Digital Being: social media and the predictive mind
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".