TY - GEN
T1 - Empirical tests of large-scale collaborative recall
AU - Gates, Monica A.
AU - Suchow, Jordan W.
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© CogSci 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - agent-based modeling
KW - collaborative inhibition
KW - collaborative memory
KW - crowdsourcing
KW - network transmission
UR - http://www.scopus.com/inward/record.url?scp=85137286925&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137286925&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85137286925
T3 - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition
SP - 403
EP - 408
BT - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
T2 - 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Y2 - 26 July 2017 through 29 July 2017
ER -