Pumping the Brakes on Psychosocial Acceleration Theory: Revisiting its Underlying Assumptions
Bibliographic record
Abstract
Psychosocial Acceleration Theory (PAT) is a popular evolutionary psychology theory that applies the biological concept of life history theory to understanding individual differences in human behavior and development. PAT argues that during a critical period in early childhood, exposure to harsh and/or unpredictable conditions leads individuals to accelerate their pubertal maturation and engage in more mating effort alongside less parental (and potentially somatic) investment (and vice versa in response to benign or predictable cues.) A large body of literature has found small, but significant, empirical effects in support of these patterns. However, a separate body of research has increasingly revealed a number of significant challenges to the underlying assumptions of PAT. The goal of my paper was to therefore review PAT’s assumptions and any challenges to those assumptions. My review shows that all of PAT’s underlying assumptions have at least modest challenges to their validity, with the majority of those assumptions facing more severe challenges to their validity. I therefore suggest that future research on PAT should focus on addressing these potential challenges to the theory so as to offer a stronger theoretical framework with which to explain current empirical data about human life histories.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.033 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".