Abstract
In two experiments with adults (N=126), we examined the influence of sampling procedure on inductive generalization. In predicate sampling, participants learned the category identity of individuals known to possess some property. In subject sampling, individuals selected for category identity were discovered to possess a novel property. In both experiments, sampling procedure influenced induction. Predicate sampling resulted in very narrow generalization, whereas subject sampling yielded a fairly high and constant rate of projection. Differences in confidence of generalizations were also observed. Conditions in which evidence was described as randomly sampled from a collection of animals yielded a consistent decrease in projections as predicted by similarity-based models. The results are presented as support for an evidence-based view of induction.
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Baumann, M., & Krems, J. F. (2002). Frequency learning and order effects in belief updating. In P. Sedlmeier & T. Betsch (Eds.), Etc.: Frequency processing and cognition (pp. 221–237). New York: Oxford University Press.
Dawes, R. M. (1993). Prediction of the future versus an understanding of the past: A basic asymmetry. American Journal of Psychology, 106, 1–24.
Eells, E. (1982). Rational decision and causality. New York: Cambridge University Press.
Fiedler, K. (2000). Beware of samples! A cognitive-ecological sampling approach to judgment biases. Psychological Review, 107, 659–676.
Fiedler, K. (2008). The ultimate sampling dilemma in experiencebased decision making. Journal of Experimental Psychology: Learning, Memory, & Cognition, 34, 186–203.
Fiedler, K., Brinkmann, B., Betsch, T., & Wild, B. (2000). A sampling approach to biases in conditional probability judgments: Beyond base-rate neglect and statistical format. Journal of Experimental Psychology: General, 129, 399–418.
Goodman, N. (1955). Fact, fiction, and forecast. Cambridge, MA: Harvard University Press.
Heit, E. (1998). A Bayesian analysis of some forms of inductive reasoning. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 248–274). Oxford: Oxford University Press.
Kalish, C. W., & Lawson, C. A. (2007). Negative evidence and inductive generalization. Thinking & Reasoning, 13, 394–425.
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10, 307–321.
Kincannon, A., & Spellman, B. A. (2003). The use of category and similarity information in limiting hypotheses. Memory & Cognition, 31, 114–132.
Lagnado, D. A., & Sloman, S. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 856–876.
McKenzie, C. R. M., & Mikkelsen, L. A. (2007). A Bayesian view of covariation assessment. Cognitive Psychology, 54, 33–61.
Nisbett, R. E., Krantz, D. H., Jepson, C., & Kunda, Z. (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90, 339–363.
Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608–631.
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185–200.
Rips, L. J. (1975). Inductive judgments about natural categories. Journal of Verbal Learning & Verbal Behavior, 14, 665–681.
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.
Shipley, E. (1993). Categories, hierarchies, and induction. Psychology of Learning & Motivation, 30, 265–301.
Sloman, S. A. (1993). Feature-based induction. Cognitive Psychology, 25, 231–280.
Sloutsky, V. M., & Fisher, A. V. (2004). Induction and categorization in young children: A similarity-based model. Journal of Experimental Psychology: General, 133, 166–188.
Smith, L. B. (1989). A model of perceptual classification in children and adults. Psychological Review, 96, 125–144.
Stewart, N., & Brown, G. D. A. (2005). Similarity and dissimilarity as evidence in perceptual categorization. Journal of Mathematical Psychology, 49, 403–409.
Stewart, N., & Morin, C. (2007). Dissimilarity is used as evidence of category membership in multidimensional perceptual categorization: A test of the similarity-dissimilarity generalized context model. Quarterly Journal of Experimental Psychology, 60, 1337–1346.
Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral & Brain Sciences, 24, 629–640.
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10, 309–318.
Tenenbaum, J. B., & Xu, F. (2000). Word learning as Bayesian inference. In L. Gleitman & A. Joshi (Eds.), Proceedings of the 22nd Annual Conference of the Cognitive Science Society (pp. 517–522). Hillsdale, NJ: Erlbaum.
Wason, P. C., & Johnson-Laird, P. N. (1972). Psychology of reasoning. Cambridge, MA: Harvard University Press.
Xu, F., & Tenenbaum, J. B. (2007). Sensitivity to sampling in Bayesian word learning. Developmental Science, 10, 288–297.
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This research was supported by NIH Postdoctoral Training Grant T32 MH019102, awarded to the first author, and by National Science Foundation-DLS Grant 0745423, awarded to the second author.
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Lawson, C.A., Kalish, C.W. Sample selection and inductive generalization. Memory & Cognition 37, 596–607 (2009). https://doi.org/10.3758/MC.37.5.596
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DOI: https://doi.org/10.3758/MC.37.5.596