<i>Homo temporus</i> : Seasonal Cycles as a Fundamental Source of Variation in Human Psychology
Bibliographic record
Abstract
Many animal species exhibit seasonal changes in their physiology and behavior. Yet despite ample evidence that humans are also responsive to seasons, the impact of seasonal changes on human psychology is underappreciated relative to other sources of variation (e.g., personality, culture, development). This is unfortunate because seasonal variation has potentially profound conceptual, empirical, methodological, and practical implications. Here, we encourage a more systematic and comprehensive collective effort to document and understand the many ways in which seasons influence human psychology. We provide an illustrative summary of empirical evidence showing that seasons impact a wide range of affective, cognitive, and behavioral phenomena. We then articulate a conceptual framework that outlines a set of causal mechanisms through which seasons can influence human psychology-mechanisms that reflect seasonal changes not only in meteorological variables but also in ecological and sociocultural variables. This framework may be useful for integrating many different seasonal effects that have already been empirically documented and for generating new hypotheses about additional seasonal effects that have not yet received empirical attention. The article closes with a section that provides practical suggestions to facilitate greater appreciation for, and systematic study of, seasons as a fundamental source of variation in human 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".