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 Psychological Science, 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 previously served as Associate Editor for the Journal of Experimental Psychology: Learning, Memory, and Cognition and is now a Section Editor for the Open Encyclopedia of Cognitive Science.

Representative publications:

• Spicer, J., Zhu, J., Chater, N., & Sanborn, A. N. (2024). How do people predict a random walk? Lessons for models of human cognition. Psychological Review, 131(5), 1069–1113. https://dx.doi.org/10.1037/rev0000493.
• Zhu, J.-Q., Sundh, J., Spicer, J., Chater, N., & Sanborn, A.N. (2024). The Autocorrelated Bayesian Sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times. Psychological Review, 131(2), 456–493. https://doi.org/10.1037/rev0000427
• Castillo, L., León-Villagrá, P., Chater, N., & Sanborn, A. N. (2024). Explaining the flaws in human random generation as local sampling with momentum. PLoS Computational Biology, 20(1): e1011739. https://doi.org/10.1371/journal.pcbi.1011739
• Sundh, J., Zhu, J.-Q., Chater, N., & Sanborn, A.N. (2023). A unified explanation of variability and bias in human probability judgments: How computational noise explains the mean-variance signature. Journal of Experimental Psychology: General, 152(10), 2842–2860. https://dx.doi.org/10.1037/xge0001414
• 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. https://doi.org/10.1037/rev0000310