Infants consider both the sample and the sampling process in inductive generalization

The ability to make inductive inferences from sparse data is a critical aspect of human learning. However, the properties observed in a sample of evidence depend not only on the true extension of those properties but also on the process by which evidence is sampled. Because neither the property exte...

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Main Authors: Gweon, Hyowon, Tenenbaum, Joshua B., Schulz, Laura E.
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Language:en_US
Published: National Academy of Sciences (U.S.) 2012
Online Access:http://hdl.handle.net/1721.1/70984
https://orcid.org/0000-0002-2981-8039
https://orcid.org/0000-0002-1925-2035
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author Gweon, Hyowon
Tenenbaum, Joshua B.
Schulz, Laura E.
author2 Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
author_facet Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Gweon, Hyowon
Tenenbaum, Joshua B.
Schulz, Laura E.
author_sort Gweon, Hyowon
collection MIT
description The ability to make inductive inferences from sparse data is a critical aspect of human learning. However, the properties observed in a sample of evidence depend not only on the true extension of those properties but also on the process by which evidence is sampled. Because neither the property extension nor the sampling process is directly observable, the learner's ability to make accurate generalizations depends on what is known or can be inferred about both variables. In particular, different inferences are licensed if samples are drawn randomly from the whole population (weak sampling) than if they are drawn only from the property's extension (strong sampling). Given a few positive examples of a concept, only strong sampling supports flexible inferences about how far to generalize as a function of the size and composition of the sample. Here we present a Bayesian model of the joint dependence between observed evidence, the sampling process, and the property extension and test the model behaviorally with human infants (mean age: 15 months). Across five experiments, we show that in the absence of behavioral cues to the sampling process, infants make inferences consistent with the use of strong sampling; given explicit cues to weak or strong sampling, they constrain their inferences accordingly. Finally, consistent with quantitative predictions of the model, we provide suggestive evidence that infants’ inferences are graded with respect to the strength of the evidence they observe.
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spelling mit-1721.1/709842022-09-30T18:29:22Z Infants consider both the sample and the sampling process in inductive generalization Gweon, Hyowon Tenenbaum, Joshua B. Schulz, Laura E. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Schulz, Laura E. Gweon, Hyowon Tenenbaum, Joshua B. Schulz, Laura E. The ability to make inductive inferences from sparse data is a critical aspect of human learning. However, the properties observed in a sample of evidence depend not only on the true extension of those properties but also on the process by which evidence is sampled. Because neither the property extension nor the sampling process is directly observable, the learner's ability to make accurate generalizations depends on what is known or can be inferred about both variables. In particular, different inferences are licensed if samples are drawn randomly from the whole population (weak sampling) than if they are drawn only from the property's extension (strong sampling). Given a few positive examples of a concept, only strong sampling supports flexible inferences about how far to generalize as a function of the size and composition of the sample. Here we present a Bayesian model of the joint dependence between observed evidence, the sampling process, and the property extension and test the model behaviorally with human infants (mean age: 15 months). Across five experiments, we show that in the absence of behavioral cues to the sampling process, infants make inferences consistent with the use of strong sampling; given explicit cues to weak or strong sampling, they constrain their inferences accordingly. Finally, consistent with quantitative predictions of the model, we provide suggestive evidence that infants’ inferences are graded with respect to the strength of the evidence they observe. National Science Foundation (U.S.) (Faculty Early Career Development Award) John Templeton Foundation (Award) James S. McDonnell Foundation (Collaborative Interdisciplinary Grant on Causal Reasoning) 2012-06-01T16:04:34Z 2012-06-01T16:04:34Z 2010-05 2010-03 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/70984 Gweon, H., J. B. Tenenbaum, and L. E. Schulz. “Infants Consider Both the Sample and the Sampling Process in Inductive Generalization.” Proceedings of the National Academy of Sciences 107.20 (2010): 9066–9071. Web. https://orcid.org/0000-0002-2981-8039 https://orcid.org/0000-0002-1925-2035 en_US http://dx.doi.org/10.1073/pnas.1003095107 Proceedings of the National Academy of Sciences of the United States of America Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences (U.S.) PNAS
spellingShingle Gweon, Hyowon
Tenenbaum, Joshua B.
Schulz, Laura E.
Infants consider both the sample and the sampling process in inductive generalization
title Infants consider both the sample and the sampling process in inductive generalization
title_full Infants consider both the sample and the sampling process in inductive generalization
title_fullStr Infants consider both the sample and the sampling process in inductive generalization
title_full_unstemmed Infants consider both the sample and the sampling process in inductive generalization
title_short Infants consider both the sample and the sampling process in inductive generalization
title_sort infants consider both the sample and the sampling process in inductive generalization
url http://hdl.handle.net/1721.1/70984
https://orcid.org/0000-0002-2981-8039
https://orcid.org/0000-0002-1925-2035
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