22 found
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  1.  61
    Building Machines That Learn and Think Like People.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
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  2.  17
    Model-Based Influences on Humans' Choices and Striatal Prediction Errors.Nathaniel D. Daw, Samuel J. Gershman, Ben Seymour, Peter Dayan & Raymond J. Dolan - 2011 - Neuron 69 (6):1204-1215.
    The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could (...)
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  3.  8
    Retrospective Revaluation in Sequential Decision Making: A Tale of Two Systems.Samuel J. Gershman, Arthur B. Markman & A. Ross Otto - 2014 - Journal of Experimental Psychology: General 143 (1):182-194.
  4.  7
    Structured Event Memory: A Neuro-Symbolic Model of Event Cognition.Nicholas T. Franklin, Kenneth A. Norman, Charan Ranganath, Jeffrey M. Zacks & Samuel J. Gershman - 2020 - Psychological Review 127 (3):327-361.
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  5.  20
    Context, Learning, and Extinction.Samuel J. Gershman, David M. Blei & Yael Niv - 2010 - Psychological Review 117 (1):197-209.
  6.  6
    A Theory of Learning to Infer.Ishita Dasgupta, Eric Schulz, Joshua B. Tenenbaum & Samuel J. Gershman - 2020 - Psychological Review 127 (3):412-441.
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  7.  6
    Moral Dynamics: Grounding Moral Judgment in Intuitive Physics and Intuitive Psychology.Felix A. Sosa, Tomer Ullman, Joshua B. Tenenbaum, Samuel J. Gershman & Tobias Gerstenberg - 2021 - Cognition 217:104890.
  8.  12
    Remembrance of Inferences Past: Amortization in Human Hypothesis Generation.Ishita Dasgupta, Eric Schulz, Noah D. Goodman & Samuel J. Gershman - 2018 - Cognition 178:67-81.
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  9.  10
    Deconstructing the Human Algorithms for Exploration.Samuel J. Gershman - 2018 - Cognition 173:34-42.
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  10.  4
    Origin of Perseveration in the Trade-Off Between Reward and Complexity.Samuel J. Gershman - 2020 - Cognition 204:104394.
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  11.  13
    Explaining Compound Generalization in Associative and Causal Learning Through Rational Principles of Dimensional Generalization.Fabian A. Soto, Samuel J. Gershman & Yael Niv - 2014 - Psychological Review 121 (3):526-558.
  12.  20
    Discovering Social Groups Via Latent Structure Learning.Tatiana Lau, Hillard T. Pouncy, Samuel J. Gershman & Mina Cikara - 2018 - Journal of Experimental Psychology: General 147 (12):1881-1891.
  13.  12
    Integrating Models of Interval Timing and Reinforcement Learning.Elijah A. Petter, Samuel J. Gershman & Warren H. Meck - 2018 - Trends in Cognitive Sciences 22 (10):911-922.
  14.  5
    Memory as a Computational Resource.Ishita Dasgupta & Samuel J. Gershman - 2021 - Trends in Cognitive Sciences 25 (3):240-251.
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  15.  10
    Learning the Structure of Social Influence.Samuel J. Gershman, Hillard Thomas Pouncy & Hyowon Gweon - 2017 - Cognitive Science 41 (S3).
    We routinely observe others’ choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals, such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic relationship is part of a more complex social structure involving multiple social groups that are not directly observable. Here we suggest that human learners go beyond dyadic similarities (...)
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  16.  7
    Decision by Sampling Implements Efficient Coding of Psychoeconomic Functions.Rahul Bhui & Samuel J. Gershman - 2018 - Psychological Review 125 (6):985-1001.
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  17.  56
    Novelty and Inductive Generalization in Human Reinforcement Learning.Samuel J. Gershman & Yael Niv - 2015 - Topics in Cognitive Science 7 (3):391-415.
    In reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and (...)
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  18.  25
    Ingredients of Intelligence: From Classic Debates to an Engineering Roadmap.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
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  19. Heuristics From Bounded Meta-Learned Inference.Marcel Binz, Samuel J. Gershman, Eric Schulz & Dominik Endres - forthcoming - Psychological Review.
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  20.  14
    Bayesian Belief Updating After a Replication Experiment.Alex O. Holcombe & Samuel J. Gershman - 2018 - Behavioral and Brain Sciences 41.
  21.  2
    Analyzing Machine‐Learned Representations: A Natural Language Case Study.Ishita Dasgupta, Demi Guo, Samuel J. Gershman & Noah D. Goodman - 2020 - Cognitive Science 44 (12).
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  22.  3
    What Is the Model in Model‐Based Planning?Thomas Pouncy, Pedro Tsividis & Samuel J. Gershman - 2021 - Cognitive Science 45 (1):e12928.
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