An Adaptationist Framework for Personality Science
Bibliographic record
Abstract
The field of personality psychology aspires to construct an overarching theory of human nature and individual differences: one that specifies the psychological mechanisms that underpin both universal and variable aspects of thought, emotion, and behaviour. Here, we argue that the adaptationist toolkit of evolutionary psychology provides a powerful meta–theory for characterizing the psychological mechanisms that give rise to within–person, between–person, and cross–cultural variations. We first outline a mechanism–centred adaptationist framework for personality science, which makes a clear ontological distinction between (i) psychological mechanisms designed to generate behavioural decisions and (ii) heuristic trait concepts that function to perceive, describe, and influence others behaviour and reputation in everyday life. We illustrate the utility of the adaptationist framework by reporting three empirical studies. Each study supports the hypothesis that the anger programme—a putative emotional adaptation—is a behaviour–regulating mechanism whose outputs are described in the parlance of the person description factor called ‘Agreeableness’. We conclude that the most productive way forward is to build theory–based models of specific psychological mechanisms, including their culturally evolved design features, until they constitute a comprehensive depiction of human nature and its multifaceted variations. © 2020 European Association of Personality Psychology
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".