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

Explanatory pitfalls and rule-based driver models.

J A Michon1

  • 1Traffic Research Centre, University of Groningen, Haren, The Netherlands.

Accident; Analysis and Prevention
|August 1, 1989
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

Unifying congnition: Has it all been put together?

The Behavioral and brain sciences·2014
Same author

The effect of depth of processing on temporal judgment tasks.

Acta psychologica·1986
Same author

Attentional effort and cognitive strategies in the processing of temporal information.

Annals of the New York Academy of Sciences·1984
Same author

Mapping mental load in car driving.

Ergonomics·1978
Same author

Programs and "programs" for sequential patterns in motor behavior.

Brain research·1974
Same author

Human information processing--with and without drugs.

Psychiatria, neurologia, neurochirurgia·1973
Same journal

Assessing autonomous driving performance and environmental influencing factors using real-world operational trajectory data.

Accident; analysis and prevention·2026
Same journal

Multi-scale modeling of electric vehicle fatal crash risk: uncovering spatial heterogeneity and infrastructure-land use coupling mechanisms.

Accident; analysis and prevention·2026
Same journal

Differential sensitivity of self-reported driving and collision measures to aspects of shiftwork, sleep, and fatigue.

Accident; analysis and prevention·2026
Same journal

Delving into the visual attention of pedestrians during street crossing under time pressure: An eye-tracking approach.

Accident; analysis and prevention·2026
Same journal

Differentiating high-frequency and high-severity hotspots: A robust risk-evolution-volume (REV) framework.

Accident; analysis and prevention·2026
Same journal

Modeling takeover decisions in driving automation: a multilevel drift-diffusion model (MDDM) framework integrating human, system, and environmental factors.

Accident; analysis and prevention·2026
See all related articles

Driver behavior models require distinct explanations: aggregate population trends or individual information processing. Confusing these levels creates theoretical issues, necessitating a functionalistic approach for better driver behavior theories.

Area of Science:

  • Cognitive Psychology
  • Human Factors Engineering
  • Transportation Science

Background:

  • Driver models often conflate aggregate population behavior with individual information processing.
  • This confounding leads to theoretical problems like circular reasoning and anthropomorphism.
  • Existing models may not accurately represent the complexities of driver decision-making.

Purpose of the Study:

  • To analyze the confounding problem in driver behavior models.
  • To advocate for a radical functionalistic (process-level) approach.
  • To identify promising modeling techniques for driver behavior.

Main Methods:

  • Conceptual analysis of the confounding issue in driver modeling.
  • Theoretical arguments for a functionalistic perspective.

Related Experiment Videos

  • Evaluation of rule-based modeling and production system architectures.

    • Main Results:

    • The study identifies significant theoretical problems arising from conflating rational/intentional and functional levels of explanation.
    • A radical functionalistic approach is proposed as a solution to these theoretical issues.
    • Rule-based modeling using advanced production systems is highlighted as a promising direction.

    Conclusions:

    • A clear distinction between aggregate and individual-level explanations is crucial for robust driver models.
    • Adopting a functionalistic, process-level approach can resolve theoretical inconsistencies.
    • Advanced rule-based modeling offers a viable path toward more accurate driver behavior theories.