Hypernatural Monitoring: A Social Rehearsal Account of Smartphone Addiction
Bibliographic record
Abstract
We present a deflationary account of smartphone addiction by situating this purportedly antisocial phenomenon within the fundamentally social dispositions of our species. While we agree with contemporary critics that the hyper-connectedness and unpredictable rewards of mobile technology can modulate negative affect, we propose to place the locus of addiction on an evolutionarily older mechanism: the human need to monitor and be monitored by others. Drawing from key findings in evolutionary anthropology and the cognitive science of religion, we articulate a hypernatural monitoring model of smartphone addiction grounded in a general social rehearsal theory of human cognition. Building on recent predictive-processing views of perception and addiction in cognitive neuroscience, we describe the role of social reward anticipation and prediction errors in mediating dysfunctional smartphone use. We conclude with insights from contemplative philosophies and harm-reduction models on finding the right rituals for honoring social connections and setting intentional protocols for the consumption of social information.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".