Neural evidence for cultural differences in the valuation of positive facial expressions
Bibliographic record
Abstract
European Americans value excitement more and calm less than Chinese. Within cultures, European Americans value excited and calm states similarly, whereas Chinese value calm more than excited states. To examine how these cultural differences influence people's immediate responses to excited vs calm facial expressions, we combined a facial rating task with functional magnetic resonance imaging. During scanning, European American (n = 19) and Chinese (n = 19) females viewed and rated faces that varied by expression (excited, calm), ethnicity (White, Asian) and gender (male, female). As predicted, European Americans showed greater activity in circuits associated with affect and reward (bilateral ventral striatum, left caudate) while viewing excited vs calm expressions than did Chinese. Within cultures, European Americans responded to excited vs calm expressions similarly, whereas Chinese showed greater activity in these circuits in response to calm vs excited expressions regardless of targets' ethnicity or gender. Across cultural groups, greater ventral striatal activity while viewing excited vs. calm expressions predicted greater preference for excited vs calm expressions months later. These findings provide neural evidence that people find viewing the specific positive facial expressions valued by their cultures to be rewarding and relevant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".