Implicit and explicit training in the mitigation of cognitive bias through the use of a serious game

Norah E. Dunbar, Claude H. Miller, Bradley Adame, Javier Elizondo, Scott N. Wilson, Brianna L. Lane, Abigail Allums Kauffman, Elena Bessarabova, Matthew L. Jensen, Sara K. Straub, Yu Hao Lee, Judee K. Burgoon, Joseph J. Valacich, Jeffrey Jenkins, Jun Zhang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Heuristics can interfere with information processing and hinder decision-making when more systematic processes that might lead to better decisions are ignored. Based on the heuristic-systematic model (HSM) of information processing, a serious training game (called MACBETH) was designed to address and mitigate cognitive biases that interfere with the analysis of evidence and the generation of hypotheses. Two biases are the focus of this paper - fundamental attribution error and confirmation bias. The efficacy of the serious game on knowledge and mitigation of biases was examined using an experiment in which participants (N = 703) either played the MACBETH game or watched an instructional video about the biases. Results demonstrate the game to be more effective than the video at mitigating cognitive biases when explicit training methods are combined with repetitive play. Moreover, explicit instruction within the game provided greater familiarity and knowledge of the biases relative to implicit instruction. Suggestions for game development for purposes of enhancing cognitive processing and bias mitigation based on the MACBETH game design are discussed.

Original languageEnglish (US)
Pages (from-to)307-318
Number of pages12
JournalComputers in Human Behavior
Volume37
DOIs
StatePublished - Aug 2014

Keywords

  • Bias mitigation
  • Cognitive biases
  • Instructional testing
  • Training
  • Video games

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

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