77 found
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  1. 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.
    Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...)
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  2.  70
    Word learning as Bayesian inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
  3.  29
    A counterfactual simulation model of causal judgments for physical events.Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado & Joshua B. Tenenbaum - 2021 - Psychological Review 128 (5):936-975.
  4. Theory-based Bayesian models of inductive learning and reasoning.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
  5.  31
    Topics in semantic representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
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  6. Generalization, similarity, and bayesian inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
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  7.  95
    Action understanding as inverse planning.Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum - 2009 - Cognition 113 (3):329-349.
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  8. A tutorial introduction to Bayesian models of cognitive development.Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu - 2011 - Cognition 120 (3):302-321.
  9.  76
    One and Done? Optimal Decisions From Very Few Samples.Edward Vul, Noah Goodman, Thomas L. Griffiths & Joshua B. Tenenbaum - 2014 - Cognitive Science 38 (4):599-637.
    In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...)
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  10.  40
    Inferring causal networks from observations and interventions.Mark Steyvers, Joshua B. Tenenbaum, Eric-Jan Wagenmakers & Ben Blum - 2003 - Cognitive Science 27 (3):453-489.
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  11.  54
    The logical primitives of thought: Empirical foundations for compositional cognitive models.Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman - 2016 - Psychological Review 123 (4):392-424.
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  12.  27
    Theory-based causal induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  13.  38
    The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...)
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  14.  27
    A Rational Analysis of Rule-Based Concept Learning.Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman & Thomas L. Griffiths - 2008 - Cognitive Science 32 (1):108-154.
  15.  48
    The learnability of abstract syntactic principles.Amy Perfors, Joshua B. Tenenbaum & Terry Regier - 2011 - Cognition 118 (3):306-338.
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  16.  22
    Structured statistical models of inductive reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  17.  69
    A critical period for second language acquisition: Evidence from 2/3 million English speakers.Joshua K. Hartshorne, Joshua B. Tenenbaum & Steven Pinker - 2018 - Cognition 177 (C):263-277.
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  18. The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective.Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum & James F. Woodward - 2010 - Philosophy of Science 77 (2):172-200.
    Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher‐level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that (...)
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  19.  49
    Sketches from a Design Process: Creative Cognition Inferred From Intermediate Products.Robert L. Goldstone, Steven A. Sloman, David A. Lagnado, Mark Steyvers, Joshua B. Tenenbaum, Saskia Jaarsveld, Cees van Leeuwen, Murray Shanahan, Terry Dartnall & Simon Dennis - 2005 - Cognitive Science 29 (1):79-101.
    Novice designers produced a sequence of sketches while inventing a logo for a novel brand of soft drink. The sketches were scored for the presence of specific objects, their local features and global composition. Self‐assessment scores for each sketch and art critics' scores for the end products were collected. It was investigated whether the design evolves in an essentially random fashion or according to an overall heuristic. The results indicated a macrostructure in the evolution of the design, characterized by two (...)
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  20.  77
    Modeling human performance in statistical word segmentation.Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum - 2010 - Cognition 117 (2):107-125.
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  21.  14
    The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...)
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  22.  30
    The structure and dynamics of scientific theories: a hierarchical Bayesian perspective.Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum & James F. Woodward - 2010 - Philosophy of Science 77 (2):172-200.
    Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘para- digms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher-level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea (...)
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  23.  34
    Learning a theory of causality.Noah D. Goodman, Tomer D. Ullman & Joshua B. Tenenbaum - 2011 - Psychological Review 118 (1):110-119.
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  24.  70
    Bootstrapping in a language of thought: A formal model of numerical concept learning.Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman - 2012 - Cognition 123 (2):199-217.
  25.  24
    Bayesian collective learning emerges from heuristic social learning.P. M. Krafft, Erez Shmueli, Thomas L. Griffiths, Joshua B. Tenenbaum & Alex “Sandy” Pentland - 2021 - Cognition 212 (C):104469.
  26.  8
    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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  27.  35
    Lucky or clever? From expectations to responsibility judgments.Tobias Gerstenberg, Tomer D. Ullman, Jonas Nagel, Max Kleiman-Weiner, David A. Lagnado & Joshua B. Tenenbaum - 2018 - Cognition 177 (C):122-141.
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  28.  24
    Children’s understanding of the costs and rewards underlying rational action.Julian Jara-Ettinger, Hyowon Gweon, Joshua B. Tenenbaum & Laura E. Schulz - 2015 - Cognition 140 (C):14-23.
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  29.  33
    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 (C):104890.
  30.  21
    Learning a commonsense moral theory.Max Kleiman-Weiner, Rebecca Saxe & Joshua B. Tenenbaum - 2017 - Cognition 167 (C):107-123.
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  31.  31
    Going beyond the evidence: Abstract laws and preschoolers’ responses to anomalous data.Laura E. Schulz, Noah D. Goodman, Joshua B. Tenenbaum & Adrianna C. Jenkins - 2008 - Cognition 109 (2):211-223.
  32.  45
    Too Many Cooks: Bayesian Inference for Coordinating Multi‐Agent Collaboration.Sarah A. Wu, Rose E. Wang, James A. Evans, Joshua B. Tenenbaum, David C. Parkes & Max Kleiman-Weiner - 2021 - Topics in Cognitive Science 13 (2):414-432.
    Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. (...)
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  33.  10
    The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...)
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  34. Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
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  35.  8
    “Structured statistical models of inductive reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
  36.  62
    The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science.Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum - 2011 - Behavioral and Brain Sciences 34 (4):194-196.
    If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.
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  37.  61
    Three ideal observer models for rule learning in simple languages.Michael C. Frank & Joshua B. Tenenbaum - 2011 - Cognition 120 (3):360-371.
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  38.  84
    Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...)
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  39.  38
    From mere coincidences to meaningful discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  40.  17
    A probabilistic model of visual working memory: Incorporating higher order regularities into working memory capacity estimates.Timothy F. Brady & Joshua B. Tenenbaum - 2013 - Psychological Review 120 (1):85-109.
  41.  35
    A probabilistic model of theory formation.Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths - 2010 - Cognition 114 (2):165-196.
  42.  44
    Intuitive theories as grammars for causal inference.Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 301--322.
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  43.  11
    Inferring mass in complex scenes by mental simulation.Jessica B. Hamrick, Peter W. Battaglia, Thomas L. Griffiths & Joshua B. Tenenbaum - 2016 - Cognition 157 (C):61-76.
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  44.  44
    Inductive reasoning about causally transmitted properties.Patrick Shafto, Charles Kemp, Elizabeth Baraff Bonawitz, John D. Coley & Joshua B. Tenenbaum - 2008 - Cognition 109 (2):175-192.
  45.  20
    A probabilistic model of cross-categorization.Patrick Shafto, Charles Kemp, Vikash Mansinghka & Joshua B. Tenenbaum - 2011 - Cognition 120 (1):1-25.
  46.  42
    Two proposals for causal grammars.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 323--345.
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  47.  68
    Probabilistic models of cognition: where next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  48. Learning causal schemata.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2007 - In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. pp. 389--394.
     
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  49.  36
    Dynamical Causal Learning.David Danks, Thomas L. Griffiths & Joshua B. Tenenbaum - unknown
    Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets, and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.
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  50.  29
    The Emergence of Organizing Structure in Conceptual Representation.Brenden M. Lake, Neil D. Lawrence & Joshua B. Tenenbaum - 2018 - Cognitive Science 42 (S3):809-832.
    Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc.. Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we (...)
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