Using Physical Theories to Infer Hidden Causal Structure (2004)
| Venue: | In Proceedings of the 26th |
| Citations: | 7 - 7 self |
BibTeX
@INPROCEEDINGS{Griffiths04usingphysical,
author = {Thomas L. Griffiths and Elizabeth R. Baraff and Joshua B. Tenenbaum},
title = {Using Physical Theories to Infer Hidden Causal Structure},
booktitle = {In Proceedings of the 26th},
year = {2004},
pages = {446--451},
publisher = {Erlbaum}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
We argue that human judgments about hidden causal structure can be explained as the operation of domain-general statistical inference over causal models constructed using domain knowledge. We present Bayesian models of causal induction in two previous experiments and a new study. Hypothetical causal models are generated by theories expressing two essential aspects of abstract knowledge about causal mechanisms: which causal relations are plausible, and what functional form they take.
Citations
| 766 | Causality: Models, reasoning, and inference - Pearl - 2001 |
| 95 | A theory of causal learning in children: causal maps and Bayes nets. Psychol Rev 111 - Gopnik, Glymour, et al. - 2004 |
| 80 | Understanding the representational mind - Perner - 1991 |
| 60 | The mind’s arrows: Bayes nets and graphical causal models in psychology - Glymour - 2001 |
| 57 | Insides and essences: Early understandings of the nonobvious - Gelman, Wellman - 1991 |
| 28 | Rules of causal attribution - Shultz - 1982 |
| 18 | The misunderstood limits of folk science: An illusion of explanatory depth - Rozenblit, Keil - 2002 |
| 12 | How temporal assumptions influence causal judgments - Hagmayer, Waldmann - 2003 |
| 6 | The debate between current versions of covariation and mechanism approaches to causal inference - Newsome - 2003 |
| 5 | Inferring hidden causes - Kushnir, Gopnik, et al. - 2003 |
| 5 | Evaluating the causal role of unobserved variables - Luhmann, Ahn - 2003 |
