Dynamical causal learning

David Danks; Thomas L. Griffiths; Joshua B. Tenenbaum

Dynamical causal learning

David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people's short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
Publisher	Neural information processing systems foundation
ISBN (Print)	0262025507, 9780262025508
State	Published - 2003
Externally published	Yes
Event	16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada Duration: Dec 9 2002 → Dec 14 2002

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Other

Other	16th Annual Neural Information Processing Systems Conference, NIPS 2002
Country/Territory	Canada
City	Vancouver, BC
Period	12/9/02 → 12/14/02

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

@inproceedings{0930dd1c6699442fa695335f911dad26,

title = "Dynamical causal learning",

abstract = "Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people's short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.",

author = "David Danks and Griffiths, {Thomas L.} and Tenenbaum, {Joshua B.}",

year = "2003",

language = "English (US)",

isbn = "0262025507",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002",

note = "16th Annual Neural Information Processing Systems Conference, NIPS 2002 ; Conference date: 09-12-2002 Through 14-12-2002",

}

Danks, D, Griffiths, TL & Tenenbaum, JB 2003, Dynamical causal learning. in Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 16th Annual Neural Information Processing Systems Conference, NIPS 2002, Vancouver, BC, Canada, 12/9/02.

Dynamical causal learning. / Danks, David; Griffiths, Thomas L.; Tenenbaum, Joshua B.
Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002. Neural information processing systems foundation, 2003. (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Dynamical causal learning

AU - Danks, David

AU - Griffiths, Thomas L.

AU - Tenenbaum, Joshua B.

PY - 2003

Y1 - 2003

N2 - Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people's short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.

AB - Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people's short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.

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M3 - Conference contribution

AN - SCOPUS:84898976148

SN - 0262025507

SN - 9780262025508

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002

PB - Neural information processing systems foundation

T2 - 16th Annual Neural Information Processing Systems Conference, NIPS 2002

Y2 - 9 December 2002 through 14 December 2002

ER -

Dynamical causal learning

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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