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    When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data. 
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    • When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data.
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    When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data.

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    Accepted version (471.1Kb)
    Volume
    41 Suppl 5
    Pagination
    1155 - 1167
    DOI
    10.1111/cogs.12356
    Journal
    Cogn Sci
    Metadata
    Show full item record
    Abstract
    Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event never occurs. A rational analysis using Bayesian inference predicts that inferences from absent data should depend on how much the absence is expected to occur, with less probable absences being more salient. We tested this prediction in two experiments in which we elicited people's judgments about patterns in the data as a function of absence salience. We found that people were able to decide that absences either were mere coincidences or were indicative of a significant pattern in the data in a manner that was consistent with predictions of a simple Bayesian model.
    Authors
    Hsu, AS; Horng, A; Griffiths, TL; Chater, N
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/11070
    Collections
    • Electronic Engineering and Computer Science [2816]
    Language
    eng
    Licence information
    This is the peer reviewed version of the following article: Hsu, Anne. "When absence of evidence is evidence of absence: Rational inferences from absent data" Cognitive Science Journal. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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