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Subliminal perception refers to the processing of sensory information that occurs below the level of conscious awareness. Researchers study subliminal perception by presenting a stimulus, such as a word or image, very quickly, typically around 50 milliseconds. This rapid presentation is often followed by another stimulus, such as a pattern of dots or lines, which blocks further mental processing of the initial stimulus. As a result, if participants cannot identify the initial stimulus better...
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Sequential Bayesian updating as a model for human perception.

Stefan Glasauer1

  • 1Computational Neuroscience, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.

Progress in Brain Research
|July 22, 2019
PubMed
Summary
This summary is machine-generated.

Sequential Bayesian updating models human perception biases like central tendency and range effects. This chapter details implementing Bayesian updating for random-change models using exact, Kalman filter, and particle filter methods, linking perception to action.

Keywords:
Central tendencyDecision makingKalman filterParticle filterProbabilistic modelRange effectSerial dependence

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Perception Psychology

Background:

  • Human perception exhibits systematic biases, including central tendency, range effects, and serial dependence.
  • Sequential Bayesian updating offers a computational framework to explain these perceptual biases.
  • Previous work introduced a random-change model that this chapter extends.

Purpose of the Study:

  • To introduce the core concepts of Bayesian updating within a random-change model framework.
  • To demonstrate practical implementation methods for sequential Bayesian updating.
  • To illustrate the integration of perceptual updating with action selection.

Main Methods:

  • Exact sequential updating using probability distributions.
  • Kalman filter for sequential updating with Gaussian distributions.
  • Particle filter for approximate sequential updating.
  • Coupling perception to action via posterior distribution analysis.

Main Results:

  • The chapter provides a comprehensive guide to implementing sequential Bayesian updating.
  • Demonstrates the utility of exact, Kalman, and particle filters for perceptual modeling.
  • Establishes a method for linking Bayesian perception to action selection.

Conclusions:

  • Sequential Bayesian updating is a viable model for explaining human perceptual biases.
  • Flexible computational methods allow for accurate and approximate sequential updating.
  • Perceptual-action coupling can be effectively modeled using posterior distributions from Bayesian updating.