David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Cognitive Science 36 (2):187-223 (2012)
Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ‘‘sampling’’ assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, data are assumed to have been generated without any restrictions. We develop a more general account of sampling that allows for an intermediate mixture of these two extremes, and we test its usefulness. In two experiments, we show that most people complete simple one-dimensional generalization tasks in a way that is consistent with their believing in some mixture of strong and weak sampling, but that there are large individual differences in the relative emphasis different people give to each type of sampling. We also show experimentally that the relative emphasis of the mixture is influenced by the structure of the available information. We discuss the psychological meaning of mixing strong and weak sampling, and possible extensions of our modeling approach to richer problems of inductive generalization
|Keywords||Bayesian modeling Inductive inference Generalization|
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References found in this work BETA
David Marr (1982). Vision. Freeman.
Nelson Goodman (1983). Fact, Fiction, and Forecast. Harvard University Press.
Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
David Marr (1982). Vison. W. H. Freeman.
Citations of this work BETA
Michael C. Frank (2013). Throwing Out the Bayesian Baby with the Optimal Bathwater: Response To. Cognition 128 (3):417-423.
Keith J. Ransom, Amy Perfors & Daniel J. Navarro (2015). Leaping to Conclusions: Why Premise Relevance Affects Argument Strength. Cognitive Science 40 (1):n/a-n/a.
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