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2020 – today
- 2024
[j6]Lucas Castillo
, Pablo León-Villagrá, Nick Chater, Adam Sanborn:
Explaining the flaws in human random generation as local sampling with momentum. PLoS Comput. Biol. 20(1) (2024)- 2023
- [c23]Pablo León-Villagrá, Lucas Castillo, Nick Chater, Adam Sanborn:
The Impact of Production Rates on Sequential Statistics and Distributional Properties in Random Generation. CogSci 2023 - [c22]Jian-Qiao Zhu, Adam Sanborn, Nick Chater, Tom Griffiths:
Computation-Limited Bayesian Updating. CogSci 2023 - 2022
- [j5]Jian-Qiao Zhu, Pablo León-Villagrá, Nick Chater, Adam N. Sanborn:
Understanding the structure of cognitive noise. PLoS Comput. Biol. 18(8) (2022) - [c21]Pablo León-Villagrá, Lucas Castillo, Nick Chater, Adam Sanborn:
Eliciting Human Beliefs using Random Generation. CogSci 2022 - [p1]Adam Sanborn, Jian-Qiao Zhu, Jake Spicer, Joakim Sundh, Pablo León-Villagrá, Nick Chater:
Sampling as the Human Approximation to Probabilistic Inference. Human-Like Machine Intelligence 2022: 430-448 - 2021
- [c20]Lucas Castillo, Pablo León-Villagrá, Nicholas Chater, Adam Sanborn:
Local Sampling with Momentum Accounts for Human Random Sequence Generation. CogSci 2021 - [c19]Pablo León-Villagrá, Nicholas Chater, Adam Sanborn:
Sampling Associations with (Un)related Suggestions. CogSci 2021 - 2020
- [c18]Joakim Sundh, Jian-Qiao Zhu, Nicholas Chater, Adam Sanborn:
How many instances come to mind when making probability estimates? CogSci 2020
2010 – 2019
- 2019
- [j4]Kevin Lloyd, Adam Sanborn, David S. Leslie, Stephan Lewandowsky:
Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation. Cogn. Sci. 43(12) (2019) - [c17]Pablo León-Villagrá, Verena Klar, Adam Sanborn, Chris Lucas:
Exploring the Representation of Linear Functions. CogSci 2019: 2105-2111 - [c16]Jian-Qiao Zhu, Adam Sanborn, Nick Chater:
Why Decisions Bias Perception: An Amortised Sequential Sampling Account. CogSci 2019: 3220-3226 - [c15]Jake Spicer, Adam Sanborn, Ulrik R. Beierholm:
Using Occam's razor and Bayesian modelling to compare discrete and continuous representations in numerostiy judgements. CogSci 2019: 3578 - [c14]Jian-Qiao Zhu, Adam Sanborn, Nicholas Chater:
Bayesian Inference Causes Incoherence in Human Probability Judgments. CogSci 2019: 3617 - 2018
- [c13]Jian-Qiao Zhu, Adam Sanborn, Nick Chater:
Mental Sampling in Multimodal Representations. NeurIPS 2018: 5753-5764 - 2017
- [c12]Kevin Lloyd, Adam Sanborn, David S. Leslie, Stephan Lewandowsky:
Why Does Higher Working Memory Capacity Help You Learn? CogSci 2017 - [c11]Jake Spicer, Adam Sanborn:
A Rational Approach to Stereotype Change. CogSci 2017 - [c10]Alexandra Surdina, Adam Sanborn:
Temporal variability in moral value judgement. CogSci 2017 - [i1]Jian-Qiao Zhu, Adam N. Sanborn, Nick Chater:
Mental Sampling in Multimodal Representations. CoRR abs/1710.05219 (2017) - 2016
- [j3]Adam N. Sanborn, Ulrik R. Beierholm:
Fast and Accurate Learning When Making Discrete Numerical Estimates. PLoS Comput. Biol. 12(4) (2016) - [c9]James Tripp, Adam Sanborn, Neil Stewart, Takao Noguchi:
Choosing Poorly: Reward-Induced Strategy Shifts in Estimating the Probabilities of Conjunctions and Disjunctions. CogSci 2016 - 2015
- [c8]Adam Sanborn, Takao Noguchi, James Tripp, Neil Stewart:
Inference, Not Dilution in the Dilution Effect. CogSci 2015 - [c7]James Tripp, Adam Sanborn, Neil Stewart, Takao Noguchi:
Multiple Strategies in Conjunction and Disjunction Judgments: Most People are Normative Part of the Time. CogSci 2015 - 2014
- [j2]Marc Scholten, Daniel Read, Adam Sanborn:
Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice. Cogn. Sci. 38(3): 399-438 (2014) - [r1]Adam N. Sanborn:
Cognition, Bayesian Models of. Encyclopedia of Computational Neuroscience 2014 - 2013
- [c6]Takao Noguchi, Adam Sanborn, Neil Stewart:
Non-parametric estimation of the individual's utility map. CogSci 2013 - 2012
- [j1]Jay B. Martin, Thomas L. Griffiths, Adam Sanborn:
Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cogn. Sci. 36(1): 150-162 (2012) - [c5]Peter W. Battaglia, Tomer D. Ullman, Joshua B. Tenenbaum, Adam Sanborn, Kenneth D. Forbus, Tobias Gerstenberg, David A. Lagnado:
Computational Models of Intuitive Physics. CogSci 2012 - [c4]Charles Blundell, Adam Sanborn, Thomas L. Griffiths:
Look-Ahead Monte Carlo with People. CogSci 2012 - [c3]Anne S. Hsu, Jay B. Martin, Adam N. Sanborn, Thomas L. Griffiths:
Identifying representations of categories of discrete items using Markov chain Monte Carlo with People. CogSci 2012
2000 – 2009
- 2009
- [c2]Katherine A. Heller, Adam Sanborn, Nick Chater:
Hierarchical Learning of Dimensional Biases in Human Categorization. NIPS 2009: 727-735 - 2007
- [c1]Adam Sanborn, Thomas L. Griffiths:
Markov Chain Monte Carlo with People. NIPS 2007: 1265-1272
Coauthor Index
aka: Nicholas Chater
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