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In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
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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
PubMed
Summary
This summary is machine-generated.

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.

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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.