Job Title
Professor
Department
Psychology
Phone
(024) 761 51354
Web Link
Research Interests
Explaining human categorisation and perception as rational behaviour. Examining how people use approximate solutions in difficult cognitive tasks. Methods for data collection and analysis.
Biography
Adam Sanborn is a Professor of Psychology at the University of Warwick. He gained his PhD in Psychological & Brain Sciences and Cognitive Science at Indiana University, and did his postdoctoral work at the Gatsby Computational Neuroscience Unit at UCL. Adam is interested in the rationality of human behaviour, which he studies with Bayesian models, approximations to Bayesian models, and behavioural experiments. His research has been published in leading psychology journals such as Psychological Review, Trends in Cognitive Sciences, and Cognitive Psychology, and he has won best paper awards in both psychology and computer science. His work has been funded by the ESRC, ERC, Alan Turing Institute, and NIESR, and he serves as Associate Editor for the Journal of Experimental Psychology: Learning, Memory, and Cognition.
Representative Publications:
Sanborn, A.N., Heller, K., Austerweil, J.L., & Chater, N. (2021). REFRESH: A new approach to modeling dimensional biases in perceptual similarity and categorization. Psychological Review, 128(6), 1145-1186.
Sanborn, A.N. & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883-893.
Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120, 411-437.
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.
- 'Zhu , Jian-Qiao, 'Newall, Philip W. S., 'Sundh, Joakim, 'Chater, Nick, 'Sanborn, Adam N., 2022. 'Clarifying the relationship between coherence and accuracy in probability judgments. Cognition, 223
- 'Zhu , Jian-Qiao, 'Le?n-Villagr?, Pablo, 'Chater, Nick, 'Sanborn, Adam N., 2022. 'Understanding the structure of cognitive noise. PLoS Computational Biology, 18 (8)
- 'Spicer, Jake, 'Zhu, Jian-Qiao, 'Chater, Nick, 'Sanborn, Adam N., 2022. 'Perceptual and cognitive judgments show both anchoring and repulsion. Psychological Science, 33 (9), pp. 1395-1407
- Castillo, Lucas, Leon-Villagra, Pablo, Chater, Nick, Sanborn, Adam N., 2021. Local sampling with momentum accounts for human random sequence generation. Proceedings of the Annual Meeting of the Cognitive Science Society, 43
- 'Sanborn, Adam N., 'Heller, Katherine, 'Austerweil, Joseph L., 'Chater, Nick, 2021. 'REFRESH : a new approach to modeling dimensional biases in perceptual similarity and categorization. Psychological Review, 128 (6), pp. 1145-1186
- Sanborn, Adam N., Noguchi, Takao, Tripp, James, Stewart, Neil, 2020. A dilution effect without dilution : when missing evidence, not non-diagnostic evidence, is judged inaccurately. Cognition, 196
- Chater, Nick, Zhu, Jian-Qiao, Spicer, Jake, Sundh, Joakim, León-Villagrá, Pablo, Sanborn, Adam N., 2020. Probabilistic biases meet the Bayesian brain. Current Directions in Psychological Science, 29 (5), pp. 506-512
- Das-Friebel, Ahuti, Lenneis, Anita, Realo, Anu, Sanborn, Adam N., Tang, Nicole K. Y., Wolke, Dieter, von Mühlenen, Adrian, Lemola, Sakari, 2020. Bedtime social media use, sleep, and affective wellbeing in young adults : an experience sampling study. Journal of Child Psychology and Psychiatry, 61 (10), pp. 1138-1149
- Spicer, Jake, Sanborn, Adam N., Beierholm, Ulrik R., 2020. Using Occam's razor and Bayesian modelling to compare discrete and continuous representations in numerostiy judgements. Cognitive Psychology, 122
- Zhu, Jianqiao, Sanborn, Adam N., Chater, Nick, 2020. The Bayesian sampler : generic Bayesian inference causes incoherence in human probability. Psychological Review, 127 (5), pp. 719-748
- Spicer, Jake, Sanborn, Adam N., 2019. What does the mind learn? A comparison of human and machine learning representations. Current Opinion in Neurobiology, 55, pp. 97-102
- Lloyd, Kevin, Sanborn, Adam N., Leslie, David, Lewandowsky, Stephan, 2019. Why higher working memory capacity may help you learn : sampling, search, and degrees of approximation. Cognitive Science, 43 (12)
- Hsu, Anne S., Martin, Jay B., Sanborn, Adam N., Griffiths, Thomas L., 2019. Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people. Behavior Research Methods, pp. 1-11
- Zhu , Jian-Qiao, Sanborn, Adam N., Chater , Nick, 2018. Mental sampling in multimodal representations. Advances in Neural Information Processing Systems (NIPS 2016), pp. 5753-5764
- Badham, Stephen P., Sanborn, Adam N., Maylor, Elizabeth A., 2017. Deficits in category learning in older adults : rule-based versus clustering accounts. Psychology and Aging, 32 (5), pp. 473-488
- Sanborn, Adam N., 2017. Types of approximation for probabilistic cognition : sampling and variational. Brain and cognition, 112, pp. 98-101
- Ramlee, Fatanah, Sanborn, Adam N., Tang, Nicole K. Y., 2017. What sways people's judgment of sleep quality? A quantitative choice-making study with good and poor sleepers. Sleep, 40 (7)
- Sanborn, Adam N., Chater, Nick, 2017. The sampling brain. Trends in Cognitive Sciences, 21 (7), pp. 492-493
- Sanborn, Adam N., Beierholm, Ulrik R., 2016. Fast and accurate learning when making discrete numerical estimates. PLoS Computational Biology, 12 (4)
- Scholten, Marc, Read, Daniel, Sanborn, Adam N., 2016. Cumulative weighing of time in intertemporal tradeoffs. Journal of Experimental Psychology: General, 145 (9), pp. 1177-1205
- Sanborn, Adam N., Chater, Nick, 2016. Bayesian brains without probabilities. Trends in Cognitive Sciences, 20 (12), pp. 883-893
- Sanborn, Adam N., Hills, Thomas Trenholm, 2014. The frequentist implications of optional stopping on Bayesian hypothesis tests. Psychonomic Bulletin & Review, 21 (2), pp. 283-300
- Scholten, Marc, Read, Daniel, Sanborn, Adam N., 2014. Weighing outcomes by time or against time? Evaluation rules in intertemporal choice. Cognitive Science, 38 (3), pp. 399-438
- Sanborn, Adam N., 2014. Testing Bayesian and heuristic predictions of mass judgments of colliding objects. Frontiers in Psychology, 5
- Tang, Nicole K. Y., Sanborn, Adam 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), pp. 1-9
- Sanborn, Adam N., Silva, Ricardo, 2013. Constraining bridges between levels of analysis : a computational justification for locally Bayesian learning. Journal of Mathematical Psychology, 57 (3-4), pp. 94-106
- Sanborn, Adam N., Mansinghka, Vikash K., Griffiths, Thomas L., 2013. Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120 (2), pp. 411-437
- Tang, Nicole K. Y., Goodchild, Claire E., Sanborn, Adam N., Howard, Jonathan, Salkovskis, Paul M., 2012. Deciphering the temporal link between pain and sleep in a heterogeneous chronic pain patient sample : a multilevel daily process study. Sleep, Vol.35 (No.5), pp. 675-687
- Martin, J. B., Griffiths, Thomas L., Sanborn, Adam N., 2012. Testing the efficiency of Markov Chain Monte Carlo with people using facial affect categories. Cognitive Science, 36 (11), pp. 150-162
- Griffiths, T. L., Vul, E., Sanborn, Adam N., 2012. Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21 (4), pp. 263-268
- Sanborn, Adam N., Dayan, P., 2011. Optimal decisions for contrast discrimination. Journal of Vision, Vol.11 (No.14)
- Sanborn, Adam N., Griffiths, Thomas L., Shiffrin, Richard M., 2010. Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, Vol.60 (No.2), pp. 63-106
- Shi, Lei, Griffiths, Thomas L., Feldman, Naomi H., Sanborn, Adam N., 2010. Exemplar models as a mechanism for performing Bayesian inference. Psychonomic bulletin & review, Vol.17 (No.4), pp. 443-64
- Sanborn, Adam N., Griffiths, Thomas L., Navarro, Daniel J, 2010. Rational approximations to rational models : alternative algorithms for category learning. Psychological Review, Vol.117 (No.4), pp. 1144-67
- Cohen, Andrew L., Sanborn, Adam N., Shiffrin, Richard M., 2008. Model evaluation using grouped or individual data. Psychonomic Bulletin & Review, Vol.15 (No.4), pp. 692-712
- Sanborn, Adam N., Malmberg, Kenneth J., Shiffrin, Richard M., 2004. High-level effects of masking on perceptual identification. Vision Research, Vol.44 (No.12), pp. 1427-1436
- Morrow, Daniel G., Ridolfo, Heather E., Menard, William E., Sanborn, Adam N., Stine-Morrow, Elizabeth A. L., Magnor, Cliff, Herman, Larry, Teller, Thomas, Bryant, David, 2003. Environmental support promotes expertise-based mitigation of age differences on pilot communication tasks. Psychology and Aging, Vol.18 (No.2), pp. 268-284
- 'Sundh, Joakim, 'Sanborn, Adam N., 'Zhu, Jian-Qiao, 'Spicer, Jake, 'Le?n-Villagr?, Pablo, 'Chater, Nick, 2024. 'Approximating Bayesian inference through internal sampling. Fiedler, Klaus; Juslin, Peter; Denrell, Jerker (eds.), Sampling in Judgment and Decision Making, Cambridge University Press
- Zhu, Jian-Qiao, Chater, Nick, León-Villagrá, Pablo, Spicer, Jake, Sundh, Joakim, Sanborn, Adam N., 2024. An introduction to psychologically plausible sampling schemes for approximating Bayesian inference. Fiedler, Klaus; Juslin, Peter; Denrell, Jerker (eds.), Sampling in Judgment and Decision Making, Cambridge University Press
- 'Sanborn, Adam N., 'Zhu, J -Q., 'Spicer, Jake, 'Sundh, Joakim, 'Le?n-Villagr?, Pablo, 'Chater, Nick, 2021. 'Sampling as the human approximation to probabilistic inference. Muggleton, Stephen; Chater, Nicholas (eds.), Human-Like Machine Intelligence, Oxford, Oxford University Press
- Sanborn, Adam N., Griffiths, Thomas L., 2015. Exploring the structure of mental representations by implementing computer algorithms with people. In 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 ; Taylor & Francis Group, pp. 212-228
- Sanborn, Adam N., 2015. Bayesian models of cognition. In Jaeger, Dieter; Jung, Ranu (eds.), Encyclopedia of Computational Neuroscience, Springer, pp. 624-625
- Griffiths, Thomas L., Sanborn, Adam N., Canini, Kevin R., Navarro, Daniel J., Tenenbaum, Joshua B., 2011. Nonparametric Bayesian models of categorization. In Pothos, Emmanuel M.; Wills, Andy J. (eds.), Formal Approaches in Categorization, Cambridge, Cambridge University Press, pp. 173-198
- Griffiths, Thomas L., Canini, Kevin R., Sanborn, Adam N., Navarro, Daniel J., 2009. Unifying rational models of categorization via the hierarchical Dirichlet process. Proceedings of the 29th Annual Conference of the Cognitive Science Society, Mahwah, N.J., Lawrence Erlbaum
- Heller, Katherine, Sanborn, Adam N., Chater, Nick, 2009. Hierarchical learning of dimensional biases inhuman categorization. Lafferty , J. Clayton (James Clayton), 1928-; Williams , C. (eds.), Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009, December 7-10, 2009, Vancouver, B.C., Canada., La Jolla, C.A., Neural Information Processing Systems
- Sanborn, Adam N., Griffiths, Thomas L., 2008. Markov chain Monte Carlo with people. In Platt, John C. (ed.), Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007, Red Hook, N.Y., Curran Associates Inc, pp. 1265-1272
- Griffiths, Thomas L., Sanborn, Adam N., Canini, Kevin R., Navarro, Daniel J., 2008. Categorization as nonparametric Bayesian density estimation. In Chater, Nick; Oaksford, Mike (eds.), The probabilistic mind : prospects for Bayesian cognitive science, Oxford, Oxford University Press, pp. 303-328
- Sanborn, Adam N., Griffiths, Thomas L., Navarro, Daniel J., 2006. A more rational model of categorization. In Proceedings of the 28th annual conference of the Cognitive Science Society, Mahwah, N.J., Lawrence Erlbaum, pp. 726-731
- Lloyd, Kevin, Sanborn, Adam N., Leslie, David, Lewandowsky, Stephan, 2017. Why does higher working memory capacity help you learn?. CogSci 2017, London, 26-29 Jul 2017, Published in CogSci 2017 Proceedings of the 39th Annual Meeting of the Cognitive Science Society London, UK, 26-29 July 2017, pp. 767-772
- Surdina, Alexandra, Sanborn, Adam N., 2017. Temporal variability in moral value judgement. CogSci 2017 : 39th Annual Meeting of the Cognitive Science Society, London, UK, 26?29 Jul 2017, Published in CogSci 2017 Proceedings of the 39th Annual Meeting of the Cognitive Science Society London, UK, 26-29 July 2017, pp. 3285-3290
- Spicer, Jake, Sanborn, Adam N., 2017. A rational approach to stereotype change. CogSci 2017 : 39th Annual Meeting of the Cognitive Science Society, London, UK, 26?29 Jul 2017, Published in CogSci 2017 Proceedings of the 39th Annual Meeting of the Cognitive Science Society London, UK, 26-29 July 2017, pp. 1102-1103
- Tripp, James, Sanborn, Adam N., Stewart, Neil, Noguchi, Takao, 2015. Multiple strategies in conjunction and disjunction judgments : most people are normative part of the time. CogSci 2015 : 37th annual conference of the Cognitive Science Society, Pasadena, California, 22-25 Jul 2015, Published in Proceedings of the 37th Annual Meeting of the Cognitive Science Society. Austin, TX
- Noguchi, Takao, Sanborn, Adam N., Stewart, Neil, 2013. Non-parametric estimation of the individual's utility map. COGSCI 2013 : Thirty-fifth annual conference of the Cognitive Science Society, Berlin, Germany, 31 Jul - 3 Aug 2013, Published in Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society, pp. 3145-3150
- Sanborn, Adam N., Mansinghka, Vikash, Griffiths , Thomas L., 2009. A Bayesian framework for modeling intuitive dynamics. CogSci 2009: 31st Annual Meeting of the Cognitive Science Society, Amsterdam, Netherlands, 29 Jul - 1 Aug 2009
- Sanborn, Adam N., Silva, Ricardo, 2009. Belief propagation and locally Bayesian learning. CogSci 2009: 31st Annual Meeting of the Cognitive Science Society, Amsterdam, Netherlands, 29 Jul - 1 Aug 2009, Published in Proceedings of the 31st Annual Conference of the Cognitive Science Society
- Martin, Jason, Griffiths , Thomas L., Sanborn, Adam N., 2009. A walk through face space : affect classification using Markov chain Monte Carlo. CogSci 2009: 31st Annual Meeting of the Cognitive Science Society, Amsterdam, Netherlands, 29 Jul - 1 Aug 2009
- Sanborn, Adam N., Zhu, Jianqiao, Spicer, Jake, Chater, Nick, 2020. Sampling as a resource-rational constraint. Behavioral and Brain Sciences, Cambridge University Press
- Zhu, Jianqiao, Sanborn, Adam N., Chater, Nick, 2017. Mental sampling in multimodal representations. University of Warwick
- 'Sanborn, Adam N., 'Tripp, James, 'Noguchi, Takao, 'Stewart, Neil, 2017. 'Data for Combination Rules in Information Integration. University of Warwick, Department of Psychology
- Sanborn, Adam N., Hills, Thomas Trenholm, Dougherty, Michael R., Thomas, Rick P., Yu, Erica C., Sprenger, Amber M., 2014. Reply to Rouder (2014) : good frequentist properties raise confidence. Psychonomic Bulletin & Review, Psychonomic Society, pp. 309-311
| Title | Funder | Award start | Award end |
|---|---|---|---|
| Searching for the Approximation Method used to Perform rationaL inference by INdividuals and Groups - SAMPLING. ERC-CoG | European Research Council | 01 Apr 2019 | 31 Mar 2024 |
| The macroeconomics of the sampling brain - extension | ESRC | 01 Apr 2020 | 31 Dec 2020 |
| Macroeconomic Implications of the Sampling Brain | ESRC | 01 Jan 2019 | 30 Nov 2019 |
| Combination Rules in Information Integration | ESRC | 01 Jul 2013 | 30 Jun 2016 |