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

Anne S. Hsu, Andy Horng, Thomas L. Griffiths, Nick Chater

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1155-1167
Number of pages13
JournalCognitive Science
Volume41
DOIs
StatePublished - May 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Absent data
  • Bayesian modeling
  • Category learning
  • Rational analysis

Fingerprint Dive into the research topics of 'When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data'. Together they form a unique fingerprint.

Cite this