Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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 summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Significance of histologic pattern of carcinoma and sarcoma components on survival outcomes of uterine carcinosarcoma.

Annals of oncology : official journal of the European Society for Medical Oncology·2016
Same author

Urinary tract infections in solid organ transplantation.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2013
Same author

A case of trisomy 12 mosaicism presenting antenatally with fetal cardiomyopathy.

Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology·2011
Same author

Effects of automation and task load on task switching during human supervision of multiple semi-autonomous robots in a dynamic environment.

Ergonomics·2010
Same author

The role of microscopic hematuria in the evaluation of urologic malignancy in renal transplant recipients.

Transplantation proceedings·2010
Same author

Selective attention to face identity and color studied with f MRI.

Human brain mapping·2010

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.

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.