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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the brain can only use...
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An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
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Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates.

Alec Solway1, Matthew M Botvinick

  • 1Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08540, USA.

Psychological Review
|January 11, 2012
PubMed
Summary

This study proposes a new theory for goal-directed decision-making, suggesting the brain uses a probabilistic model and Bayesian inversion. This framework advances understanding beyond habit formation models.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry
  • Machine Learning

Background:

  • Reward-based decision-making involves habit and goal-oriented systems.
  • Existing computational theories primarily address habit formation.
  • Formalizing goal-directed decision-making processes remains a challenge.

Purpose of the Study:

  • To outline a novel computational theory for goal-directed decision-making.
  • To propose that the brain implements a probabilistic generative model of reward.
  • To suggest goal-directed decisions arise from Bayesian inversion of this model.

Main Methods:

  • Drawing on cognitive neuroscience, animal conditioning, psychology, and machine learning.
  • Developing a computational framework for goal-directed decision-making.
  • Implementing simulations to test the proposed theory.

Main Results:

  • The proposed theory models goal-directed decision-making through Bayesian inversion.
  • Simulations successfully replicate benchmark behavioral and neuroscientific findings.
  • The framework generates novel, testable predictions for future research.

Conclusions:

  • The brain's reward processing network may implement a probabilistic generative model.
  • Bayesian inversion offers a formal mechanism for goal-directed choice.
  • This theory connects to existing models of perceptual choice and decision-making.