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

Identification Model Based on the Maximum Information Entropy Principle.

Hisao Miyano1

  • 1Chiba University

Journal of Mathematical Psychology
|February 17, 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

Pain Candidate Genes 5-HTTLPR and COMT Affect Anxiety and Mood in Japanese Ballet Dancers: A Cross-Sectional and Longitudinal Study.

Sports (Basel, Switzerland)·2024
Same author

Effects of Different Exercise Conditions on Antioxidant Potential and Mental Assessment.

Sports (Basel, Switzerland)·2021
Same journal

Corrigendum to "An entropy model of decision uncertainty" [Journal of Mathematical Psychology 125 (2025), 102919].

Journal of mathematical psychology·2025
Same journal

An entropy model of decision uncertainty.

Journal of mathematical psychology·2025
Same journal

How do people build up visual memory representations from sensory evidence? Revisiting two classic models of choice.

Journal of mathematical psychology·2024
Same journal

Experiment-based calibration in psychology: Optimal design considerations.

Journal of mathematical psychology·2024
Same journal

Expressions for Bayesian confidence of drift diffusion observers in fluctuating stimuli tasks.

Journal of mathematical psychology·2024
Same journal

A Statistical Foundation for Derived Attention.

Journal of mathematical psychology·2023
See all related articles

A novel probabilistic model using maximum information entropy offers a new approach to stimulus identification. This framework unifies existing models like multidimensional scaling (MDS) and general recognition theory, providing a more comprehensive understanding of decision-making.

Area of Science:

  • Cognitive Science
  • Mathematical Psychology
  • Information Theory

Background:

  • Existing models for stimulus identification often rely on specific assumptions, such as Luce's choice rule or predefined similarity functions.
  • There is a need for a unified theoretical framework that can encompass various identification and choice models.
  • Ashby's general recognition theory provides a foundational model for understanding stimulus identification.

Purpose of the Study:

  • To propose a new theoretical approach to stimulus identification based on a probabilistic multidimensional model.
  • To derive the multidimensional scaling (MDS) choice model from this new framework without relying on Luce's choice rule or similarity functions.
  • To clarify the relationship between the new model, the MDS choice model, and Ashby's general recognition theory.

Related Experiment Videos

Main Methods:

  • Development of a probabilistic multidimensional model grounded in the principle of maximum information entropy.
  • Theoretical derivation of the multidimensional scaling (MDS) choice model as a special case of the proposed approach.
  • Comparison of the new approach with Ashby's general recognition theory and its optimal identification model.

Main Results:

  • The proposed maximum information entropy model successfully derives the multidimensional scaling (MDS) choice model.
  • The new approach theoretically demonstrates that it encompasses both the MDS choice model and Ashby's optimal identification model as special cases.
  • A novel model for similarity judgment is proposed and shown to be comparable to Ashby's extended similarity model.

Conclusions:

  • The maximum information entropy principle provides a powerful and unifying foundation for stimulus identification models.
  • The new theoretical approach offers a more general framework that integrates existing models, enhancing our understanding of cognitive processes.
  • The derived similarity judgment model offers a new perspective for analyzing perceptual judgments.