“Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences.”
– inform professor Anne S. Hsu and colleagues Andy Horng, Thomas L. Griffiths and Nick Chater in a new paper for the journal Cognitive Science.
“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.”
For further explorations regarding the implications of things which may or may not, to some extent or other, be there, also see: The Presence of the Absence of Absences + Maybe no data? Maybe no problem? + Non-ignor ble mis ingn ss + Almost nothings