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Active Inference Insights

Ryan Smith ~ Active Inference Insights 013 ~ Wellbeing, Modelling, Precision

May 3, 2024
Neuro-computationalist Ryan Smith discusses empirical computational psychiatry, in-silico simulations, and the role of aberrant precision-weighting in neuropsychological disorders. The podcast delves into optimizing well-being through free energy, decision uncertainty in mental health conditions, exposure therapy, preferences in active inference models, emotional granularity, and future work in the field.
01:28:03

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Understanding the difference between empirical computational psychiatry and in-silico simulations is crucial for modeling neuropsychological disorders.
  • The interplay between variational and expected free energy influences decision-making processes and action dynamics.

Deep dives

The Complexity of Subjective Well-Being and Active Inference Mechanisms

The discussion delves into the complexity of grounding subjective well-being in the parameters and mechanisms of active inference. While variational free energy aims to optimize posterior beliefs about states, expected free energy focuses on rewarding outcomes and information gain. The distinction highlights valency considerations for subjective well-being beyond basic biological fitness.

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