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A Predictive Model of Situation Awareness with ACT-R

  • Kim, Junghwan (Korea University, Department of Industrial Management Engineering) ;
  • Myung, Rohae (Korea University, Department of Industrial Management Engineering)
  • 투고 : 2016.03.17
  • 심사 : 2016.05.18
  • 발행 : 2016.08.31

초록

Objective: The aim of this study is to model all levels of situation awareness (SA), which would be able to predict situation awareness quantitatively. Background: When measuring situation awareness, directly measuring SA methods such as SAGAT and SART have been utilized. Several approaches (cognitive modeling approaches) were introduced to model SA but level 3 SA was not completed. For real-life situation, however, it is necessary to detect the problematic level of SA rather than overall SA. Therefore, we proposed a new model of all levels of SA in this study. Method: In order to model all levels of SA, this study chose factors in ACT-R architecture through literature review. ATC (Air Traffic Control)-related simulation task was video-taped to analyze human behaviors in order to model all levels of SA including level 3. Results: As a result, regression analyses show that cognitive activities (neural activations) represented for all levels of SA were highly correlated with SAGAT. Conclusion: In conclusion, neural activations in ACT-R could be proved to be effective to model all levels of SA. Application: Our SA model could be used to predict all levels of SA quantitatively without directly measuring the SA of operators.

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참고문헌

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