The distorting effect of deciding to stop sampling

Anna Coenen, Todd M. Gureckis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

People usually collect information to serve specific goals and often end up with samples that are unrepresentative of the underlying population. This can introduce biases on later judgments that generalize from these samples. Here we show that goals influence not only what information we collect, but also when we decide to terminate search. Using an optimal stopping analysis, we demonstrate that even when learners have no control over the content of a sample (i.e., natural sampling), the simple decision of when to stop sampling can yield sample distributions that are non-representative and could potentially bias future decision making. We test the prediction of these theoretical analyses with two behavioral experiments.

Original languageEnglish (US)
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsAnna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell
PublisherThe Cognitive Science Society
Pages2819-2824
Number of pages6
ISBN (Electronic)9780991196739
StatePublished - 2016
Event38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States
Duration: Aug 10 2016Aug 13 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Conference38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
Country/TerritoryUnited States
CityPhiladelphia
Period8/10/168/13/16

Keywords

  • decision-making
  • information search
  • sampling
  • stopping rules

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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