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Adam N. Sanborn [14]Adam Sanborn [3]
  1.  40
    Rational approximations to rational models: Alternative algorithms for category learning.Adam N. Sanborn, Thomas L. Griffiths & Daniel J. Navarro - 2010 - Psychological Review 117 (4):1144-1167.
  2.  16
    The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments.Jian-Qiao Zhu, Adam N. Sanborn & Nick Chater - 2020 - Psychological Review 127 (5):719-748.
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  3.  18
    Reconciling intuitive physics and Newtonian mechanics for colliding objects.Adam N. Sanborn, Vikash K. Mansinghka & Thomas L. Griffiths - 2013 - Psychological Review 120 (2):411-437.
  4.  18
    A dilution effect without dilution: When missing evidence, not non-diagnostic evidence, is judged inaccurately.Adam N. Sanborn, Takao Noguchi, James Tripp & Neil Stewart - 2020 - Cognition 196 (C):104110.
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  5.  21
    Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
  6. Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Navarro & J. Daniel - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
     
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  7.  6
    REFRESH: A new approach to modeling dimensional biases in perceptual similarity and categorization.Adam N. Sanborn, Katherine Heller, Joseph L. Austerweil & Nick Chater - 2021 - Psychological Review 128 (6):1145-1186.
  8.  29
    Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice.Marc Scholten, Daniel Read & Adam Sanborn - 2014 - Cognitive Science 38 (3):399-438.
    Models of intertemporal choice draw on three evaluation rules, which we compare in the restricted domain of choices between smaller sooner and larger later monetary outcomes. The hyperbolic discounting model proposes an alternative-based rule, in which options are evaluated separately. The interval discounting model proposes a hybrid rule, in which the outcomes are evaluated separately, but the delays to those outcomes are evaluated in comparison with one another. The tradeoff model proposes an attribute-based rule, in which both outcomes and delays (...)
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  9.  11
    Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation.Kevin Lloyd, Adam Sanborn, David Leslie & Stephan Lewandowsky - 2019 - Cognitive Science 43 (12):e12805.
    Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct (...)
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  10. A Bayesian framework for modeling intuitive dynamics.Adam N. Sanborn, Vikash Mansinghka & Thomas L. Griffiths - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  11.  25
    The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.Jian-Qiao Zhu, Joakim Sundh, Jake Spicer, Nick Chater & Adam N. Sanborn - 2024 - Psychological Review 131 (2):456-493.
  12. Belief propagation and locally Bayesian learning.Adam N. Sanborn & Ricardo Silva - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 31.
     
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  13.  6
    Confirmation bias emerges from an approximation to Bayesian reasoning.Charlie Pilgrim, Adam Sanborn, Eugene Malthouse & Thomas T. Hills - 2024 - Cognition 245 (C):105693.
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  14.  18
    Testing Bayesian and heuristic predictions of mass judgments of colliding objects.Adam N. Sanborn - 2014 - Frontiers in Psychology 5.
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  15. Noise in cognition : bug or feature?Adam N. Sanborn, Jian-Qiao Zhu, Jake Spicer, Pablo León-Villagrá, Lucas Castillo, Johanna K. Falbén, Yun-Xiao Li, Aidan Tee & Nick Chater - forthcoming - .
    Noise in behavior is often viewed as a nuisance: while the mind aims to take the best possible action, it is let down by unreliability in the sensory and response systems. How researchers study cognition reflects this viewpoint – averaging over trials and participants to discover the deterministic relationships between experimental manipulations and their behavioral consequences, with noise represented as additive, often Gaussian, and independent. Yet a careful look at behavioral noise reveals rich structure that defies easy explanation. First, both (...)
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  16.  51
    Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories.Jay B. Martin, Thomas L. Griffiths & Adam N. Sanborn - 2012 - Cognitive Science 36 (1):150-162.
    Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure (...)
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  17.  5
    Clarifying the relationship between coherence and accuracy in probability judgments.Jian-Qiao Zhu, Philip W. S. Newall, Joakim Sundh, Nick Chater & Adam N. Sanborn - 2022 - Cognition 223 (C):105022.
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