2017

Zhu, Jianqiao, Sanborn, Adam N. and Chater, Nick (2017) Mental sampling in multimodal representations. Working Paper. Coventry: University of Warwick. (Unpublished)

Badham, Stephen P., Sanborn, Adam N. and Maylor, Elizabeth A. (2017) Deficits in category learning in older adults : rule-based versus clustering accounts. Psychology and Aging, 32 (5). pp. 473-488. doi:10.1037/pag0000183

Ramlee, Fatanah, Sanborn, Adam N. and Tang, Nicole K. Y. (2017) What sways people's judgement of sleep quality? A quantitative choice-making study with good and poor sleepers. Sleep, 40 (7). Sleep zsx091.. doi:10.1093/sleep/zsx091

Sanborn, Adam N. and Chater, Nick (2017) The sampling brain. Trends in Cognitive Sciences, 21 (7). pp. 492-493.

Sanborn, Adam N., Tripp, James, Noguchi, Takao and Stewart, Neil (2017) Data for Combination Rules in Information Integration. [Dataset]

Surdina, Alexandra and Sanborn, Adam N. (2017) Temporal variability in moral value judgement. In: CogSci 2017 : 39th Annual Meeting of the Cognitive Science Society, London, UK, 26–29 Jul 2017. Published in: Proceedings of the 39th Annual Conference of the Cognitive Science Society (In Press)

Lloyd, Kevin, Sanborn, Adam N., Leslie, David and Lewandowsky, Stephan (2017) Why does higher working memory capacity help you learn? In: CogSci 2017, London, 26-29 Jul 2017. Published in: Proceedings of the 39th Annual Conference of the Cognitive Science Society (In Press)

Spicer, Jake and Sanborn, Adam N. (2017) A rational approach to stereotype change. In: CogSci 2017 : 39th Annual Meeting of the Cognitive Science Society, London, UK, 26–29 Jul 2017. Published in: Proceedings of the 39th Annual Conference of the Cognitive Science Society (In Press)

2016

Sanborn, Adam N. and Chater, Nick (2016) Bayesian brains without probabilities. Trends in Cognitive Sciences, 20 (12). pp. 883-893. doi:10.1016/j.tics.2016.10.003

Scholten, Marc, Read, Daniel and Sanborn, Adam N. (2016) Cumulative weighing of time in intertemporal tradeoffs. Journal of Experimental Psychology: General, 145 (9). pp. 1177-1205.

Sanborn, Adam N. and Beierholm, Ulrik R. (2016) Fast and accurate learning when making discrete numerical estimates. PLoS Computational Biology, 12 (4). e1004859. doi:10.1371/journal.pcbi.1004859

2015

Sanborn, Adam N. (2015) Types of approximation for probabilistic cognition : sampling and variational. Brain and cognition . doi:10.1016/j.bandc.2015.06.008 (In Press)

Sanborn, Adam N. (2015) Bayesian models of cognition. In: Jaeger, Dieter and Jung, Ranu, (eds.) Encyclopedia of Computational Neuroscience. Springer, pp. 624-625. ISBN 9781461466758

Sanborn, Adam N. and Griffiths, Thomas L. (2015) Exploring the structure of mental representations by implementing computer algorithms with people. In: Raaijmakers, J. G. W. and Criss, A. H. and Goldstone, R. L. and Nosofsky, R. M. and 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.

2014

Sanborn, Adam N. (2014) Testing Bayesian and heuristic predictions of mass judgments of colliding objects. Frontiers in Psychology, Volume 5 . Article number 938. doi:10.3389/fpsyg.2014.00938

Sanborn, Adam N. and Hills, Thomas Trenholm (2014) The frequentist implications of optional stopping on Bayesian hypothesis tests. Psychonomic Bulletin & Review , Volume 21 (Number 2). pp. 283-300. doi:10.3758/s13423-013-0518-9

Tang, Nicole K. Y. and 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, Volume 9 (Number 3). pp. 1-9. Article number e92158. doi:10.1371/journal.pone.0092158

Sanborn, Adam N., Hills, Thomas Trenholm, Dougherty, Michael R., Thomas, Rick P., Yu, Erica C. and Sprenger, Amber M. (2014) Reply to Rouder (2014) : good frequentist properties raise confidence. Psychonomic Bulletin & Review , Volume 21 (Number 2). pp. 309-311. doi:10.3758/s13423-014-0607-4

Scholten, Marc, Read, Daniel and Sanborn, Adam N. (2014) Weighing outcomes by time or against time? Evaluation rules in intertemporal choice. Cognitive Science . doi:10.1111/cogs.12104 (In Press)

2013

Sanborn, Adam N., Mansinghka, Vikash K. and Griffiths, Thomas L. (2013) Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120 (2). pp. 411-437. doi:10.1037/a0031912

Sanborn, Adam N. and Silva, Ricardo (2013) Constraining bridges between levels of analysis : a computational justification for locally Bayesian learning. Journal of Mathematical Psychology, Volume 57 (Number 3-4). pp. 94-106. doi:10.1016/j.jmp.2013.05.002

Noguchi, Takao, Sanborn, Adam N. and Stewart, Neil (2013) Non-parametric estimation of the individual's utility map. In: 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. ISBN 9780976831891.

2012

Griffiths, T. L., Vul, E. and Sanborn, Adam N. (2012) Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, Volume 21 (Number 4). pp. 263-268. doi:10.1177/0963721412447619

Tang, Nicole K. Y., Goodchild, Claire E., Sanborn, Adam N., Howard, Jonathan and 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. doi:10.5665/sleep.1830

Martin, J. B., Griffiths, Thomas L. and 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. doi:10.1111/j.1551-6709.2011.01204.x

2011

Griffiths, Thomas L., Sanborn, Adam N., Canini, Kevin R., Navarro, Daniel J. and Tenenbaum, Joshua B. (2011) Nonparametric Bayesian models of categorization. In: Pothos, Emmanuel M. and Wills, Andy J. , (eds.) Formal Approaches in Categorization. Cambridge: Cambridge University Press, pp. 173-198. ISBN 9780521190480

Sanborn, Adam N. and Dayan, P. (2011) Optimal decisions for contrast discrimination. Journal of Vision, Vol.11 (No.14). Article no. 9. doi:10.1167/11.14.9

2010

Shi, Lei, Griffiths, Thomas L., Feldman, Naomi H. and Sanborn, Adam N. (2010) Exemplar models as a mechanism for performing Bayesian inference. Psychonomic bulletin & review, Vol.17 (No.4). pp. 443-64. doi:10.3758/PBR.17.4.443

Sanborn, Adam N., Griffiths, Thomas L. and Navarro, Daniel J (2010) Rational approximations to rational models : alternative algorithms for category learning. Psychological Review, Vol.117 (No.4). pp. 1144-67. doi:10.1037/a0020511

Sanborn, Adam N., Griffiths, Thomas L. and Shiffrin, Richard M. (2010) Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, Vol.60 (No.2). pp. 63-106. doi:10.1016/j.cogpsych.2009.07.001

2009

Sanborn, Adam N., Mansinghka, Vikash and Griffiths , Thomas L. (2009) A Bayesian framework for modeling intuitive dynamics. In: CogSci 2009: 31st Annual Meeting of the Cognitive Science Society, Amsterdam, Netherlands, 29 Jul - 1 Aug 2009

Sanborn, Adam N. and Silva, Ricardo (2009) Belief propagation and locally Bayesian learning. In: 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

Heller, Katherine, Sanborn, Adam N. and Chater, Nick (2009) Hierarchical learning of dimensional biases in human categorization. In: Lafferty , J. Clayton (James Clayton), 1928- and 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. ISBN 9781615679119

Griffiths, Thomas L., Canini, Kevin R., Sanborn, Adam N. and Navarro, Daniel J. (2009) Unifying rational models of categorization via the hierarchical Dirichlet process. In: Proceedings of the 29th Annual Conference of the Cognitive Science Society. Mahwah, N.J.: Lawrence Erlbaum, p. 323. ISBN 9781605605074

Martin, Jason, Griffiths , Thomas L. and Sanborn, Adam N. (2009) A walk through face space : affect classification using Markov chain Monte Carlo. In: CogSci 2009: 31st Annual Meeting of the Cognitive Science Society, Amsterdam, Netherlands, 29 Jul - 1 Aug 2009

2008

Griffiths, Thomas L., Sanborn, Adam N., Canini, Kevin R. and Navarro, Daniel J. (2008) Categorization as nonparametric Bayesian density estimation. In: Chater, Nick and Oaksford, Mike, (eds.) The probabilistic mind : prospects for Bayesian cognitive science. Oxford : Oxford University Press, pp. 303-328. ISBN 9780199216093

Sanborn, Adam N. and 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. ISBN 9781605603520

Cohen, Andrew L., Sanborn, Adam N. and Shiffrin, Richard M. (2008) Model evaluation using grouped or individual data. Psychonomic Bulletin & Review, Vol.15 (No.4). pp. 692-712. doi:10.3758/PBR.15.4.692

2006

Sanborn, Adam N., Griffiths, Thomas L. and 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. ISBN 9780976831822

2004

Sanborn, Adam N., Malmberg, Kenneth J. and Shiffrin, Richard M. (2004) High-level effects of masking on perceptual identification. Vision Research, Vol.44 (No.12). pp. 1427-1436. doi:10.1016/j.visres.2004.01.004

2003

Morrow, Daniel G., Ridolfo, Heather E., Menard, William E., Sanborn, Adam N., Stine-Morrow, Elizabeth A. L., Magnor, Cliff, Herman, Larry, Teller, Thomas and 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. doi:10.1037/0882-7974.18.2.268