| A Bayesian framework for modeling intuitive dynamics AN Sanborn, V Mansinghka, TL Griffiths Cognitive Science Society, 2009 | 21 | 2009 |
| A Bayesian Framework for Modeling Intuitive Dynamics T Griffiths, V Mansinghka, A Sanborn Proceedings of the Annual Meeting of the Cognitive Science Society 31 (31), 2009 | | 2009 |
| A dilution effect without dilution: When missing evidence, not non-diagnostic evidence, is judged inaccurately AN Sanborn, T Noguchi, J Tripp, N Stewart Cognition 196, 104110, 2020 | 7 | 2020 |
| A more rational model of categorization A Sanborn, T Griffiths, D Navarro LAWRENCEE, 2006 | 154 | 2006 |
| A rational approach to stereotype change J Spicer, AN Sanborn CogSci 2017 Proceedings of the 39th Annual Meeting of the Cognitive Science …, 2017 | 1 | 2017 |
| A unified explanation of variability and bias in human probability judgments: How computational noise explains the mean–variance signature. J Sundh, JQ Zhu, N Chater, A Sanborn Journal of Experimental Psychology: General, 2023 | 6* | 2023 |
| A walk through face space: affect classification using Markov chain Monte Carlo J Martin, TL Griffiths, AN Sanborn Cognitive Science Society, 2009 | | 2009 |
| A walk through face space: Affect classification using Markov chain Monte Carlo T Griffiths, J Martin, A Sanborn Proceedings of the Annual Meeting of the Cognitive Science Society 31 (31), 2009 | | 2009 |
| An introduction to psychologically plausible sampling schemes for approximating Bayesian inference JQ Zhu, N Chater, P León-Villagrá, J Spicer, J Sundh, A Sanborn Sampling in Judgment and Decision Making, 467, 2023 | 1 | 2023 |
| Anchoring and Repulsion in Judgments J Spicer, JQ Zhu, N Chater, A Sanborn OSF, 2022 | | 2022 |
| Approximate probabilistic inference TL Griffiths, AN Sanborn MIT Press, 2024 | | 2024 |
| Approximating Bayesian inference through internal sampling J Sundh, A Sanborn, JQ Zhu, J Spicer, P León-Villagrá, N Chater Cambridge University Press, 2023 | | 2023 |
| Avoiding Animal Suffering and Preserving Human Lives: Mind Perception and Speciesism Predict Differential Moral Judgments of Torture and Killing S Myers, J Preston, A Sanborn OSF, 0 | | |
| Bayesian brains without probabilities AN Sanborn, N Chater Trends in cognitive sciences 20 (12), 883-893, 2016 | 386 | 2016 |
| Bayesian Inference Causes Incoherence in Human Probability Judgments. JQ Zhu, A Sanborn, N Chater CogSci, 3617, 2019 | 3 | 2019 |
| Bayesian models of cognition AN Sanborn Springer, 2015 | | 2015 |
| Bedtime social media use, sleep, and affective wellbeing in young adults: an experience sampling study A Das‐Friebel, A Lenneis, A Realo, A Sanborn, NKY Tang, D Wolke, ... Journal of Child Psychology and Psychiatry 61 (10), 1138-1149, 2020 | 44 | 2020 |
| Belief propagation and locally Bayesian learning A Sanborn, R Silva Proceedings of the Annual Meeting of the Cognitive Science Society 31 (31), 2009 | 1 | 2009 |
| Better quality sleep promotes daytime physical activity in patients with chronic pain? A multilevel analysis of the within-person relationship NKY Tang, AN Sanborn PloS one 9 (3), e92158, 2014 | 118 | 2014 |
| Bridging levels of analysis for probabilistic models of cognition TL Griffiths, E Vul, AN Sanborn Current Directions in Psychological Science 21 (4), 263-268, 2012 | 265 | 2012 |
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton University在 princeton.edu 的电子邮件经过验证
