Skip to Content

Department of Cognitive Science

The effect of causal strength on the use of causal and similarity-based information in feature inference

Rachel G. Stephens (rachel.stephens@adelaide.edu.au)
Daniel J. Navarro (daniel.navarro@adelaide.edu.au)
John C. Dunn (john.c.dunn@adelaide.edu.au)
School of Psychology, University of Adelaide
Michael D. Lee (mdlee@uci.edu)
Department of Cognitive Sciences, University of California, Irvine

Abstract

Category-based feature generalisations are affected by similarity relationships between objects and by knowledge of causal relationships between features. However, there is disagreement between recent studies about whether people will simultaneously consider both relationships. To help resolve this discrepancy, the current study addresses an important difference between past experimental designs: the strength of causal relationships between features. Participants were trained on a set of four different kinds of artificial alien animals (with a known perceptual similarity structure), and were taught about three novel features. Participants were taught that either: 1) there were no relationships between the three features; 2) the features shared weak causal relationships; or 3) the features shared strong causal relationships. After training, all participants then made predictions about the features of the four kinds of animals. As expected, it was found that the strength of the causal relationships influenced the degree to which participants' feature predictions were affected by causal and similarity considerations. Three probabilistic graphical models were fit to the participants' predictions, in a preliminary effort to predict participant responses.

Citation details for this article:

Stephens, R., Navarro, D., Dunn, J., Lee, M. (2010). The Effect of Causal Strength on the Use of Causal and Similarity-based Information in Feature Inference. In W. Christensen, E. Schier, and J. Sutton (Eds.), ASCS09: Proceedings of the 9th Conference of the Australasian Society for Cognitive Science (pp. 325-333). Sydney: Macquarie Centre for Cognitive Science.

DOI: 10.5096/ASCS200949
Download the PDF here

References

  1. Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: MIT Press, Bradford Books.
  2. Heit, E. & Rubinstein, J. (1994). Similarity and property effects in inductive reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 411-422.
  3. Heussen, D. & Hampton, J. A. (2008). Ways of explaining properties. In V. Sloutsky, B. Love, & K. McRae (Eds.) Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 143-148). Austin, TX: Cognitive Science Society.
  4. Kemp, C., Shafto, P., Berke, A. & Tenenbaum, J. B. (2007). Combining causal and similarity-based reasoning. Advances in Neural Information Processing Systems, 19, 681-688.
  5. Lee, M. D. & Wagenmakers, E-J. (2009). A course in Bayesian graphical modeling for cognitive science. Unpublished manuscript. http://users.fmg.uva.nl/ewagenmakers/BayesCourse/BayesBook.pdf
  6. Lewandowsky, S. & Kirsner, K. (2000). Knowledge partitioning: Context-dependent use of expertise. Memory & Cognition, 28, 295-305.
  7. Lunn, D. J., Thomas, A., Best, N., Spiegelhalter, D. (2000). WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 10, 325-337.
  8. Medin, D. L., Coley, J. D., Storms, G., & Hayes, B. K. (2003). A relevance theory of induction. Psychonomic Bulletin & Review, 10, 517-532.
  9. Rehder, B. (2006). When similarity and causality compete in category-based property generalization. Memory & Cognition, 34, 3-16.
  10. Rehder, B. (2009). Causal-based property generalization. Cognitive Science, 33, 301-344.
  11. Rehder, B. & Burnett, R. C. (2005). Feature inference and the causal structure of categories. Cognitive Psychology, 50, 264-314.
  12. Rips, L. J. (1975). Inductive judgments about natural categories. Journal of Verbal Learning and Verbal Behavior, 14, 665-681.
  13. Schulz, L. E. & Sommerville, J. (2006). God does not play dice: Causal determinism and preschoolers' causal inferences. Child Development, 77, 427-442.
  14. Shafto, P., & Coley, J. D. (2003). Development of categorization and reasoning in the natural world: Novices to experts, naive similarity to ecological knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 641-649.
  15. Stephens, R. G. & Navarro, D. J. (2008). One of these greebles is not like the others: Semi-supervised models for similarity structures. Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1996-2001). Austin, TX: Cognitive Science Society.

Further Information

Seminars

Who is Visiting

  • Professor Jason Hollowell
  • Dr Olena Nikolenko
  • Dr Lianzhong Zheng
  • Dr Emmanual Chemla
  • Associate Professor Sara Hart
  • Dr Dona Jayakody
  • Dr Erik Chang
  • Dr Nichola Burton
  • Dr Clare Sutherland
  • Dielle Horne
  • Dr Amy Dawel
  • Ellen Bothe
  • Samantha-Kaye Johnston
  • Dr Ryan Balzan
  • Dr Teresa Schubert
  • Jemma Collova
  • Derek Swe
  • Professor Stefan Schweinberger
  • Chloe Giffard
  • Kaitlyn Turbett
  • Dr Britta Biedermann
  • Jonathon Love
  • Professor Ingo Bojak
  • Professor Sylvain Baillet
  • Dr Christos Pliatsikas
  • Professor James Douglas Saddy
  • Professor Tom Johnstone
  • Professor Matthew Lambon-Ralph
  • Dr Sharon Savage
  • Dr Donna Rose Addis
  • [Previous Visitors]

Contact Details

Telephone: (02) 9850 9599
Fax : (02) 9850 6059
Email : cogsci@mq.edu.au
Web : www.cogsci.mq.edu.au