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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Model averaging, optimal inference, and habit formation.

Thomas H B FitzGerald1, Raymond J Dolan1, Karl J Friston1

  • 1Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK.

Frontiers in Human Neuroscience
|July 15, 2014
PubMed
Summary
This summary is machine-generated.

The brain may use Bayesian model averaging to choose the best environmental model for behavior. This approach balances model accuracy and complexity, potentially explaining complex cognitive phenomena.

Keywords:
Bayesian inferenceactive inferencehabitinterference effectpredictive coding

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • The brain is thought to perform approximate Bayesian inference for neuronal function.
  • Current models focus on inference and learning under a single assumed model.
  • Organisms must also determine the best model(s) of their environment for behavior.

Purpose of the Study:

  • To investigate the role of Bayesian model averaging in cognition.
  • To explore how Bayesian model averaging can be implemented in neuronal architectures.
  • To explain apparently suboptimal behaviors within a Bayesian inference framework.

Main Methods:

  • Outlining the principles of Bayesian model averaging.
  • Describing potential neuronal implementations of model averaging.
  • Analyzing the trade-off between model accuracy and complexity.

Main Results:

  • Bayesian model averaging provides a principled method for selecting environmental models.
  • Optimal inference requires balancing model accuracy and complexity.
  • Model averaging can explain phenomena like the interplay between goal-directed and habitual behavior.

Conclusions:

  • Bayesian model averaging is a potentially crucial cognitive mechanism.
  • It offers a framework for understanding complex behaviors and their apparent suboptimality.
  • This approach aligns with approximate Bayesian inference and plausible neural architectures.