Temporal self-regulation theory: A model for individual health behavior
Bibliographic record
Abstract
Human behavior often seems “maladaptive”, “self-defeating”, or “dysfunctional” to the observer. Upon closer examination, the rationality of human behavior largely depends on the temporal frame adopted; behaviors judged to be maladaptive in the long-run are usually driven by a strongly favorable balance of immediate costs and benefits. That is, many ‘‘maladaptive’’ behaviors are associated with substantial long-term costs and few (if any) long-term benefits; however, these same behaviors are frequently associated with many benefits and few costs for the individual at the time of action. In contrast, many avoided behaviors that seem ‘‘adaptive’’ to the outside observer, are in fact associated with substantial costs (and few benefits) at the time of action, leading to the perplexing but common state of affairs where individuals know ‘‘what is good for them’’, but do not do it. We present a new theoretical framework—Temporal Self-Regulation Theory—as a way of understanding human behavior in general, and those special instances of seemingly ‘‘self-defeating’’ behavior that have important implications for physical health. This theoretical framework incorporates thinking about temporal aspects of behavioral contingencies and the biological roots of self-regulation to make sense of human behavioral patterns that seem to represent, on the surface, significant deviations from rationality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".