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Updated: Oct 16, 2025
12:09
Studying Food Reward and Motivation in Humans
Published on: March 19, 2014
23.8K
Hunger improves reinforcement-driven but not planned action.
Maaike M H van Swieten1, Rafal Bogacz1, Sanjay G Manohar2
1Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.
Cognitive, Affective & Behavioral Neuroscience
|October 15, 2021
Summary
Hunger enhances decision-making by increasing reliance on the model-free learning system, not the model-based system. This shift boosts performance through primitive reinforcement learning, offering adaptive benefits.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Behavioral Economics
Background:
- Human decisions are governed by two learning systems: model-free (reflexive) and model-based (planned).
- Metabolic states like hunger may influence these decision-making systems differently.
- The model-based system is context-sensitive, while model-free relies on primitive reinforcement.
Purpose of the Study:
- To investigate the general effects of hunger on model-free and model-based decision-making systems.
- To determine if hunger modulates the flexibility or primitive reinforcement aspects of decision control.
Main Methods:
- Utilized a well-established two-stage sequential decision-making task.
- Dissociated the contributions of model-based and model-free control in participants.
- Measured performance changes under conditions of hunger.
Main Results:
- Hunger significantly enhanced overall task performance.
- This performance increase was driven by heightened model-free control.
- Model-based control remained unaffected by hunger.
Conclusions:
- Hunger exerts a generalized effect on decision-making, favoring primitive reinforcement learning.
- Enhanced reliance on the model-free system can lead to adaptive benefits in certain situations.
- This highlights a direct link between biological drives and fundamental learning mechanisms in decision control.
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