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Directed Convergence in Stable Percept Acquisition.

Bruce M. Bennett1, Rachel C. Lehman

  • 1University of California, Irvine

Journal of Mathematical Psychology
|October 5, 2001
PubMed
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This study introduces directed convergence as a method for achieving stable percepts through nondeductive inference. It explores Bayesian probabilistic inference, updating priors and posteriors iteratively for enhanced perceptual stability.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Mathematical Psychology

Background:

  • Perceptual capacity is modeled as nondeductive inference, mapping premises to conclusions.
  • An instantaneous percept is a single inference step; a stable percept is a sequence of these.
  • Existing Bayesian inference often uses a fixed prior, updating only the posterior.

Purpose of the Study:

  • To define and implement a strategy for acquiring stable percepts.
  • To explore Bayesian probabilistic/recursive inference within a metric space framework.
  • To adapt directed convergence for Bayesian inference and provide numerical control.

Main Methods:

  • Representing perceptual capacity as a function from premise sets to conclusion sets.
  • Defining stable percepts as convergent sequences of instantaneous percepts.

Related Experiment Videos

  • Introducing directed convergence as a strategy for stable percept acquisition in metric spaces.

    • Main Results:

    • Developed directed convergence for stable percept acquisition.
    • Investigated Bayesian probabilistic/recursive inference with non-trivial prior/posterior updates.
    • Demonstrated implementation of directed convergence in Bayesian inference using the L(infinity) metric.

    Conclusions:

    • Directed convergence provides a framework for achieving stable percepts.
    • Bayesian probabilistic/recursive inference offers a dynamic alternative to classical methods.
    • The L(infinity) metric enables numerical control of Bayesian directed convergence.