104 found
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  1.  53
    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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  2. 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.
  3.  93
    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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  4.  35
    Topics in semantic representation.Thomas L. Griffiths, Mark Steyvers & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):211-244.
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  5.  46
    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.
  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. 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.
  8.  80
    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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  9.  11
    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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  10.  31
    Theory-based causal induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  11.  30
    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.
  12.  76
    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.  29
    Strategy selection as rational metareasoning.Falk Lieder & Thomas L. Griffiths - 2017 - Psychological Review 124 (6):762-794.
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  14.  58
    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.  80
    A Bayesian framework for word segmentation: Exploring the effects of context.Sharon Goldwater, Thomas L. Griffiths & Mark Johnson - 2009 - Cognition 112 (1):21-54.
  16.  17
    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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  17.  82
    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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  18.  66
    When children are better (or at least more open-minded) 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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  19.  29
    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.
  20.  36
    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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  21.  35
    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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  22.  10
    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.
  23.  37
    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.  21
    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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  25.  46
    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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  26.  53
    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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  27.  21
    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.
  28.  72
    Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations.Joshua C. Peterson, Joshua T. Abbott & Thomas L. Griffiths - 2018 - Cognitive Science 42 (8):2648-2669.
    Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real‐world stimuli that can potentially be leveraged to capture psychological representations. We find that state‐of‐the‐art object classification networks provide surprisingly accurate predictions of human similarity judgments for (...)
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  29.  24
    Manifesto for a new (computational) cognitive revolution.Thomas L. Griffiths - 2015 - Cognition 135 (C):21-23.
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  30.  52
    Learning the Form of Causal Relationships Using Hierarchical Bayesian Models.Christopher G. Lucas & Thomas L. Griffiths - 2010 - Cognitive Science 34 (1):113-147.
  31. 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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  32.  36
    From partners to populations: A hierarchical Bayesian account of coordination and convention.Robert D. Hawkins, Michael Franke, Michael C. Frank, Adele E. Goldberg, Kenny Smith, Thomas L. Griffiths & Noah D. Goodman - 2023 - Psychological Review 130 (4):977-1016.
  33.  70
    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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  34.  42
    From mere coincidences to meaningful discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  35.  23
    Extracting Low‐Dimensional Psychological Representations from Convolutional Neural Networks.Aditi Jha, Joshua C. Peterson & Thomas L. Griffiths - 2023 - Cognitive Science 47 (1):e13226.
    Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) (...)
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  36.  22
    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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  37.  39
    A probabilistic model of theory formation.Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi & Thomas L. Griffiths - 2010 - Cognition 114 (2):165-196.
  38.  50
    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. New York: Oxford University Press. pp. 301--322.
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  39.  7
    A rational model of people’s inferences about others’ preferences based on response times.Vael Gates, Frederick Callaway, Mark K. Ho & Thomas L. Griffiths - 2021 - Cognition 217 (C):104885.
  40.  14
    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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  41.  14
    Intuitions about magic track the development of intuitive physics.Casey Lewry, Kaley Curtis, Nadya Vasilyeva, Fei Xu & Thomas L. Griffiths - 2021 - Cognition 214 (C):104762.
  42.  18
    A rational reinterpretation of dual-process theories.Smitha Milli, Falk Lieder & Thomas L. Griffiths - 2021 - Cognition 217 (C):104881.
  43.  17
    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.
  44. 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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  45.  30
    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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  46.  54
    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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  47.  39
    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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  48.  19
    Learning How to Generalize.Joseph L. Austerweil, Sophia Sanborn & Thomas L. Griffiths - 2019 - Cognitive Science 43 (8):e12777.
    Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning (...)
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  49.  12
    Show or tell? Exploring when (and why) teaching with language outperforms demonstration.Theodore R. Sumers, Mark K. Ho, Robert D. Hawkins & Thomas L. Griffiths - 2023 - Cognition 232 (C):105326.
  50.  44
    Two proposals for causal grammars.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal learning: psychology, philosophy, and computation. New York: Oxford University Press. pp. 323--345.
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