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Published on: August 2, 2018
The basal ganglia optimize decision making over general perceptual hypotheses
Nathan F Lepora1, Kevin N Gurney
1Department of Psychology, University of Sheffield, Sheffield S10 2TP, UK. n.lepora@sheffield.ac.uk
Neural Computation
|August 28, 2012
Summary
The basal ganglia optimize decision-making by implementing Bayesian sequential analysis for perceptual hypotheses. This framework explains how these nuclei process sensory data for optimal action selection.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Decision Science
Background:
- The basal ganglia are crucial subcortical nuclei for action selection.
- A recent theory links basal ganglia function to optimal statistical hypothesis testing for decision-making.
Purpose of the Study:
- To extend the existing model of basal ganglia function to general Bayesian sequential analysis.
- To propose a practical theory for perceptual hypothesis testing and decision optimization.
Main Methods:
- Demonstrated the extension of the basal ganglia model to arbitrary probability distributions.
- Showed how evidence in the model can represent log likelihoods, log-likelihood ratios, or log odds.
Main Results:
- The basal ganglia model naturally encompasses general Bayesian sequential analysis.
- The model provides a framework for understanding cortical processing of sensory data.
Conclusions:
- The basal ganglia optimize decision-making over general perceptual hypotheses represented in the cortex.
- This theory has implications for understanding cortical encoding, cortico-basal ganglia anatomy, and reinforcement learning.
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