کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6839067 | 618456 | 2014 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Implicit and explicit training in the mitigation of cognitive bias through the use of a serious game
ترجمه فارسی عنوان
آموزش مستمر و صریح در کاهش تعصب شناختی از طریق استفاده از یک بازی جدی
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کلمات کلیدی
بازی های ویدئویی، تعصب شناختی، آموزش، کاهش تقلبی، تست آموزشی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
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 (NÂ =Â 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Human Behavior - Volume 37, August 2014, Pages 307-318
Journal: Computers in Human Behavior - Volume 37, August 2014, Pages 307-318
نویسندگان
Norah E. Dunbar, Claude H. Miller, Bradley J. 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,