When Absence of Evidence Is Evidence of Absence: Rational Inferences from Absent Data

Autor/inn/en	Hsu, Anne S.; Horng, Andy; Griffiths, Thomas L.; Chater, Nick
Titel	When Absence of Evidence Is Evidence of Absence: Rational Inferences from Absent Data
Quelle	In: Cognitive Science, 41 (2017), S.1155-1167 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12356
Schlagwörter	Statistical Inference; Bayesian Statistics; Evidence; Prediction; Probability; Experiments; Decision Making + Suchen Sie Ihr Suchwort? Inferential statistics; Schließende Statistik; Evidenz; Vorhersage; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Erprobung; Decision-making; Entscheidungsfindung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01