, A. M. (2014). Reply to Rouder (2014): Good frequentist properties raise confidence. Psychonomic Bulletin & Review, 21, 309-311. (link,manuscript pdf,link to Rouder (2014))
Tang, N. K. Y., & Sanborn, A. N. (2014). Better quality sleep promotes daytime physical activity in patients with chronic pain? A multilevel analysis of the within-person relationship. PLoS ONE, 9(3), e92158. (open access link)
Noguchi, T., Sanborn, A. N., & Stewart, N. (2013). Non-parametric estimation of the individual’s utility map. Proceedings of the 35th Annual Conference of the Cognitive Science Society, (pp. 3145-3150). (pdf)
Sanborn, A. N. & Silva, R. (2013). Constraining bridges between levels of analysis: A computational justification for Locally Bayesian Learning. Journal of Mathematical Psychology, 57, 94-106. (link,manuscript pdf)
Blundell, C., Sanborn, A. N., & Griffiths, T. L. (2012). Look-ahead Monte Carlo with People. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
Hsu, A. S., Martin, J. B., Sanborn, A. N., & Griffiths, T. L. (2012). Identifying representations of categories of discrete items using Markov chain Monte Carlo with People. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21, 263-268. (link)
Martin, J. B., Griffiths, T. L., & Sanborn, A. N. (2012). Testing the efficiency of Markov Chain Monte Carlo with People using facial affect categories. Cognitive Science, 36, 150-162. (link,manuscript pdf)
Tang, N. K. Y., Goodchild, C. E., Sanborn, A. N., Howard, J., & Salkovskis, P. M. (2012). Deciphering the temporal link between pain and sleep in a hetergeneous chronic pain patient sample: A multilevel daily process study. Sleep, 35, 675-687. (link)
Sanborn, A. N. & Dayan, P. (2011). Optimal decisions for contrast discrimination. Journal of Vision, 11(14):9, 1-13. (pdf)
Griffiths, T. L., Sanborn, A. N., Canini, K. R., Navarro, D. J., & Tenenbaum, J. B. (2011). Nonparametric Bayesian models of categorization. In Pothos, E & Wills A. (Eds) Formal Approaches in Categorization.
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117, 1144-1167. (link,manuscript pdf)
Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17, 443-464. (link)
Heller, K., Sanborn, A. N., & Chater, N. (2009). Hierarchical learning of dimensional biases in human categorization. In J. Lafferty & C. Williams (Eds) Advances in Neural Information Processing Systems 22. Cambridge, MA: MIT Press. (pdf)
Sanborn, A. N. & Silva, R. (2009). Belief propagation and locally Bayesian learning. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2009). A Bayesian framework for modeling intuitive dynamics. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf)
Cohen, A. L., Sanborn, A. N., & Shiffrin, R. M. (2008). Model evaluation using grouped or individual data. Psychonomic Bulletin & Review, 15, 692-712. (link)
Sanborn, A. N. & Griffiths T. L. (2008). Markov chain Monte Carlo with people. In J. C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds) Advances in Neural Information Processing Systems 20, 1265-1272. Cambridge, MA: MIT Press. (pdf)
Griffiths, T. L., Sanborn, A. N., Canini, K. R., & Navarro, D. J. (2008). Categorization as nonparametric Bayesian density estimation. In M. Oaksford and N. Chater (Eds.). The Probabilistic Mind: Prospects for Rational Models of Cognition, 303-328. Oxford: Oxford University Press. (pdf)
2007 and earlier
Griffiths, T. L., Canini, K. R., Sanborn, A. N., & Navarro, D. (2007). Unifying rational models of categorization via the hierarchical Dirichlet process. In R. Sun & N. Miyake (Eds), Proceedings of the 29th Annual Conference of the Cognitive Science Society. (pdf)
Sanborn, A. N., Griffiths, T. L., & Navarro, D. (2006). A more rational model of categorization. In R. Sun & N. Miyake (Eds), Proceedings of the 28th Annual Conference of the Cognitive Science Society. (pdf)
Sanborn, A., Malmberg, K., & Shiffrin, R. (2004). High-level effects of masking on perceptual identification. Vision Research, 44, 1427-1436. (link)
Morrow, D.G., Menard, W.E., Ridolfo, H.E., Sanborn, A., Stine-Morrow, E.A.L., Magnor, C., Herman, L., Teller, T. & Bryant, D. (2003). Environmental support promotes expertise-based mitigation of age differences in pilot communication tasks. Psychology and Aging, 18, 268-284. (link)