Adam N. Sanborn

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Data for Combination Rules in Information Integration

Takao Noguchi, Neil Stewart, Adam N. Sanborn, 2017 .

Formal Approaches in Categorization: Nonparametric Bayesian models of categorization

Thomas L. Griffiths, Joshua B. Tenenbaum, Kevin Robert Canini, 2011 .

Testing Bayesian and heuristic predictions of mass judgments of colliding objects

Adam N. Sanborn, 2014, Front. Psychol..

Exploring the structure of mental representations by implementing computer algorithms with people

Thomas L. Griffiths, Adam N. Sanborn, T. Griffiths, 2015 .

A More Rational Model of Categorization - eScholarship

Thomas L. Griffiths, Daniel J. Navarro, Adam N. Sanborn, 2006 .

Belief Propagation and Locally Bayesian Learning - eScholarship

Ricardo Silva, Adam N. Sanborn, 2009 .

A more rational model of categorization

Thomas L. Griffiths, Daniel J. Navarro, Adam N. Sanborn, 2006 .

Dataset to support grant: 'Combination Rules in Information Integration'

Takao Noguchi, Neil Stewart, Adam N. Sanborn, 2017 .

Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning

Ricardo Silva, Adam N. Sanborn, Ricardo Silva, 2013 .

Cognitive Variability Matches Speculative Price Dynamics

Nick Chater, Jian-Qiao Zhu, Jake Spicer, 2021 .

Nick Chater, Jian-Qiao Zhu, Adam N. Sanborn, 2021 .

Bridging Levels of Analysis for Probabilistic Models of Cognition

Adam N. Sanborn, T. Griffiths, E. Vul, 2012 .

Unifying rational models of categorization via the hierarchical Dirichlet process

Adam N. Sanborn, T. Griffiths, Tom Griffiths, 2019 .

Uncovering mental representations with Markov chain Monte Carlo

Adam N. Sanborn, R. Shiffrin, T. Griffiths, 2010, Cognitive Psychology.

Model evaluation using grouped or individual data

Adam N. Sanborn, R. Shiffrin, A. Cohen, 2008, Psychonomic bulletin & review.

A Bayesian framework for modeling intuitive dynamics

Vikash K. Mansinghka, Adam N. Sanborn, T. Griffiths, 2009 .

Categorization as nonparametric Bayesian density estimation

Adam N. Sanborn, T. Griffiths, K. Canini, 2008 .

Types of approximation for probabilistic cognition: Sampling and variational

Adam N. Sanborn, 2017, Brain and Cognition.

Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories

Thomas L. Griffiths, Adam Sanborn, Jay B. Martin, 2012, Cogn. Sci..

Uncovering mental representations with Markov chain

Adam N. Sanborn, R. Shiffrin, T. Griffiths, 2010 .

Reconciling intuitive physics and Newtonian mechanics for colliding objects.

Vikash K. Mansinghka, Vikash K Mansinghka, Adam N Sanborn, 2013, Psychological review.

Optimal decisions for contrast discrimination.

Adam N. Sanborn, P. Dayan, 2011, Journal of vision.

Probabilistic Models of Physical Reasoning

Jessica B. Hamrick, Kevin A. Smith, Adam N. Sanborn, 2020 .

The Sampling Brain

Adam N. Sanborn, N. Chater, 2017, Trends in Cognitive Sciences.

The frequentist implications of optional stopping on Bayesian hypothesis tests

Thomas T. Hills, Adam N. Sanborn, 2013, Psychonomic Bulletin & Review.

Computational Models of Intuitive Physics - eScholarship

Kenneth D. Forbus, Adam N. Sanborn, J. Tenenbaum, 2012 .

What Sways People’s Judgment of Sleep Quality? A Quantitative Choice-Making Study With Good and Poor Sleepers

Adam N. Sanborn, N. Tang, Fatanah Ramlee, 2017, Sleep.

Environmental support promotes expertise-based mitigation of age differences on pilot communication tasks.

Adam Sanborn, Adam N. Sanborn, Thomas L. Teller, 2003, Psychology and aging.

Deficits in Category Learning in Older Adults: Rule-Based Versus Clustering Accounts

Elizabeth A. Maylor, Adam N. Sanborn, E. Maylor, 2017, Psychology and aging.

High-level effects of masking on perceptual identification

Adam N. Sanborn, R. Shiffrin, K. Malmberg, 2004, Vision Research.

A dilution effect without dilution: When missing evidence, not non-diagnostic evidence, is judged inaccurately

Adam N. Sanborn, Takao Noguchi, Neil Stewart, 2019, Cognition.

Belief propagation and locally Bayesian learning

Adam N. Sanborn, Ricardo Silva, 2009 .

Bayesian Brains without Probabilities

Adam N. Sanborn, N. Chater, 2016, Trends in Cognitive Sciences.

Why Decisions Bias Perception: An Amortised Sequential Sampling Account

Jian-Qiao Zhu, Adam Sanborn, Nick Chater, 2019, CogSci.

TEMPORAL LINK BETWEEN PAIN AND SLEEP IN CHRONIC PAIN PATIENTS

Adam N. Sanborn, P. Salkovskis, N. Tang, 2012 .

Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion

Adam N. Sanborn, N. Chater, Jian-Qiao Zhu, 2022, Psychological science.

Judgement of sleep quality of the previous night changes as the day unfolds: A prospective experience sampling study

Adam N. Sanborn, N. Tang, P. D. W. Banks, 2022, Journal of sleep research.

The frequentist implications of optional stopping on Bayesian hypothesis tests

Thomas T. Hills, Adam N. Sanborn, 2013, Psychonomic Bulletin & Review.

Fast and Accurate Learning When Making Discrete Numerical Estimates

Ulrik R. Beierholm, Adam Sanborn, Ulrik R Beierholm, 2016, PLoS Comput. Biol..

Look-Ahead Monte Carlo with People

Thomas L. Griffiths, Charles Blundell, Adam Sanborn, 2012, CogSci.

Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people

Anne S Hsu, Jay B Martin, Adam N Sanborn, 2019, Behavior research methods.

Identifying representations of categories of discrete items using Markov chain Monte Carlo with People

Thomas L. Griffiths, Adam Sanborn, Jay B. Martin, 2012, CogSci.

Markov Chain Monte Carlo with People

Thomas L. Griffiths, Adam Sanborn, Adam N. Sanborn, 2007, NIPS.

Cumulative Weighing of Time in Intertemporal Tradeoffs

Adam N. Sanborn, D. Read, M. Scholten, 2016, Journal of experimental psychology. General.

REFRESH: A New Approach to Modeling Dimensional Biases in Perceptual Similarity and Categorization

Joseph L. Austerweil, Adam N. Sanborn, N. Chater, 2021, Psychological review.

Rational approximations to rational models: alternative algorithms for category learning.

Adam N Sanborn, Thomas L Griffiths, Daniel J Navarro, 2010, Psychological review.

Categorization as nonparametric Bayesian density estimation 1 Running head: CATEGORIZATION AS NONPARAMETRIC BAYESIAN DENSITY ESTIMATION Categorization as nonparametric Bayesian density estimation

Adam N. Sanborn, T. Griffiths, 2007 .

Deciphering the temporal link between pain and sleep in a heterogeneous chronic pain patient sample: a multilevel daily process study.

Adam N. Sanborn, P. Salkovskis, N. Tang, 2012, Sleep.

Exemplar models as a mechanism for performing Bayesian inference

Adam N Sanborn, Thomas L Griffiths, Naomi H Feldman, 2010, Psychonomic bulletin & review.

Non-parametric estimation of the individual's utility map

Takao Noguchi, Neil Stewart, Adam Sanborn, 2013, CogSci.

What does the mind learn? A comparison of human and machine learning representations

Adam N Sanborn, Jake Spicer, Adam N. Sanborn, 2019, Current Opinion in Neurobiology.

Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation

Stephan Lewandowsky, Adam Sanborn, Adam N. Sanborn, 2019, Cogn. Sci..

Mental Sampling in Multimodal Representations

Nick Chater, Jian-Qiao Zhu, Adam Sanborn, 2017, NeurIPS.

Understanding the structure of cognitive noise

Nick Chater, Adam N Sanborn, Jian-Qiao Zhu, 2021, PLoS Comput. Biol..

Temporal variability in moral value judgement

Adam Sanborn, Adam N. Sanborn, Alexandra Surdina, 2017, CogSci.

Using Occam’s razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements

Ulrik R. Beierholm, Adam Sanborn, Ulrik R Beierholm, 2020, Cognitive Psychology.

Why Does Higher Working Memory Capacity Help You Learn?

Stephan Lewandowsky, Adam Sanborn, David Leslie, 2017, CogSci.

Hierarchical Learning of Dimensional Biases in Human Categorization

Nick Chater, Katherine A. Heller, Adam Sanborn, 2009, NIPS.

Better Quality Sleep Promotes Daytime Physical Activity in Patients with Chronic Pain? A Multilevel Analysis of the Within-Person Relationship

Adam N. Sanborn, N. Tang, 2014, PloS one.

Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice

Daniel Read, Adam Sanborn, Marc Scholten, 2014, Cogn. Sci..

Sampling as the Human Approximation to Probabilistic Inference

Adam N. Sanborn, N. Chater, Pablo León-Villagrá, 2021, Human-Like Machine Intelligence.

The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments

Nick Chater, Adam N Sanborn, Jian-Qiao Zhu, 2020, Psychological review.

Exploring the Representation of Linear Functions

Christopher G. Lucas, Pablo León-Villagrá, Christopher Lucas, 2019, CogSci.

A unified explanation of variability and bias in human probability judgments: How computational noise explains the mean-variance signature.

Adam N. Sanborn, N. Chater, Joakim Sundh, 2023, Journal of experimental psychology. General.

Probabilistic Biases Meet the Bayesian Brain

Adam N. Sanborn, N. Chater, Pablo León-Villagrá, 2020 .

Bedtime social media use, sleep, and affective wellbeing in young adults: an experience sampling study.

Adam N. Sanborn, A. Realo, A. von Mühlenen, 2020, Journal of child psychology and psychiatry, and allied disciplines.

Clarifying the relationship between coherence and accuracy in probability judgments

Adam N. Sanborn, N. Chater, P. Newall, 2022, Cognition.

The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments

Jian-Qiao Zhu, Adam Sanborn, Nicholas Chater, 2018, Psychological review.

Sampling as a resource-rational constraint

Nick Chater, Adam N Sanborn, Jake Spicer, 2020, Behavioral and Brain Sciences.

Local Sampling with Momentum Accounts for Human Random Sequence Generation

Adam N. Sanborn, N. Chater, Pablo León-Villagrá, 2021 .

Adam N. Sanborn, N. Chater, Joakim Sundh, 2023, Psychological review.

The mean-variance signature of Bayesian probability judgment

Adam N. Sanborn, N. Chater, Joakim Sundh, 2021 .

Cognition, Bayesian Models of

Adam Sanborn, Adam N. Sanborn, 2014, Encyclopedia of Computational Neuroscience.

Reply to Rouder (2014): Good frequentist properties raise confidence

Thomas T. Hills, Rick P. Thomas, Erica C. Yu, 2014, Psychonomic bulletin & review.

Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories

Adam N. Sanborn, Jay B. Martin, T. Griffiths, 2012, Cogn. Sci..

Using Occam’s razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements

Ulrik R Beierholm, Adam N. Sanborn, Jake Spicer, 2020, Cognitive Psychology.

Intuitive physics as probabilistic inference

Jessica B. Hamrick, Adam N. Sanborn, Tobias Gerstenberg, 2022 .

UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

Adam N. Sanborn, Jian-Qiao Zhu, Adam N. Sanborn, 2022 .

The influence of sleep on subjective well-being: An experience sampling study.

Adam N. Sanborn, A. Realo, A. von Mühlenen, 2023, Emotion.

Bridging Levels of Analysis for Probabilistic Models of Cognition

Adam N. Sanborn, T. Griffiths, E. Vul, 2012 .