Inferring interventional predictions from observational learning data

Psychonomic Bulletin and Review

Volumn 15, Issue 1, 2008, Pages 75-80

  Meder, Björn ^a   Hagmayer, York ^a   Waldmann, Michael R ^a  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ASSOCIATION; ATTENTION; COMPREHENSION; COMPUTER SIMULATION; CONCEPT FORMATION; DECISION MAKING; HUMAN; IMAGINATION; LEARNING; MEMORY; PROBLEM SOLVING; PSYCHOLOGICAL MODEL; REINFORCEMENT;

ASSOCIATION LEARNING; ATTENTION; COMPREHENSION; COMPUTER SIMULATION; CONCEPT FORMATION; CUES; DECISION MAKING; HUMANS; IMAGINATION; JUDGMENT; KNOWLEDGE OF RESULTS (PSYCHOLOGY); MEMORY; MODELS, PSYCHOLOGICAL; PROBABILITY LEARNING; PROBLEM SOLVING;

EID: 50949085910     PISSN: 10699384     EISSN: 15315320     Source Type: Journal    
DOI: 10.3758/PBR.15.1.75     Document Type: Article

References (17)

1. BLAISDELL, A. P., SAWA, K., LEISING, K. J., & WALDMANN, M. R. (2006). Causal reasoning in rats. Science, 311, 1020-1022.
2. DANKS, D., GRIFFITHS, T. L., & TENENBAUM, J. B. (2003). Dynamical causal learning. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15 (pp. 67-74). Cambridge, MA: MIT Press.
3. DAWID, A. P. (2002). Influence diagrams for causal modelling and inference. International Statistical Review, 70, 161-189.
4. DICKINSON, A. (2001). Causal learning: An associative analysis. Quarterly Journal of Experimental Psychology, 54B, 3-25.
5. GOPNIK, A., GLYMOUR, C., SOBEL, D. M., SCHULZ, L. E., KUSHNIR, T., & DANKS, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 3-32.
6. HAGMAYER, Y., & WALDMANN, M. R. (2000). Simulating causal models: The way to structural sensitivity. In L. R. Gleitman & A. K. Joshi (Eds.), Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society (pp. 214-219). Mahwah, NJ: Erlbaum.
7. LAGNADO, D. A., & SLOMAN, S. A. (2006). Time as a guide to cause. Journal of Experimental Psychology: Learning, Memory, & Cognition, 32, 451-460.
8. LAGNADO, D. A., WALDMANN, M. R., HAGMAYER, Y., & SLOMAN, S. A. (2007). Beyond covariation: Cues to causal structure. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 154-172). Oxford: Oxford University Press.
9. PEARL, I. (1988). Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufmann.
10. PEARL, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.
11. SHANKS, D. R. (1991). On similarities between causal judgments in experienced and described situations. Psychological Science, 5, 341-350.
12. SLOMAN, S. A., & LAGNADO , D. A. (2005). Do we "do"? Cognitive Science, 29, 5-39.
13. SPIRTES, P., GLYMOUR, C., & SCHEINES, P. (1993). Causation, prediction, and search. New York: Springer.
14. STEYVERS, M., TENENBAUM, J. B., WAGENMAKERS, E. J., & BLUM, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453-489.
15. WALDMANN, M. R., & HAGMAYER, Y. (2005). Seeing versus doing: Two modes of accessing causal knowledge. Journal of Experimental Psychology: Learning, Memory, & Cognition, 31, 216-227.
16. WALDMANN, M. R., HAGMAYER, Y, & BLAISDELL, A. P. (2006). Beyond the information given: Causal models in learning and reasoning. Current Directions in Psychological Science, 15, 307-311.
17. WOODWARD, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press