Abstract
How does the causal structure of a problem concept influence judgments of treatment efficacy? We argue that the task of evaluating treatment efficacy involves a combination of causal reasoning and categorization. After an exemplar has been categorized, a treatment task involves judging where to intervene in the causal structure to eradicate the problem, removing the exemplar from category membership. We hypothesized that the processes underlying such category membership removal tasks are not identical to those underlying categorization. Whereas previous experiments have shown that both the root cause (as the most generative feature) and the coherence of the exemplar heavily influence categorization, Experiments 1 and 2 showed that people base category membership removal judgments on the root cause. In Experiment 3, people spontaneously chose to remove an exemplar from category membership when asked to treat the terminal effect. We discuss how our findings are compatible with existing models of categorization. A description of pilot studies for Experiment 1 may be downloaded as supplemental materials from mc.psychonomic-journals.org.
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Ahn, W., Kim, N. S., Lassaline, M. E., & Dennis, M. (2000). Causal status as a determinant of feature centrality. Cognitive Psychology, 41, 361–416.
American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author.
Delaney, C. (1998). Reducing recidivism: Medication versus psychosocial rehabilitation. Journal of Psychosocial Nursing & Mental Health Services, 36, 28–34.
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.
Gopnik, A., Sobel, D. M., Schulz, L. E., & Glymour, C. (2001). Causal learning mechanisms in very young children: Two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation. Developmental Psychology, 37, 620–629.
Keil, F. C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press.
Kim, N. S., & Ahn, W. (2002a). Clinical psychologists’ theory-based representations of mental disorders predict their diagnostic reasoning and memory. Journal of Experimental Psychology: General, 131, 451–476.
Kim, N. S., & Ahn, W. (2002b). The influence of naive causal theories on lay concepts of mental illness. American Journal of Psychology, 115, 33–65.
Marsh, J. K., & Ahn, W. (2006). The role of causal status versus interfeature links in feature weighting. In R. Sun (Ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 561–566). Mahwah, NJ: Erlbaum.
McKenzie, C. R. M. (2006). Increased sensitivity to differentially diagnostic answers using familiar materials: Implications for confirmation bias. Memory & Cognition, 34, 577–588.
Murphy, G. L. & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.
National Institute of Mental Health (2006). Research on adherence to interventions for mental disorders. Retrieved April 28, 2008, from grants.nih.gov/grants/guide/pa-files/PA06-324.html.
Rehder, B. (2003). Categorization and causal reasoning. Cognitive Science, 27, 709–748.
Rehder, B., & Kim, S. (2006). How causal knowledge affects classification: A generative theory of categorization. Journal of Experimental Psychology: Learning, Memory, & Cognition, 32, 659–683.
Rehder, B., & Kim, S. (2008). The role of coherence in causal-based categorization. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 285–290). Mahwah, NJ: Erlbaum.
Rips, L. J. (1989). Similarity, typicality, and categorization. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 21–59). Cambridge: Cambridge University Press.
Ross, B. H. (1997). The use of categories affects classification. Journal of Memory & Language, 37, 240–267.
Ross, B. H. (1999). Postclassification category use: The effects of learning to use categories after learning to classify. Journal of Experimental Psychology: Learning, Memory, & Cognition, 25, 743–757.
Rozenblit, L. R., & Keil, F. C. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science, 26, 521–562.
Schulz, L. E., Gopnik, A., & Glymour, C. (2007). Preschool children learn about causal structure from conditional interventions. Developmental Science, 10, 322–332.
Sloman, S. A. (2005). Causal models: How people think about the world and its alternatives. Oxford: Oxford University Press.
Sloman, S. A., & Lagnado, D. A. (2005). Do we “do”? Cognitive Science, 29, 5–39.
Sloman, S. A., Love, B. C., & Ahn, W. (1998). Feature centrality and conceptual coherence. Cognitive Science, 22, 189–228.
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.
Waldmann, M. R., & Holyoak, K. J. (1992). Predictive and diagnostic learning within causal models: Asymmetries in cue competition. Journal of Experimental Psychology: General, 121, 222–236.
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Yopchick, J.E., Kim, N.S. The influence of causal information on judgments of treatment efficacy. Memory & Cognition 37, 29–41 (2009). https://doi.org/10.3758/MC.37.1.29
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DOI: https://doi.org/10.3758/MC.37.1.29