Abstract summary 

Social media is implicated today in an array of mental health concerns. While concerns around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience, however, are now poised to shed light on this matter. In this paper, we approach the phenomenon of social media addiction through the lens of the active inference framework. According to this framework, predictive agents like us use a 'generative model' of the world to predict our own incoming sense data and act to minimize any discrepancy between the prediction and incoming signal (prediction error). In order to live well and be able to act effectively to minimize prediction error, it is vital that agents like us have a generative model, which not only accurately reflects the regularities of our complex environment but is also flexible and dynamic and able to stay accurate in volatile and turbulent circumstances. In this paper, we propose that some social media platforms are a spectacularly effective way of warping an agent's generative model and of arresting the model's ability to flexibly track and adapt to changes in the environment. We go on to investigate cases of digital tech, which do not have these adverse effects and suggest-based on the active inference framework-some ways to understand why some forms of digital technology pose these risks, while others do not.

Authors & Co-authors:  White Clark Miller

Study Outcome 

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