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Related Experiment Videos

Human versus automation in responding to failures: an expected-value analysis.

T B Sheridan1, R Parasuraman

  • 1Massachusetts Institute of Technology, Cambridge, USA. sheridan@mit.edu

Human Factors
|January 2, 2001
PubMed
Summary

This study offers a simple method to determine if humans or automation are better for failure detection. It uses expected-value decision theory, considering costs, benefits, and error probabilities for optimal system design.

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Area of Science:

  • Decision Theory
  • Human-Computer Interaction
  • System Design and Evaluation

Background:

  • Failure detection is critical in many systems, requiring careful consideration of human versus automation roles.
  • Existing methods for assigning tasks between humans and automation lack a unified, analytical approach.
  • Signal detection theory provides a framework for analyzing detection tasks but needs extension for decision-making.

Purpose of the Study:

  • To provide a simple analytical criterion for deciding between human and automation for failure detection tasks.
  • To establish a framework for comparing different automation modes in failure detection.
  • To offer a method applicable to the design and evaluation of systems involving human or automated decision-making.

Main Methods:

Keywords:
NASA Discipline Space Human FactorsNon-NASA Center

Related Experiment Videos

  • Utilizes expected-value decision theory, analogous to signal detection.
  • Requires input on miss (false negative) and false alarm (false positive) probabilities for both human and automation.
  • Incorporates costs and benefits of correct/incorrect decisions and the prior probability of failure.

    • Main Results:

    • A clear analytical criterion is derived for optimal human-automation allocation in failure detection.
    • The method allows for quantitative comparison between different automation systems.
    • Demonstrates the utility of expected-value decision theory in optimizing task allocation.

    Conclusions:

    • The proposed criterion offers a robust method for determining the most effective agent (human or automation) for failure detection.
    • This approach is broadly applicable to various systems requiring human or automated decision-making.
    • The framework supports informed design choices and system evaluations, enhancing overall system performance and reliability.