Novelty–variety as a candidate basic psychological need: New evidence across three studies
Bibliographic record
Abstract
This paper investigates the plausibility of novelty–variety as a potential basic psychological need in a series of three studies. Using criteria proposed by Baumeister and Leary (Psychol Bull 117:497–529, 1995) and Ryan and Deci (in Self-determination theory: basic psychological needs in motivation, development, and wellness. Guilford Publishing, New York, 2017) to establish a motive as a basic human need, we focus on those criteria where evidence is lacking. Specifically, we examine whether novelty–variety is distinct from other needs in Basic Psychological Need Theory (BPNT) proposed by Self-Determination Theory (SDT), whether its absence results in adverse effects and its satisfaction uniquely predicts well-being outcomes, and whether the effects are different across age and personality. In Study 1, participants (N = 202) rated novelty–variety and needs from BPNT (competence, autonomy, relatedness) in three domains to assess its independence from these needs and the extent to which novelty–variety uniquely relates to domain-specific well-being. In Study 2 (N = 414), the fulfillment of novelty–variety and two BPNT needs (autonomy and relatedness) was experimentally manipulated in work-related vignettes, further showing that unsatisfied novelty–variety is related to lower well-being. Finally, the third study (N = 599) accounts for some of the limitations in Study 2 and examines the criteria of universality. Based on the examined criteria, all three studies provide support for further considering novelty–variety as a potential basic psychological need.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".