A meta-analytic review of cultural variation in affect valuation.
Bibliographic record
Abstract
What affective states do people ideally want to feel and why? In Affect Valuation Theory, Tsai et al. (2006) proposed and observed that (a) how people would ideally like to feel (their "ideal affect") differs from how they actually feel (their "actual affect"), and (b) cultural factors shape people's ideal affect even more than their actual affect. In this individual participant data meta-analysis, we reexamined these two premises in a combined data file of over 31,000 participants from 124 data sets collected by different research teams across the world. Consistent with Tsai et al., we observed that (a) actual affect and ideal affect are empirically distinct constructs, and (b) cultural differences in ideal affect are larger in magnitude than cultural differences in actual affect. These findings held across research teams, participant populations, and publication status. Importantly, most cultural differences in ideal affect endured over time, including European Americans' greater valuation of high arousal positive states compared to East Asian Americans and East Asians. New patterns also emerged: European Americans valued low arousal positive states more over time; differences in ideal affect emerged among specific East Asian cultural groups; and socioeconomic status, gender, and age were also associated with differences in ideal affect. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.033 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".