title: When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data.
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.affiliation: School of Electronic Engineering and Computer Science, Queen Mary, University of London.
authors.forename: Anne S
authors.initials: AS
authors.lastname: Hsu
authors.affiliation: Department of Psychology, University of California.
authors.forename: Andy
authors.initials: A
authors.lastname: Horng
authors.affiliation: Department of Psychology, University of California.
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.affiliation: Warwick Business School, University of Warwick.
authors.forename: Nick
authors.initials: N
authors.lastname: Chater
mesh_terms: D000328:Adult; D003210:Concept Formation / Q000502:physiology*; D006801:Humans; D007600:Judgment / Q000502:physiology*; D008960:Models, Psychological
keywords: Absent data; Bayesian modeling; Category learning; Rational analysis