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International Conference on Human Interaction and Emerging Technologies

IHIET 2019: Human Interaction and Emerging Technologies pp 78–83Cite as

Judgment Analysis for Real-Time Decision Support Using the Cognitive Shadow Policy-Capturing System

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1018)

Abstract

The present work introduces a prototype intelligent cognitive assistant that continuously learns the decision pattern of the user online, instantly recognizes deviations from that pattern as potential errors and then alerts the user accordingly. We investigated the potential of this prototype system using a human-in-the-loop experiment designed to assess impacts on decision making performance, workload and trust in the decision support capability. Study participants interacted with a naval air-defence testbed to classify radar contacts as friendly, uncertain or hostile based on track parameters displayed on screen. The between-group experimental design included a control condition and two decision support conditions (with system reliability provided either offline or online). Results showed that both decision support conditions significantly improved task accuracy compared to the control condition. The advisory system was successful at improving human performance without burdening the user with excessive additional workload, even when providing reliability information in real time.

Keywords

  • Judgment analysis
  • Policy capturing
  • Decision support
  • Trust
  • Machine learning
  • Classification
  • Cognitive engineering
  • Dynamic decision making
  • Workload
  • Cognitive modeling
  • Human-in-the-loop experiment

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Acknowledgments

We are thankful to Wheejae Kim for assistance in data collection and to Frédéric Morin, Philippe Michaud-Côté, Sébastien Carrier, Jean-Sébastien Thivierge, Jonathan Ouellet, Jonathan Richard, Jean-Baptiste Bebaah, Michel Mercier, Marlène Gauthier and Nicolas Déry for software development and testing. This work was supported by an R&D partnership grant awarded to Sébastien Tremblay and to Heather Neyedli from the National Sciences and Engineering Research Council of Canada (NSERC) with Defence R&D Canada – Atlantic and Thales Research and Technology Canada.

Author information

Authors and Affiliations

  1. Thales Research and Technology, Quebec City, Canada

    Daniel Lafond

  2. School of Psychology, Université Laval, Quebec City, Canada

    Katherine Labonté & Sébastien Tremblay

  3. Defence Research and Development Canada - Atlantic, Halifax, Canada

    Aren Hunter

  4. School of Health and Human Performance, Dalhousie University, Halifax, Canada

    Heather F. Neyedli

Authors
  1. Daniel Lafond
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  2. Katherine Labonté
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  3. Aren Hunter
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  4. Heather F. Neyedli
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  5. Sébastien Tremblay
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Corresponding author

Correspondence to Daniel Lafond .

Editor information

Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Prof. Tareq Ahram

  2. Université de Reims Champagne Ardenne, GRESPI, Reims, France

    Prof. Redha Taiar

  3. Laboratoire Motricité Humaine, Expertise, Sport, Santé, Université Côte d’Azur, Nice Cedex 3, France

    Prof. Serge Colson

  4. IFMK Niçois, Université Côte d’Azur, Nice Cedex 3, France

    Prof. Arnaud Choplin

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Lafond, D., Labonté, K., Hunter, A., Neyedli, H.F., Tremblay, S. (2020). Judgment Analysis for Real-Time Decision Support Using the Cognitive Shadow Policy-Capturing System. In: Ahram, T., Taiar, R., Colson, S., Choplin, A. (eds) Human Interaction and Emerging Technologies. IHIET 2019. Advances in Intelligent Systems and Computing, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-030-25629-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-25629-6_13

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  • Publisher Name: Springer, Cham

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