Empirical tests of large-scale collaborative recall

Monica A. Gates, Jordan W. Suchow, Thomas L. Griffiths

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

1 Scopus citations

Abstract

Much of our knowledge is transmitted socially rather than through firsthand experience. Even our memories depend on recollections of those around us. Surprisingly, when people recall memories with others, they do not reach the potential number of items they could have recalled alone. This phenomenon is called collaborative inhibition. Recently, Luhmann and Rajaram (2015) analyzed the dynamics of collaborative inhibition at scale with an agent-based model, extrapolating from previous small-scale laboratory experiments. We tested their model against human data collected in a large-scale experiment and found that participants demonstrate non-monotonicities not evident in these predictions. We next analyzed memory transmission beyond directly interacting agents by placing agents into networks. Contrary to model predictions, we observed high similarity only within directly interacting pairs. By comparing behavior to model predictions in large-scale experiments, we reveal unexpected results that motivate future work in elucidating the algorithms underlying collaborative memory.

Original languageEnglish (US)
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages403-408
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Externally publishedYes
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: Jul 26 2017Jul 29 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/26/177/29/17

All Science Journal Classification (ASJC) codes

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

Keywords

  • agent-based modeling
  • collaborative inhibition
  • collaborative memory
  • crowdsourcing
  • network transmission

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