David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. 119--38 (2007)
There is now substantial agreement about the representational component of a normative theory of causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused primarily on learning from single trials under a variety of conditions. In contrast, one component of the normative theory focuses on learning from a sample drawn from a population under some experimental or observational study regime. Through a virtual Causality Lab that embodies the normative theory of causal reasoning and which allows us to record student behavior, we have begun to systematically explore how best to teach the normative theory. In this paper we explain the overall project and report on pilot studies which suggest that students can quickly be taught to (appear to) be quite rational
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
Ralph Wedgwood (2006). The Normative Force of Reasoning. Noûs 40 (4):660–686.
Sieghard Beller & Gregory Kuhnm (2007). What Causal Conditional Reasoning Tells Us About People's Understanding of Causality. Thinking and Reasoning 13 (4):426 – 460.
Clark Glymour (2003). Learning, Prediction and Causal Bayes Nets. Trends in Cognitive Sciences 7 (1):43-48.
Alison Gopnik & Laura Schulz (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
Peter A. White (2011). Not by Contingency: Some Arguments About the Fundamentals of Human Causal Learning. Thinking and Reasoning 15 (2):129-166.
Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
York Hagmayer & Magda Osman (2012). From Colliding Billiard Balls to Colluding Desperate Housewives: Causal Bayes Nets as Rational Models of Everyday Causal Reasoning. Synthese 189 (S1):17-28.
David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
Added to index2010-12-22
Total downloads8 ( #187,207 of 1,413,268 )
Recent downloads (6 months)2 ( #94,880 of 1,413,268 )
How can I increase my downloads?