45 found
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  1.  70
    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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  2.  13
    Pragmatic Language Interpretation as Probabilistic Inference.Noah D. Goodman & Michael C. Frank - 2016 - Trends in Cognitive Sciences 20 (11):818-829.
  3. Knowledge and Implicature: Modeling Language Understanding as Social Cognition.Noah D. Goodman & Andreas Stuhlmüller - 2013 - Topics in Cognitive Science 5 (1):173-184.
    Is language understanding a special case of social cognition? To help evaluate this view, we can formalize it as the rational speech-act theory: Listeners assume that speakers choose their utterances approximately optimally, and listeners interpret an utterance by using Bayesian inference to “invert” this model of the speaker. We apply this framework to model scalar implicature (“some” implies “not all,” and “N” implies “not more than N”). This model predicts an interaction between the speaker's knowledge state and the listener's interpretation. (...)
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  4.  28
    The Double-Edged Sword of Pedagogy: Instruction Limits Spontaneous Exploration and Discovery.Elizabeth Bonawitz, Patrick Shafto, Hyowon Gweon, Noah D. Goodman, Elizabeth Spelke & Laura Schulz - 2011 - Cognition 120 (3):322-330.
  5.  8
    When Redundancy is Useful: A Bayesian Approach to “Overinformative” Referring Expressions.Judith Degen, Robert D. Hawkins, Caroline Graf, Elisa Kreiss & Noah D. Goodman - 2020 - Psychological Review 127 (4):591-621.
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  6.  76
    Adjectival Vagueness in a Bayesian Model of Interpretation.Daniel Lassiter & Noah D. Goodman - 2017 - Synthese 194 (10):3801-3836.
    We derive a probabilistic account of the vagueness and context-sensitivity of scalar adjectives from a Bayesian approach to communication and interpretation. We describe an iterated-reasoning architecture for pragmatic interpretation and illustrate it with a simple scalar implicature example. We then show how to enrich the apparatus to handle pragmatic reasoning about the values of free variables, explore its predictions about the interpretation of scalar adjectives, and show how this model implements Edgington’s Vagueness: a reader, 1997) account of the sorites paradox, (...)
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  7. 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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  8.  20
    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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  9.  9
    The Language of Generalization.Michael Henry Tessler & Noah D. Goodman - 2019 - Psychological Review 126 (3):395-436.
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  10.  9
    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.
  11.  45
    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.
  12.  17
    Where Science Starts: Spontaneous Experiments in Preschoolers’ Exploratory Play.Claire Cook, Noah D. Goodman & Laura E. Schulz - 2011 - Cognition 120 (3):341-349.
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  13.  1
    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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  14.  6
    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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  15.  10
    The Interactions of Rational, Pragmatic Agents Lead to Efficient Language Structure and Use.Benjamin N. Peloquin, Noah D. Goodman & Michael C. Frank - 2020 - Topics in Cognitive Science 12 (1):433-445.
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  16.  20
    The Emergence of Social Norms and Conventions.Robert X. D. Hawkins, Noah D. Goodman & Robert L. Goldstone - 2019 - Trends in Cognitive Sciences 23 (2):158-169.
  17.  19
    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.
  18.  71
    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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  19.  22
    Affective Cognition: Exploring Lay Theories of Emotion.Desmond C. Ong, Jamil Zaki & Noah D. Goodman - 2015 - Cognition 143:141-162.
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  20.  17
    Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap.Desmond C. Ong, Jamil Zaki & Noah D. Goodman - 2019 - Topics in Cognitive Science 11 (2):338-357.
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  21.  27
    How Many Kinds of Reasoning? Inference, Probability, and Natural Language Semantics.Daniel Lassiter & Noah D. Goodman - 2015 - Cognition 136:123-134.
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  22.  12
    Remembrance of Inferences Past: Amortization in Human Hypothesis Generation.Ishita Dasgupta, Eric Schulz, Noah D. Goodman & Samuel J. Gershman - 2018 - Cognition 178:67-81.
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  23.  53
    The Strategic Use of Noise in Pragmatic Reasoning.Leon Bergen & Noah D. Goodman - 2015 - Topics in Cognitive Science 7 (2):336-350.
    We combine two recent probabilistic approaches to natural language understanding, exploring the formal pragmatics of communication on a noisy channel. We first extend a model of rational communication between a speaker and listener, to allow for the possibility that messages are corrupted by noise. In this model, common knowledge of a noisy channel leads to the use and correct understanding of sentence fragments. A further extension of the model, which allows the speaker to intentionally reduce the noise rate on a (...)
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  24. 1. Not a Sure Thing: Fitness, Probability, and Causation Not a Sure Thing: Fitness, Probability, and Causation (Pp. 147-171). [REVIEW]Denis M. Walsh, Leah Henderson, Noah D. Goodman, Joshua B. Tenenbaum, James F. Woodward, Hannes Leitgeb, Richard Pettigrew, Brad Weslake & John Kulvicki - 2010 - Philosophy of Science 77 (2):172-200.
    Hierarchical Bayesian models 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 higher-level (...)
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  25.  2
    Characterizing the Dynamics of Learning in Repeated Reference Games.Robert D. Hawkins, Michael C. Frank & Noah D. Goodman - 2020 - Cognitive Science 44 (6).
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  26.  4
    The Division of Labor in Communication: Speakers Help Listeners Account for Asymmetries in Visual Perspective.Robert D. Hawkins, Hyowon Gweon & Noah D. Goodman - 2021 - Cognitive Science 45 (3):e12926.
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  27.  24
    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.
  28. Cause and Intent: Social Reasoning in Causal Learning.Noah D. Goodman, Chris L. Baker & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 2759--2764.
     
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  29. Informative Communication in Word Production and Word Learning.Michael C. Frank, Noah D. Goodman, Peter Lai & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  30.  6
    Extremely Costly Intensifiers Are Stronger Than Quite Costly Ones.Erin D. Bennett & Noah D. Goodman - 2018 - Cognition 178:147-161.
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  31.  35
    A Computational Model of Linguistic Humor in Puns.Justine T. Kao, Roger Levy & Noah D. Goodman - 2016 - Cognitive Science 40 (5):1270-1285.
    Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human (...)
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  32. How Tall is Tall? Compositionality, Statistics, and Gradable Adjectives.Lauren A. Schmidt, Noah D. Goodman, David Barner & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  33.  6
    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 - forthcoming - Psychological Review.
  34.  6
    Logic, Probability, and Pragmatics in Syllogistic Reasoning.Michael Henry Tessler, Joshua B. Tenenbaum & Noah D. Goodman - forthcoming - Topics in Cognitive Science.
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  35. Beyond Boolean Logic: Exploring Representation Languages for Learning Complex Concepts.Steven T. Piantadosi, Joshua B. Tenenbaum & Noah D. Goodman - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 859--864.
  36.  6
    Resolving Uncertainty in Plural Predication.Gregory Scontras & Noah D. Goodman - 2017 - Cognition 168:294-311.
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  37. Relational and Role-Governed Categories: Views From Psychology, Computational Modeling, and Linguistics.Micah B. Goldwater, Noah D. Goodman, Stephen Wechsler & Gregory L. Murphy - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
  38.  15
    Compositionality in Rational Analysis: Grammar-Based Induction for Concept Learning.Noah D. Goodman, Joshua B. Tenenbaum, Thomas L. Griffiths & Jacob Feldman - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
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  39.  2
    Logic, Probability, and Pragmatics in Syllogistic Reasoning.Michael Henry Tessler, Joshua B. Tenenbaum & Noah D. Goodman - forthcoming - Wiley: Topics in Cognitive Science.
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  40.  2
    Warm (for Winter): Inferring Comparison Classes in Communication.Michael Henry Tessler & Noah D. Goodman - 2022 - Cognitive Science 46 (3):e13095.
    Cognitive Science, Volume 46, Issue 3, March 2022.
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  41.  27
    Comparing Pluralities.Gregory Scontras, Peter Graff & Noah D. Goodman - 2012 - Cognition 123 (1):190-197.
  42. The Effects of Information Utility and Teachers’ Knowledge on Evaluations of Under-Informative Pedagogy Across Development.Ilona Bass, Elizabeth Bonawitz, Daniel Hawthorne-Madell, Wai Keen Vong, Noah D. Goodman & Hyowon Gweon - 2022 - Cognition 222:104999.
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  43. Compositionality in Rational Analysis: Grammar-Based Induction for Concept Learning.Noah D. Goodman, Joshua B. Tenenbaum, Thomas L. Griffiths & Feldman & Jacob - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
     
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  44.  2
    Analyzing Machine‐Learned Representations: A Natural Language Case Study.Ishita Dasgupta, Demi Guo, Samuel J. Gershman & Noah D. Goodman - 2020 - Cognitive Science 44 (12).
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  45.  4
    Avoiding Frostbite: It Helps to Learn From Others.Michael Henry Tessler, Noah D. Goodman & Michael C. Frank - 2017 - Behavioral and Brain Sciences 40.
    Machines that learn and think like people must be able to learn from others. Social learning speeds up the learning process and – in combination with language – is a gateway to abstract and unobservable information. Social learning also facilitates the accumulation of knowledge across generations, helping people and artificial intelligences learn things that no individual could learn in a lifetime.
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