| Rational approximations to rational models: alternative algorithms for category learning. AN Sanborn, TL Griffiths, DJ Navarro Psychological review 117 (4), 1144, 2010 | 477 | 2010 |
| Bayesian brains without probabilities AN Sanborn, N Chater Trends in cognitive sciences 20 (12), 883-893, 2016 | 304 | 2016 |
| 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 | 249 | 2012 |
| Deciphering the temporal link between pain and sleep in a heterogeneous chronic pain patient sample: a multilevel daily process study NKY Tang, CE Goodchild, AN Sanborn, J Howard, PM Salkovskis Sleep 35 (5), 675-687, 2012 | 220 | 2012 |
| Reconciling intuitive physics and Newtonian mechanics for colliding objects. AN Sanborn, VK Mansinghka, TL Griffiths Psychological review 120 (2), 411, 2013 | 200 | 2013 |
| Exemplar models as a mechanism for performing Bayesian inference L Shi, TL Griffiths, NH Feldman, AN Sanborn Psychonomic bulletin & review 17 (4), 443-464, 2010 | 179 | 2010 |
| A more rational model of categorization A Sanborn, T Griffiths, D Navarro LAWRENCEE, 2006 | 152 | 2006 |
| Model evaluation using grouped or individual data AL Cohen, AN Sanborn, RM Shiffrin Psychonomic Bulletin & Review 15 (4), 692-712, 2008 | 127 | 2008 |
| Unifying rational models of categorization via the hierarchical Dirichlet process T Griffiths, K Canini, A Sanborn, D Navarro Psychology Press, 2007 | 116 | 2007 |
| 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 | 108 | 2014 |
| Uncovering mental representations with Markov chain Monte Carlo AN Sanborn, TL Griffiths, RM Shiffrin Cognitive psychology 60 (2), 63-106, 2010 | 96 | 2010 |
| Environmental support promotes expertise-based mitigation of age differences on pilot communication tasks. DG Morrow, HE Ridolfo, WE Menard, A Sanborn, EAL Stine-Morrow, ... Psychology and Aging 18 (2), 268, 2003 | 92 | 2003 |
| The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments. JQ Zhu, AN Sanborn, N Chater Psychological review 127 (5), 719, 2020 | 87 | 2020 |
| Categorization as nonparametric Bayesian density estimation TL Griffiths, AN Sanborn, KR Canini, DJ Navarro The probabilistic mind: Prospects for Bayesian cognitive science, 303-328, 2008 | 71 | 2008 |
| Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice M Scholten, D Read, A Sanborn Cognitive science 38 (3), 399-438, 2014 | 69 | 2014 |
| The frequentist implications of optional stopping on Bayesian hypothesis tests AN Sanborn, TT Hills Psychonomic bulletin & review 21, 283-300, 2014 | 69 | 2014 |
| Markov chain Monte Carlo with people A Sanborn, T Griffiths Advances in neural information processing systems 20, 2007 | 59 | 2007 |
| What sways people’s judgment of sleep quality? a quantitative choice-making study with good and poor sleepers F Ramlee, AN Sanborn, NKY Tang Sleep 40 (7), 2017 | 57 | 2017 |
| Types of approximation for probabilistic cognition: Sampling and variational AN Sanborn Brain and cognition 112, 98-101, 2017 | 44 | 2017 |
| What does the mind learn? A comparison of human and machine learning representations J Spicer, AN Sanborn Current opinion in neurobiology 55, 97-102, 2019 | 39 | 2019 |
