A Bayesian Framework for Cross-Situational Word-Learning (2009)
Michael C. Frank, Noah D. Goodman, Joshua B. Tenenbaum
For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use...
Learning and using relational theories (2009)
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that...
A Bayesian Framework for Cross-Situational Word-Learning (2009)
Michael C. Frank, Noah D. Goodman, Joshua B. Tenenbaum
For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use...
Fragment Grammars: Exploring Computation and Reuse in Language (2009)
Tenenbaum, Joshua B., Goodman, Noah D., O'Donnell, Timothy J.
Language relies on a division of labor between stored units and structure building operations which combine the stored units into larger structures. This division of labor leads to a tradeoff: more...
Fragment Grammars: Exploring Computation and Reuse in Language (2009)
Tenenbaum, Joshua B., Goodman, Noah D., O'Donnell, Timothy J.
Language relies on a division of labor between stored units and structure building operations which combine the stored units into larger structures. This division of labor leads to a tradeoff: more...
Random-World Semantics and Syntactic Independence for Expressive Languages (2008)
McAllester, David, Milch, Brian, Goodman, Noah D.
We consider three desiderata for a language combining logic and probability: logical expressivity, random-world semantics, and the existence of a useful syntactic condition for probabilistic...
Random-World Semantics and Syntactic Independence for Expressive Languages (2008)
McAllester, David, Milch, Brian, Goodman, Noah D.
We consider three desiderata for a language combining logic and probability: logical expressivity, random-world semantics, and the existence of a useful syntactic condition for probabilistic...
Learning and using relational theories (2008)
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that...
Learning Grounded Causal Models (2008)
Noah D. Goodman, Vikash K. Mansinghka, Joshua B. Tenenbaum
We address the problem of learning grounded causal models: systems of concepts that are connected by causal relations and explicitly grounded in perception. We present a Bayesian framework for...
Learning and using relational theories: supporting material (2008)
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
We previously considered a Bayesian model based on our complexity measure, but Equations 2 and 3 can also be used to define a Bayesian model based on Goodman’s measure. The left columns show...
Learning causal schemata (2007)
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
Causal inferences about sparsely observed objects are often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that learns simple...
A rational analysis of rule-based concept learning (2007)
Noah D. Goodman, Thomas Griffiths (tom, Jacob Feldman, Joshua B. Tenenbaum
We propose a new model of human concept learning that provides a rational analysis for learning of feature-based concepts. This model is built upon Bayesian inference for a grammatically structured...
Learning causal schemata (2007)
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
Causal inferences about sparsely observed objects are often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that learns simple...
A Rational Analysis of Rule-based Concept Learning (2007)
Jacob Feldman, Noah D. Goodman, Joshua B. Tenenbaum, Thomas L. Griffiths
We propose a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured...
A rational analysis of rule-based concept learning (2007)
Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman, Thomas L. Griffiths
Address correspondence to
Intuitive theories of mind: a rational approach to false belief (2006)
Noah D. Goodman, Chris L. Baker, Elizabeth Baraff Bonawitz, Vikash K. Mansinghka, Alison Gopnik, Henry Wellman, ...
We propose a rational analysis of children’s false belief reasoning. Our analysis realizes a continuous, evidencedriven transition between two causal Bayesian models of false belief. Both models...
Intuitive theories of mind: a rational approach to false belief (2006)
Noah D. Goodman, Chris L. Baker, Elizabeth Baraff Bonawitz, Vikash K. Mansinghka, Alison Gopnik, Henry Wellman, ...
We propose a causal Bayesian model of false belief reasoning in children. This model realizes theory of mind as the rational use of intuitive theories and supports causal prediction, explanation, and...