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Cognitive Modeling of Automation Adaptation in a Time Critical Task.

Junya Morita1, Kazuhisa Miwa2, Akihiro Maehigashi3

  • 1Department of Behavior Informatics, Faculty of Informatics, Shizuoka University, Hamamatsu, Japan.

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|October 30, 2020
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Summary
This summary is machine-generated.

This study models how people adapt to automation in critical tasks. It shows how automation reliance changes with varying success rates, offering insights into trust in automated systems.

Keywords:
ACT-RSemi-Markov Decision Processautomated operationreinforcement learningtrust calibration

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Automation is increasingly used in time-critical tasks like vehicle operation.
  • Understanding human adaptation and trust in automation is crucial for safe and effective system design.

Purpose of the Study:

  • To develop and validate a cognitive model simulating human adaptation to automation in a time-critical tracking task.
  • To investigate how varying success probabilities of automatic and manual modes influence reliance on automation.

Main Methods:

  • Utilized the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture.
  • Implemented reinforcement learning methods (summation of rewards, gating mechanism).
  • Simulated a tracking task with productions for perception and motor control, updating utility values based on rewards.

Main Results:

  • The model successfully simulated key behavioral trends, including tracking accuracy, automation use ratio, and mode-switching frequency.
  • Varying success probabilities demonstrated shifts in reliance between automatic and manual modes.
  • The model's simulated trends aligned with observed behavioral data, supporting its validity.

Conclusions:

  • Combining cognitive modeling paradigms offers practical solutions for understanding automation and trust.
  • The developed model provides a framework for exploring human-automation interaction dynamics.
  • Findings contribute to designing more effective and trustworthy automated systems.