89 found
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  1. Probabilistic Models of Cognition: Exploring Representations and Inductive Biases.Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum - 2010 - Trends in Cognitive Sciences 14 (8):357-364.
  2.  22
    Resource-Rational Analysis: Understanding Human Cognition as the Optimal Use of Limited Computational Resources.Falk Lieder & Thomas L. Griffiths - forthcoming - Behavioral and Brain Sciences:1-85.
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  3.  90
    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.
  4.  65
    Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic.Thomas L. Griffiths, Falk Lieder & Noah D. Goodman - 2015 - Topics in Cognitive Science 7 (2):217-229.
    Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the (...)
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  5. 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.
  6.  19
    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.
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  7.  62
    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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  8.  16
    Topics in Semantic Representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
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  9.  79
    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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  10.  23
    Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  11.  53
    A Bayesian Framework for Word Segmentation: Exploring the Effects of Context.Sharon Goldwater, Thomas L. Griffiths & Mark Johnson - 2009 - Cognition 112 (1):21-54.
  12.  51
    Language Evolution by Iterated Learning With Bayesian Agents.Thomas L. Griffiths & Michael L. Kalish - 2007 - Cognitive Science 31 (3):441-480.
    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior (...)
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  13.  61
    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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  14.  42
    The Evolution of Frequency Distributions: Relating Regularization to Inductive Biases Through Iterated Learning.Florencia Reali & Thomas L. Griffiths - 2009 - Cognition 111 (3):317-328.
  15.  8
    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.
  16.  30
    Children’s Imitation of Causal Action Sequences is Influenced by Statistical and Pedagogical Evidence.Daphna Buchsbaum, Alison Gopnik, Thomas L. Griffiths & Patrick Shafto - 2011 - Cognition 120 (3):331-340.
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  17.  32
    Rational Variability in Children’s Causal Inferences: The Sampling Hypothesis.Stephanie Denison, Elizabeth Bonawitz, Alison Gopnik & Thomas L. Griffiths - 2013 - Cognition 126 (2):285-300.
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  18.  2
    Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning.Neil R. Bramley, Peter Dayan, Thomas L. Griffiths & David A. Lagnado - 2017 - Psychological Review 124 (3):301-338.
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  19.  11
    Random Walks on Semantic Networks Can Resemble Optimal Foraging.Joshua T. Abbott, Joseph L. Austerweil & Thomas L. Griffiths - 2015 - Psychological Review 122 (3):558-569.
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  20.  15
    The Influence of Categories on Perception: Explaining the Perceptual Magnet Effect as Optimal Statistical Inference.Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan - 2009 - Psychological Review 116 (4):752-782.
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  21.  34
    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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  22.  13
    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.
  23.  14
    Strategy Selection as Rational Metareasoning.Falk Lieder & Thomas L. Griffiths - 2017 - Psychological Review 124 (6):762-794.
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  24.  29
    When Children Are Better Learners Than Adults: Developmental Differences in Learning the Forms of Causal Relationships.Christopher G. Lucas, Sophie Bridgers, Thomas L. Griffiths & Alison Gopnik - 2014 - Cognition 131 (2):284-299.
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  25.  36
    Learning the Form of Causal Relationships Using Hierarchical Bayesian Models.Christopher G. Lucas & Thomas L. Griffiths - 2010 - Cognitive Science 34 (1):113-147.
  26.  8
    Bayesian Collective Learning Emerges From Heuristic Social Learning.P. M. Krafft, Erez Shmueli, Thomas L. Griffiths, Joshua B. Tenenbaum & Alex “Sandy” Pentland - 2021 - Cognition 212:104469.
  27.  96
    Probabilistic Inference in Human Semantic Memory.Mark Steyvers, Thomas L. Griffiths & Simon Dennis - 2006 - Trends in Cognitive Sciences 10 (7):327-334.
  28.  36
    Word-Level Information Influences Phonetic Learning in Adults and Infants.Naomi H. Feldman, Emily B. Myers, Katherine S. White, Thomas L. Griffiths & James L. Morgan - 2013 - Cognition 127 (3):427-438.
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  29.  26
    From Mere Coincidences to Meaningful Discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  30.  24
    A Probabilistic Model of Theory Formation.Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths - 2010 - Cognition 114 (2):165-196.
  31. Seeking Confirmation Is Rational for Deterministic Hypotheses.Joseph L. Austerweil & Thomas L. Griffiths - 2011 - Cognitive Science 35 (3):499-526.
    The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. (...)
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  32.  27
    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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  33.  63
    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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  34.  13
    Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.Jordan W. Suchow, David D. Bourgin & Thomas L. Griffiths - 2017 - Trends in Cognitive Sciences 21 (7):522-530.
  35.  11
    Manifesto for a New Cognitive Revolution.Thomas L. Griffiths - 2015 - Cognition 135:21-23.
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  36.  33
    The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning.Stephan Lewandowsky, Thomas L. Griffiths & Michael L. Kalish - 2009 - Cognitive Science 33 (6):969-998.
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  37. Learning Phonetic Categories by Learning a Lexicon.Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
  38.  18
    Probabilistic Models, Learning Algorithms, and Response Variability: Sampling in Cognitive Development.Elizabeth Bonawitz, Stephanie Denison, Thomas L. Griffiths & Alison Gopnik - 2014 - Trends in Cognitive Sciences 18 (10):497-500.
  39.  39
    When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data.Anne S. Hsu, Andy Horng, Thomas L. Griffiths & Nick Chater - 2017 - Cognitive Science 41 (S5):1155-1167.
    Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event never occurs. A rational analysis using Bayesian inference predicts that inferences from absent data should depend on how much the absence is expected to (...)
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  40.  33
    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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  41.  10
    A Role for the Developing Lexicon in Phonetic Category Acquisition.Naomi H. Feldman, Thomas L. Griffiths, Sharon Goldwater & James L. Morgan - 2013 - Psychological Review 120 (4):751-778.
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  42.  20
    Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases.Thomas L. Griffiths, Brian R. Christian & Michael L. Kalish - 2008 - Cognitive Science 32 (1):68-107.
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  43.  14
    Infant-Directed Speech is Consistent with Teaching.Baxter S. Eaves, Naomi H. Feldman, Thomas L. Griffiths & Patrick Shafto - 2016 - Psychological Review 123 (6):758-771.
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  44.  19
    Sensitivity to Shared Information in Social Learning.Andrew Whalen, Thomas L. Griffiths & Daphna Buchsbaum - 2018 - Cognitive Science 42 (1):168-187.
    Social learning has been shown to be an evolutionarily adaptive strategy, but it can be implemented via many different cognitive mechanisms. The adaptive advantage of social learning depends crucially on the ability of each learner to obtain relevant and accurate information from informants. The source of informants’ knowledge is a particularly important cue for evaluating advice from multiple informants; if the informants share the source of their information or have obtained their information from each other, then their testimony is statistically (...)
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  45.  23
    Performing Bayesian Inference with Exemplar Models.Lei Shi, Naomi H. Feldman & Thomas L. Griffiths - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 745--750.
  46.  19
    Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor.Anna N. Rafferty, Thomas L. Griffiths & Dan Klein - 2014 - Cognitive Science 38 (7):1406-1431.
    Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling (...)
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  47.  29
    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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  48.  9
    Greater Learnability is Not Sufficient to Produce Cultural Universals.Anna N. Rafferty, Thomas L. Griffiths & Marc Ettlinger - 2013 - Cognition 129 (1):70-87.
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  49.  21
    Revealing Ontological Commitments by Magic.Thomas L. Griffiths - 2015 - Cognition 136:43-48.
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  50.  16
    Probabilistic Models of Cognitive Development: Towards a Rational Constructivist Approach to the Study of Learning and Development.Fei Xu & Thomas L. Griffiths - 2011 - Cognition 120 (3):299-301.
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