Results for 'Griffiths Thomas'

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  1.  52
    From mere coincidences to meaningful discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  2.  14
    On the hazards of relating representations and inductive biases.Thomas L. Griffiths, Sreejan Kumar & R. Thomas McCoy - 2023 - Behavioral and Brain Sciences 46:e275.
    The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a “language-of-thought” that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.
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  3. How do we practice our religion while we practice?Thomas B. Griffith - 2009 - In Scott Wallace Cameron, Galen LeGrande Fletcher & Jane H. Wise, Life in the Law: Service & Integrity. J. Reuben Clark Law Society, Brigham Young University Law School.
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  4. 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.  48
    Topics in semantic representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
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  6.  36
    A primer on probabilistic inference.Thomas L. Griffiths & Alan Yuille - 2008 - In Nick Chater & Mike Oaksford, The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press. pp. 33--57.
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  7. 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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  8.  35
    Theory-based causal induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  9.  29
    Learning to Be (In)variant: Combining Prior Knowledge and Experience to Infer Orientation Invariance in Object Recognition.L. Austerweil Joseph, L. Griffiths Thomas & E. Palmer Stephen - 2017 - Cognitive Science 41 (S5):1183-1201.
    How does the visual system recognize images of a novel object after a single observation despite possible variations in the viewpoint of that object relative to the observer? One possibility is comparing the image with a prototype for invariance over a relevant transformation set. However, invariance over rotations has proven difficult to analyze, because it applies to some objects but not others. We propose that the invariant transformations of an object are learned by incorporating prior expectations with real-world evidence. We (...)
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  10.  99
    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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  11.  26
    The Challenges of Large‐Scale, Web‐Based Language Datasets: Word Length and Predictability Revisited.Stephan C. Meylan & Thomas L. Griffiths - 2021 - Cognitive Science 45 (6):e12983.
    Language research has come to rely heavily on large‐scale, web‐based datasets. These datasets can present significant methodological challenges, requiring researchers to make a number of decisions about how they are collected, represented, and analyzed. These decisions often concern long‐standing challenges in corpus‐based language research, including determining what counts as a word, deciding which words should be analyzed, and matching sets of words across languages. We illustrate these challenges by revisiting “Word lengths are optimized for efficient communication” (Piantadosi, Tily, & Gibson, (...)
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  12.  62
    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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  13.  31
    Manifesto for a new (computational) cognitive revolution.Thomas L. Griffiths - 2015 - Cognition 135 (C):21-23.
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  14.  70
    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.  25
    A rational reinterpretation of dual-process theories.Smitha Milli, Falk Lieder & Thomas L. Griffiths - 2021 - Cognition 217 (C):104881.
  16.  86
    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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  17.  40
    Revealing ontological commitments by magic.Thomas L. Griffiths - 2015 - Cognition 136 (C):43-48.
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  18.  46
    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.
    Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses (...)
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  19.  48
    Two proposals for causal grammars.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - In Alison Gopnik & Laura Schulz, Causal learning: psychology, philosophy, and computation. New York: Oxford University Press. pp. 323--345.
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  20. 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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  21.  44
    Strategy selection as rational metareasoning.Falk Lieder & Thomas L. Griffiths - 2017 - Psychological Review 124 (6):762-794.
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  22.  36
    Introduction. Cultural transmission and the evolution of human behaviour.Kenny Smith, Michael Kalish, Thomas Griffiths & Stephan Lewandowsky - unknown
    The articles in this theme issue seek to understand the evolutionary bases of social learning and the consequences of cultural transmission for the evolution of human behaviour. In this introductory article, we provide a summary of these articles and a personal view of some promising lines of development suggested by the work summarized here.
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  23.  52
    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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  24.  19
    Randomness and Coincidences: Reconciling Intuition and Probability Theory.Thomas L. Griffiths & Joshua B. Tenenbaum - unknown
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  25.  55
    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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  26.  27
    Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro - 2008 - In Nick Chater & Mike Oaksford, The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
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  27.  26
    Optimal policies for free recall.Qiong Zhang, Thomas L. Griffiths & Kenneth A. Norman - 2023 - Psychological Review 130 (4):1104-1124.
  28.  58
    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.
    Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called ‘‘iterated learning,’’ in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate (...)
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  29. Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Navarro & J. Daniel - 2008 - In Nick Chater & Mike Oaksford, The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
     
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  30.  28
    Reconciling novelty and complexity through a rational analysis of curiosity.Rachit Dubey & Thomas L. Griffiths - 2020 - Psychological Review 127 (3):455-476.
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  31. 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.
  32.  60
    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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  33.  48
    Overrepresentation of extreme events in decision making reflects rational use of cognitive resources.Falk Lieder, Thomas L. Griffiths & Ming Hsu - 2018 - Psychological Review 125 (1):1-32.
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  34. The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted.Thomas L. Griffiths, Stephan Lewandowsky & Michael L. Kalish - 2013 - 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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  35.  42
    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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  36.  67
    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.
  37.  38
    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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  38. Learning phonetic categories by learning a lexicon.Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan - 2009 - In N. A. Taatgen & H. van Rijn, Proceedings of the 31st Annual Conference of the Cognitive Science Society.
  39.  39
    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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  40.  39
    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.
  41.  39
    Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences.Abdullah Almaatouq, Thomas L. Griffiths, Jordan W. Suchow, Mark E. Whiting, James Evans & Duncan J. Watts - 2024 - Behavioral and Brain Sciences 47:e33.
    The dominant paradigm of experiments in the social and behavioral sciences views an experiment as a test of a theory, where the theory is assumed to generalize beyond the experiment's specific conditions. According to this view, which Alan Newell once characterized as “playing twenty questions with nature,” theory is advanced one experiment at a time, and the integration of disparate findings is assumed to happen via the scientific publishing process. In this article, we argue that the process of integration is (...)
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  42.  99
    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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  43.  20
    If it's important, then I’m curious: Increasing perceived usefulness stimulates curiosity.Rachit Dubey, Thomas L. Griffiths & Tania Lombrozo - 2022 - Cognition 226 (C):105193.
  44.  56
    Intuitive theories as grammars for causal inference.Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi - 2007 - In Alison Gopnik & Laura Schulz, Causal learning: psychology, philosophy, and computation. New York: Oxford University Press. pp. 301--322.
  45.  44
    Exploring Human Cognition Using Large Image Databases.Thomas L. Griffiths, Joshua T. Abbott & Anne S. Hsu - 2016 - Topics in Cognitive Science 8 (3):569-588.
    Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how (...)
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  46.  30
    Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task.Sonia K. Murthy, Thomas L. Griffiths & Robert D. Hawkins - 2022 - Cognition 225 (C):105152.
  47.  26
    A nonparametric Bayesian framework for constructing flexible feature representations.Joseph L. Austerweil & Thomas L. Griffiths - 2013 - Psychological Review 120 (4):817-851.
  48.  94
    A Bayesian framework for word segmentation: Exploring the effects of context.Sharon Goldwater, Thomas L. Griffiths & Mark Johnson - 2009 - Cognition 112 (1):21-54.
  49.  27
    How to Be Helpful to Multiple People at Once.Vael Gates, Thomas L. Griffiths & Anca D. Dragan - 2020 - Cognitive Science 44 (6):e12841.
    When someone hosts a party, when governments choose an aid program, or when assistive robots decide what meal to serve to a family, decision‐makers must determine how to help even when their recipients have very different preferences. Which combination of people’s desires should a decision‐maker serve? To provide a potential answer, we turned to psychology: What do people think is best when multiple people have different utilities over options? We developed a quantitative model of what people consider desirable behavior, characterizing (...)
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  50. 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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