Learning a theory of causality
The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuiti...
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| Format: | Article |
| Language: | en_US |
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American Psychological Association
2012
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| Online Access: | http://hdl.handle.net/1721.1/70135 https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0003-1722-2382 |
| _version_ | 1826206391299211264 |
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| author | Goodman, Noah D. Ullman, Tomer David Tenenbaum, Joshua B. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Goodman, Noah D. Ullman, Tomer David Tenenbaum, Joshua B. |
| author_sort | Goodman, Noah D. |
| collection | MIT |
| description | The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned—an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion. |
| first_indexed | 2024-09-23T13:29:07Z |
| format | Article |
| id | mit-1721.1/70135 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:29:07Z |
| publishDate | 2012 |
| publisher | American Psychological Association |
| record_format | dspace |
| spelling | mit-1721.1/701352022-10-01T15:30:31Z Learning a theory of causality Goodman, Noah D. Ullman, Tomer David Tenenbaum, Joshua B. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Tenenbaum, Joshua B. Ullman, Tomer David Tenenbaum, Joshua B. Goodman, Noah D. The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned—an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion. James S. McDonnell Foundation (Causal Learning Collaborative Initiative) United States. Office of Naval Research (Grant N00014-09-0124) United States. Air Force Office of Scientific Research (Grant FA9550-07-1-0075) United States. Army Research Office (Grant W911NF-08-1-0242) 2012-04-25T19:44:48Z 2012-04-25T19:44:48Z 2011-01 2010-06 Article http://purl.org/eprint/type/JournalArticle 0033-295X 1939-1471 http://hdl.handle.net/1721.1/70135 Goodman, Noah D., Tomer D. Ullman, and Joshua B. Tenenbaum. “Learning a Theory of Causality.” Psychological Review 118.1 (2011): 110–119. Web. https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0003-1722-2382 en_US http://dx.doi.org/10.1037/a0021336 Psychological Review Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Psychological Association Prof. Tenenbaum |
| spellingShingle | Goodman, Noah D. Ullman, Tomer David Tenenbaum, Joshua B. Learning a theory of causality |
| title | Learning a theory of causality |
| title_full | Learning a theory of causality |
| title_fullStr | Learning a theory of causality |
| title_full_unstemmed | Learning a theory of causality |
| title_short | Learning a theory of causality |
| title_sort | learning a theory of causality |
| url | http://hdl.handle.net/1721.1/70135 https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0003-1722-2382 |
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