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An Active Inference Model of the Optimism Bias.

Elizabeth L Fisher1, Christopher J Whyte1,2,3, Jakob Hohwy1

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Summary
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Optimism bias, overestimating good outcomes, is adaptive and a target for mental health interventions. This study introduces a computational model to understand optimism

Keywords:
active inferencebelief updatingdepressionoptimism bias

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Area of Science:

  • Cognitive Science
  • Computational Psychiatry
  • Neuroscience

Background:

  • Optimism bias involves overestimating positive outcomes and underestimating negative ones, linked to better quality of life.
  • This bias is considered adaptive and a potential target for mental health interventions, particularly for conditions like major depressive disorder.

Purpose of the Study:

  • To develop a domain-general formal model of optimism bias using the active inference framework.
  • To provide a computational basis for understanding the emergence, expression, and behavioral impact of optimism bias.
  • To lay groundwork for future research on optimism as a mental health intervention.

Main Methods:

  • Proposed a model of optimism bias as high precision likelihood biased towards positive outcomes within the active inference framework.
  • Simulated the loss of optimism during development due to negative event exposure.
  • Grounded the model in empirical literature using belief updating and two-armed bandit tasks.

Main Results:

  • Demonstrated how developmental differences in optimism are expressed in belief updating tasks.
  • Showcased the effect of optimism on action selection in a modified two-armed bandit task.
  • Provided simulations illustrating the model's capacity to explain optimism bias.

Conclusions:

  • The developed computational model offers a basis for understanding optimism bias.
  • The model explains how optimism bias emerges, is expressed in tasks, and influences decision-making.
  • This work supports future research into optimism as a therapeutic intervention for mental health conditions.