Sampling Assumptions in Inductive Generalization

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...

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Bibliographic Details
Published in:Cognitive science 2012-03, Vol.36 (2), p.187-223
Main Authors: Navarro, Daniel J., Dry, Matthew J., Lee, Michael D.
Format: Article
Language:English
Subjects:
Australia
Bayes Theorem
Bayesian modeling
Biological and medical sciences
Cognition & reasoning
Cognition. Intelligence
Concept Formation
Decision making
Foreign Countries
Fundamental and applied biological sciences. Psychology
Generalization
Generalization (Psychology)
Humans
Individual Differences
Inductive inference
Learning
Logical Thinking
Models, Psychological
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Reasoning. Problem solving
Sampling
Stimulus Generalization
Undergraduate Students
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Summary: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.
ISSN:0364-0213
1551-6709
DOI:10.1111/j.1551-6709.2011.01212.x