Approaches to Cognitive Bias in Serious Games for Critical Thinking

This Panel discusses decision and learning theory and application in the design of serious games to train people for bias-free critical thinking, particularly in analytical domains. The theoretical understanding of cognitive bias will determine the shape of the cognitive work that is entrained by the games. Panelists will describe an approach to bias mitigation training that is premised on a notion that reasoning strategies can have heuristic value depending on circumstances. THE CHALLENGE PROBLEM AND A POTENTIAL APPROACH Cognitive bias can be an impediment to effective critical thinking, as in the domain of intelligence analysis. Several decision support environments have been based on the assumption that cognitive biases cannot be eliminated, but rather must be minimized through reliance on software systems utilizing normative methods and standards (Fan, McNeese and Yen, 2010; Koehler, Brenner and Griffin, 2002). For example, a software support system might attempt to mitigate confirmation bias by presenting the reasoner with disconfirming information bearing on a hypothesis under consideration, or by adjusting the weights assigned to information so as to conform to a normative or Bayesian model. Other mitigation strategies have relied on training in an attempt to: (1) sensitize decision makers to the conditions under which biases emerge, (2) help decision makers recognize when they are manifesting a bias, and (3) teach decision makers to compensate for errors resulting from the bias. Training programs exhibiting these strategies have met with mixed success (Adelman, Tolcott and Bresnick, 1993; Mautone, Spiker and Karp, 2006). Serious games are a promising approach to bias mitigation in critical thinking because they are likely to engage learners at a deeper level of involvement than some traditional and non-experiential tutoring approaches. Additionally, the use of games might allow training designers to present a broader range of variations than is possible with more traditional approaches, capturing the functional constraints that must be learned in order for a reasoner to progress from novice to expert. Functional constraints that are present in experiential learning often are not present in instruction that focuses solely on the acquisition of declarative knowledge. Panelist #1: John Flach Conventional vs. Open-Loop Models C op yr ig ht 2 01 2 by H um an F ac to rs a nd E rg on om ic s S oc ie ty , I nc . A ll rig ht s re se rv ed . D O I 1 0. 11 77 /1 07 11 81 31 25 61 06 4 PROCEEDINGS of the HUMAN FACTORS and ERGONOMICS SOCIETY 56th ANNUAL MEETING 2012 272