57 found
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  1.  7
    Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum (2010). Probabilistic Models of Cognition: Exploring Representations and Inductive Biases. Trends in Cognitive Sciences 14 (8):357-364.
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  2.  8
    Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp (2006). Theory-Based Bayesian Models of Inductive Learning and Reasoning. Trends in Cognitive Sciences 10 (7):309-318.
  3. Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu (2011). A Tutorial Introduction to Bayesian Models of Cognitive Development. Cognition 120 (3):302-321.
  4.  9
    Edward Vul, Noah Goodman, Thomas L. Griffiths & Joshua B. Tenenbaum (2014). One and Done? Optimal Decisions From Very Few Samples. 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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  5.  11
    Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman & Thomas L. Griffiths (2008). A Rational Analysis of Rule‐Based Concept Learning. Cognitive Science 32 (1):108-154.
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  6.  7
    Stephanie Denison, Elizabeth Bonawitz, Alison Gopnik & Thomas L. Griffiths (2013). Rational Variability in Children's Causal Inferences: The Sampling Hypothesis. Cognition 126 (2):285-300.
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  7.  14
    Joshua B. Tenenbaum & Thomas L. Griffiths (2001). Generalization, Similarity, and Bayesian Inference. 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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  8.  9
    Sharon Goldwater, Thomas L. Griffiths & Mark Johnson (2009). A Bayesian Framework for Word Segmentation: Exploring the Effects of Context. Cognition 112 (1):21-54.
  9.  10
    Daphna Buchsbaum, Alison Gopnik, Thomas L. Griffiths & Patrick Shafto (2011). Children’s Imitation of Causal Action Sequences is Influenced by Statistical and Pedagogical Evidence. Cognition 120 (3):331-340.
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  10.  5
    Thomas L. Griffiths & Michael L. Kalish (2007). Language Evolution by Iterated Learning With Bayesian Agents. 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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  11.  6
    Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum (2010). Modeling Human Performance in Statistical Word Segmentation. Cognition 117 (2):107-125.
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  12.  10
    Florencia Reali & Thomas L. Griffiths (2009). The Evolution of Frequency Distributions: Relating Regularization to Inductive Biases Through Iterated Learning. Cognition 111 (3):317-328.
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  13.  1
    Thomas L. Griffiths, Falk Lieder & Noah D. Goodman (2015). Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic. 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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  14.  10
    Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths (2010). A Probabilistic Model of Theory Formation. Cognition 114 (2):165-196.
  15.  7
    Christopher G. Lucas & Thomas L. Griffiths (2010). Learning the Form of Causal Relationships Using Hierarchical Bayesian Models. Cognitive Science 34 (1):113-147.
  16.  25
    Thomas L. Griffiths & Joshua B. Tenenbaum (2007). Two Proposals for Causal Grammars. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press 323--345.
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  17.  8
    Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi (2007). Intuitive Theories as Grammars for Causal Inference. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press 301--322.
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  18.  2
    Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum (2011). The Imaginary Fundamentalists: The Unshocking Truth About Bayesian Cognitive Science. 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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  19.  1
    Anna N. Rafferty, Thomas L. Griffiths & Marc Ettlinger (2013). Greater Learnability is Not Sufficient to Produce Cultural Universals. Cognition 129 (1):70-87.
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  20.  4
    Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan (2009). Learning Phonetic Categories by Learning a Lexicon. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
  21.  5
    Mark Steyvers, Thomas L. Griffiths & Simon Dennis (2006). Probabilistic Inference in Human Semantic Memory. Trends in Cognitive Sciences 10 (7):327-334.
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  22.  4
    Thomas L. Griffiths, Brian R. Christian & Michael L. Kalish (2008). Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases. Cognitive Science 32 (1):68-107.
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  23.  31
    Joseph L. Austerweil & Thomas L. Griffiths (2011). Seeking Confirmation Is Rational for Deterministic Hypotheses. 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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  24.  14
    Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik (2011). Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. 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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  25.  6
    Lei Shi, Naomi H. Feldman & Thomas L. Griffiths (2008). Performing Bayesian Inference with Exemplar Models. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society 745--750.
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  26.  11
    Naomi H. Feldman, Emily B. Myers, Katherine S. White, Thomas L. Griffiths & James L. Morgan (2013). Word-Level Information Influences Phonetic Learning in Adults and Infants. Cognition 127 (3):427-438.
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  27.  3
    Stephan Lewandowsky, Thomas L. Griffiths & Michael L. Kalish (2009). The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. Cognitive Science 33 (6):969-998.
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  28.  5
    Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro (2008). Categorization as Nonparametric Bayesian Density Estimation. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford
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  29.  4
    Thomas L. Griffiths & Joshua B. Tenenbaum (2007). From Mere Coincidences to Meaningful Discoveries. Cognition 103 (2):180-226.
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  30.  21
    Thomas L. Griffiths, Stephan Lewandowsky & Michael L. Kalish (2013). The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted. Cognitive Science 37 (5):953-967.
    Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that information changes. We tested this prediction using a function-learning task, in which people learn a functional relationship between two variables by observing the values of those variables. (...)
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  31.  5
    Elizabeth Baraff Bonawitz & Thomas L. Griffiths (2010). Deconfounding Hypothesis Generation and Evaluation in Bayesian Models. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society
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  32. Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Navarro & J. Daniel (2008). Categorization as Nonparametric Bayesian Density Estimation. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford
     
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  33.  11
    Chris Lucas, Alison Gopnik & Thomas L. Griffiths (2010). Developmental Differences in Learning the Forms of Causal Relationships. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society 28--52.
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  34.  6
    Mark Steyvers & Thomas L. Griffiths (2008). Rational Analysis as a Link Between Human Memory and Information Retrieval. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford 329--349.
  35.  5
    Joseph L. Austerweil & Thomas L. Griffiths (2010). Learning Hypothesis Spaces and Dimensions Through Concept Learning. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society 73--78.
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  36.  6
    Aaron Beppu & Thomas L. Griffiths (2009). Iterated Learning and the Cultural Ratchet. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2089--2094.
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  37.  5
    Daphna Buchsbaum, Thomas L. Griffiths, Alison Gopnik & Dare Baldwin (2009). Learning From Actions and Their Consequences: Inferring Causal Variables From Continuous Sequences of Human Action. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 134.
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  38.  8
    Adam N. Sanborn, Vikash Mansinghka & Thomas L. Griffiths (2009). A Bayesian Framework for Modeling Intuitive Dynamics. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  39.  8
    Thomas L. Griffiths (2015). Revealing Ontological Commitments by Magic. Cognition 136:43-48.
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  40.  3
    Jing Xu, Florencia Reali & Thomas L. Griffiths (2008). A Formal Analysis of Cultural Evolution by Replacement. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society 1435--1400.
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  41.  6
    Joseph Jay Williams & Thomas L. Griffiths (2008). Why Are People Bad at Detecting Randomness? Because It is Hard. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society
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  42. Joshua B. Tenenbaum & Thomas L. Griffiths (2001). Some Specifics About Generalization. Behavioral and Brain Sciences 24 (4):762-778.
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  43.  7
    Jing Xu, Thomas L. Griffiths & Mike Dowman (2010). Replicating Color Term Universals Through Human Iterated Learning. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society
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  44.  10
    Jay B. Martin, Thomas L. Griffiths & Adam N. Sanborn (2012). Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. 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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  45.  1
    Elizabeth Bonawitz, Stephanie Denison, Thomas L. Griffiths & Alison Gopnik (2014). Probabilistic Models, Learning Algorithms, and Response Variability: Sampling in Cognitive Development. Trends in Cognitive Sciences 18 (10):497-500.
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  46.  1
    Thomas L. Griffiths (2015). Manifesto for a New Cognitive Revolution. Cognition 135:21-23.
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  47.  5
    Thomas L. Griffiths & Alan Yuille (2008). A Primer on Probabilistic Inference. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford 33--57.
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  48.  5
    Anna N. Rafferty, Michelle M. LaMar & Thomas L. Griffiths (2015). Inferring Learners' Knowledge From Their Actions. Cognitive Science 39 (3):584-618.
    Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our (...)
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  49.  5
    Christopher G. Lucas, Sophie Bridgers, Thomas L. Griffiths & Alison Gopnik (2014). When Children Are Better Learners Than Adults: Developmental Differences in Learning the Forms of Causal Relationships. Cognition 131 (2):284-299.
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  50.  3
    Fei Xu & Thomas L. Griffiths (2011). Probabilistic Models of Cognitive Development: Towards a Rational Constructivist Approach to the Study of Learning and Development. Cognition 120 (3):299-301.
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