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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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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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An Introduction to Predictive Processing Models of Perception and Decision-Making.

Mark Sprevak1, Ryan Smith2

  • 1School of Philosophy, Psychology and Language Sciences, University of Edinburgh.

Topics in Cognitive Science
|October 29, 2023
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Summary

This review introduces predictive coding and active inference, two key theories explaining how the brain uses predictive models for perception, cognition, and action. Both theories aim to minimize variational free energy for Bayesian inference.

Keywords:
Active inferenceBayesian inferenceFree energy minimizationGenerative modelsPartially observable Markov decision processes (POMDPs)Predictive codingPredictive processing

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • The predictive processing framework offers a unified view of brain function.
  • Predictive coding and active inference are two dominant theories within this framework.
  • Understanding these theories is crucial for advancing our knowledge of perception, cognition, and action.

Purpose of the Study:

  • To provide an up-to-date introduction to predictive coding and active inference.
  • To review the evolution and core principles of predictive coding.
  • To explore the recent developments and applications of active inference.

Main Methods:

  • Review of existing literature on predictive coding and active inference.
  • Analysis of theoretical claims at computational, algorithmic, and implementation levels.
  • Exploration of mathematical connections to Bayesian inference and variational free energy.

Main Results:

  • Predictive coding evolved from efficient coding in vision to a general model of perception, cognition, and motor control.
  • Active inference extends predictive coding by focusing on planning and decision-making using expected free energy.
  • Both theories utilize variational free energy minimization for biologically plausible Bayesian inference.

Conclusions:

  • Predictive coding and active inference provide powerful, complementary frameworks for understanding brain function.
  • Active inference offers novel approaches to modeling planning and decision-making.
  • Future research should further develop and integrate these theoretical models.