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Digital Being: social media and the predictive mind.

Ben White1, Andy Clark1,2, Mark Miller3,4

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Social media may warp our internal models of the world, contributing to addictive behaviors. Active inference offers a framework to understand why some digital technologies are harmful while others are not.

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active inferenceaddictiondepressionmental healthsocial media

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Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Psychology

Background:

  • Social media use is linked to mental health issues.
  • The cognitive mechanisms behind social media addiction are not well understood.
  • Understanding why users persist with detrimental social media engagement is crucial.

Purpose of the Study:

  • To explain social media addiction using the active inference framework.
  • To investigate how social media platforms can disrupt cognitive processes.
  • To differentiate between harmful and non-harmful digital technologies.

Main Methods:

  • Utilizing the active inference framework to model user-environment interactions.
  • Analyzing the role of generative models in predicting and minimizing prediction error.
  • Examining how digital technologies affect an agent's generative model flexibility.

Main Results:

  • Social media platforms can distort an agent's generative model.
  • This distortion impairs the model's ability to adapt to environmental changes.
  • Certain digital technologies pose risks by warping generative models.

Conclusions:

  • Active inference provides a mechanistic explanation for social media addiction.
  • Understanding generative model dynamics can help identify risks associated with digital technologies.
  • This framework can inform the design of healthier digital experiences.