Zobrazeno 1 - 10
of 188
pro vyhledávání: '"Patricia W. Cheng"'
Autor:
Gigerenzer, G.
Publikováno v:
Thinking & Reasoning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1874::0c362a287eb0ee157f17d84257018943
https://hdl.handle.net/21.11116/0000-0000-D882-921.11116/0000-0000-BCA2-5
https://hdl.handle.net/21.11116/0000-0000-D882-921.11116/0000-0000-BCA2-5
Publikováno v:
Cognitive scienceReferences. 46(5)
The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of causal-i
Publikováno v:
Cognition. 230:105303
The present paper reports two experiments (N = 232, 254) addressing the question: How do reasoners reconcile the desire to have useable (i.e., invariant) causal knowledge - knowledge that holds true when applied in new circumstances/contexts - with t
Autor:
Motoyuki Saito, Patricia W. Cheng
Publikováno v:
The Proceedings of the Annual Convention of the Japanese Psychological Association. 81:2D-059
Publikováno v:
Cognitive psychology. 132
For causal knowledge to be worth learning, it must remain valid when that knowledge is applied. Because unknown background causes are potentially present, and may vary across the learning and application contexts, extricating the strength of a candid
Autor:
Hongjing Lu, Patricia W. Cheng
This chapter illustrates the representational nature of causal understanding of the world and examines its implications for causal learning. The vastness of the search space of causal relations, given the representational aspect of the problem, impli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85294d0fdb44f341729c18853dd1e591
https://doi.org/10.1093/oxfordhb/9780199399550.013.9
https://doi.org/10.1093/oxfordhb/9780199399550.013.9
Autor:
Keith J. Holyoak, Patricia W. Cheng
Publikováno v:
Annual Review of Psychology. 62:135-163
Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with Bayesian probabilistic inference. We describe this new synth
Autor:
Patricia W. Cheng, Mimi Liljeholm
Publikováno v:
Journal of Experimental Psychology: Learning, Memory, and Cognition. 35:157-172
The authors investigated whether confidence in causal judgments varies with virtual sample size--the frequency of cases in which the outcome is (a) absent before the introduction of a generative cause or (b) present before the introduction of a preve
Publikováno v:
Hongjing Lu; Alan L. Yuille; Mimi Liljeholm; Patricia W. Cheng; & Keith J. Holyoak. (2011). Bayesian Generic Priors for Causal Learning. Department of Statistics, UCLA. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/2595d045
Lu, Hongjing; Yuille, Alan L.; Liljeholm, Mimi; Cheng, Patricia W.; & Holyoak, Keith J.(2008). Bayesian Generic Priors for Causal Learning. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/2b57w213
Lu, Hongjing; Yuille, Alan L.; Liljeholm, Mimi; Cheng, Patricia W.; & Holyoak, Keith J.(2008). Bayesian Generic Priors for Causal Learning. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/2b57w213
The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d023b7ad10463716292d40f924608734
http://www.escholarship.org/uc/item/2595d045
http://www.escholarship.org/uc/item/2595d045