# Academic Details

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 has taught 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's Deputy Director of Research. He was the lead organizer for the MathPsych/ICCM 2017 conference, and is an Associate Editor for *Journal of Experimental Psychology: Learning, Memory, and Cognition*.

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

Joakim Sundh, Postdoc, 2019-present

Jake Spicer, Postdoc, 2019-present

Jianqiao Zhu, Postdoc, 2019-present

Simon Myers, PhD student, 2018-present

Mengran Wang, PhD student, 2016-present

Alexandra Surdina, PhD student, 2015-present

Jianqiao Zhu, PhD student, 2015-2019

Jake Spicer, PhD student, 2015-2019

James Tripp, Postdoc, 2013-2016

Takao Noguchi, PhD student, 2011-2014

#### Funding and Awards

SAMPLING: Searching for the Approximation Method used to Perform rationaL inference by INdividuals and Groups, Principal Investigator, European Research Council Consolidator Award, 2019 – 2024

Macroeconomic Implications of the Sampling Brain, Co-Investigator, National Institute of Economic and Social Research, 2019

Alan Turing Institute Fellowship, 2018 – 2020

Combination Rules in Information Integration, Principal 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

## Publications

#### 2019 and in press

Lloyd, K., Sanborn, A.N., Lewandowsky, S., & Leslie, D. (in press). Why higher working memory capacity may help you learn: sampling, search, and degrees of approximation. *Cognitive Science*. (open access link)

Sanborn, A.N., Noguchi, T., Tripp, J. & Stewart, N. (in press). A dilution effect without dilution: When missing evidence, not non-diagnostic evidence, is judged inaccurately. *Cognition*. (open access link)

Sanborn, A.N., Zhu, J.-Q., Spicer, J. & Chater, N. (in press). Sampling as a resource-rational constraint. *Behavioral and Brain Sciences*.

Spicer, J. & Sanborn, A.N.. (2019). What does the mind learn? A comparison of human and machine learning representations. *Current Opinion in Neurobiology, 55,* 97-102. (pdf).

Hsu, A. S., Martin, J.B., Sanborn, A.N., & Griffiths, T.L. (2019). Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people. *Behavior Research Methods,* 1-11. (open access link).

León-Villagrá, P., Klar, V.S., Sanborn, A.N., & Lucas, C.G. (2019). Exploring the representation of linear functions. In A. Goel, C. Seifert, & C. Freska (Eds.) *Proceedings of the 41st Annual Conference of the Cognitive Science Society*. (open access link).

Zhu, J.-Q., Sanborn, A.N., & Chater, N. (2019). Why decisions bias perception: an amortised sequential sampling account. In A. Goel, C. Seifert, & C. Freska (Eds.) *Proceedings of the 41st Annual Conference of the Cognitive Science Society*. (open access link).

#### 2018

Zhu, J.-Q., Sanborn, A.N., & Chater, N. (2018). Mental sampling in multimodal representations. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.) *Advances in Neural Information Processing Systems 31,* 5752-5763. (open access link)

#### 2017

Badham, S., Sanborn, A.N., & Maylor, E. (2017). Deficits in category learning in older adults: rule-based versus clustering accounts. *Psychology and Aging, 32, *473-488. (open access link)

Sanborn, A.N. & Chater, N. (2017). The sampling brain. *Trends in Cognitive Sciences, 21(7), *492-493. (manuscript pdf)

Sanborn, A.N. (2017). Types of approximation for probabilistic cognition: sampling and variational. *Brain and Cognition, 112, *98-101. (pdf,open access link)

Ramlee, F., Sanborn, A.N., & Tang, N.K.Y. (2017). What sways people's judgement of sleep quality? A quantitative choice-making study with good and poor sleepers. *Sleep, 40(7),* zsx091. (open access link)

Lloyd, K., Sanborn, A.N., Lewandowsky, S., & Leslie, D. (2017). Why does higher working memory capacity help you learn? In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.) *Proceedings of the 39th Annual Conference of the Cognitive Science Society,* 767-772. (pdf)

Spicer, J. & Sanborn, A.N. (2017). A rational approach to stereotype change. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.) *Proceedings of the 39th Annual Conference of the Cognitive Science Society*, 1102-1107. (pdf)

Surdina, A. & Sanborn, A.N. (2017). Temporal variability in moral value judgement. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.) *Proceedings of the 39th Annual Conference of the Cognitive Science Society,* 3285-3290. (pdf)

#### 2016

Sanborn, A.N. & Chater, N. (2016). Bayesian brains without probabilities. *Trends in Cognitive Sciences, 20(12),* 883-893. (pdf,open access link)

Scholten, M., Read, D., & Sanborn, A. N. (2016). Cumulative weighing of time in intertemporal tradeoffs. *Journal of Experimental Psychology: General, 145(9), *1177-1205 (pdf,open access link).

Sanborn A.N. & Beierholm U.R. (2016). Fast and accurate learning when making discrete numerical estimates. *PLoS Computational Biology 12(4)*: e1004859. (pdf,open access link)

#### 2015

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.

#### 2014

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. (2014). The frequentist implications of optional stopping on Bayesian hypothesis tests. *Psychonomic Bulletin & Review, 21,* 283-300. (link,manuscript pdf)

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)

#### 2013

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)

Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects.* Psychological Review, 120, *411-437. (link,manuscript pdf)

#### 2012

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)

#### 2011

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*.

#### 2010

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)

Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. M. (2010). Uncovering mental representations with Markov chain Monte Carlo. *Cognitive Psychology, 60*, 63-106. (link,manuscript_pdf)

#### 2009

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)

#### 2008

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)