Strategic cognitive sequencing: a computational cognitive neuroscience approach
Seth A Herd1, Kai A Krueger, Trenton E Kriete
1Department of Psychology, University of Colorado Boulder, Boulder, CO 80309, USA. seth.herd@gmail.com
Computational Intelligence and Neuroscience
|August 13, 2013
Summary
This study explores strategic cognitive sequencing, the brain's process for selecting cognitive tasks. It introduces neural network models to explain how brain regions coordinate for complex problem-solving.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
Background:
- Strategic cognitive sequencing, the brain's
- outer loop
- is crucial for multistep tasks but understudied.
- Recent advances in understanding neural computations enable new approaches to coordination.
Purpose of the Study:
- To investigate how the brain sequences cognitive processes for complex tasks.
- To propose neural network models for strategic cognitive sequencing.
Main Methods:
- Development of four preliminary neural network models.
- Modeling cooperation between prefrontal cortex (PFC) and basal ganglia (BG) for sequence learning.
- Modeling reward prediction in PFC and strategic decision-making in BG.
- Modeling sequence memorization involving PFC, BG, parietal cortex, and hippocampus.
- Modeling constraint satisfaction for plan generation.
Main Results:
- Models demonstrate trial-and-error learning of sequences by PFC and BG.
- PFC reward prediction aids BG strategic decisions.
- PFC, BG, parietal cortex, and hippocampus can memorize action sequences.
- Constraint satisfaction can generate abstract plans by linking states.
Conclusions:
- These models offer a framework for understanding strategic cognitive sequencing.
- Coordination between brain regions is key to complex cognition.
- Future research can build on these models to explore dynamic cognitive control.
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