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Model-based prioritization for acquiring protection
Sarah M Tashjian1, Toby Wise1,2, Dean Mobbs1,3
1Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America.
Plos Computational Biology
|December 19, 2022
Summary
Acquiring protection uses more model-based control than seeking rewards or avoiding punishment. This flexible decision-making in protection is driven by context and outcome valence.
Area of Science:
- Cognitive Neuroscience
- Decision Science
- Behavioral Economics
Background:
- Prospective planning is crucial for mitigating harm, but its computational basis, especially for protection, remains unclear.
- Understanding how protection decisions differ from other goal-directed actions like reward acquisition is essential for a comprehensive model of decision-making.
- Existing research often treats different action motivations under similar computational frameworks, potentially overlooking unique characteristics.
Purpose of the Study:
- To computationally compare the decision-making processes underlying protection acquisition, reward acquisition, and punishment avoidance.
- To identify overlapping and distinct computational features across these three types of prospective actions.
- To investigate the role of context and valence in shaping decision strategies for protection.
Main Methods:
- Employed computational modeling, specifically model-based reinforcement learning, across three independent studies with a total of 600 human participants.
- Analyzed behavioral data to assess learning rates and the degree of model-based control for each action type.
- Investigated the influence of context-valence asymmetry on decision strategies.
Main Results:
- Decisions aimed at acquiring protection demonstrated a significantly higher degree of model-based control compared to reward acquisition or punishment avoidance.
- No significant differences were observed in the learning rates across the three conditions.
- The unique context-valence asymmetry of protection motivated increased deployment of flexible decision strategies.
Conclusions:
- Model-based control in decision-making is influenced not only by outcome valence but critically by the context in which outcomes are encountered.
- Protection acquisition uniquely engages more sophisticated, flexible decision strategies compared to reward seeking or threat avoidance.
- Findings suggest distinct computational architectures for different types of prospective actions, highlighting the specialized nature of protection.
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