Chinese college students' ability to recognize facial expressions based on their meaning‐in‐life profiles: An eye‐tracking study
Bibliographic record
Abstract
OBJECTIVE: People can be categorized into one of four meaning-in-life profiles: High Presence High Search (HPHS), High Presence Low Search (HPLS), Low Presence High Search (LPHS), and Low Presence Low Search (LPLS).The main goal of this study is to provide a theoretical explanation for why Chinese people with different meaning-in-life profiles have different mental health levels than Western people, based on their emotional-cognitive-processing ability. METHOD: We adopted eye-movement analysis and recognition-judgment experimental paradigm concerning absolute-recognition judgment and relative-recognition judgment in our study. Moreover, we applied a multifactor and multilevel mixed-experimental design. We selected 118 participants for the experiments from the 788 Chinese college students who responded. RESULTS: Our results showed that HPHS individuals preferred positive-emotion pictures, LPLS individuals preferred negative-emotion pictures, HPLS individuals preferred positive- and neutral-emotion pictures, and LPHS individuals preferred neutral-emotion pictures. Moreover, HPHS individuals were better at accurately processing facial expression from pictures, while LPLS individuals lacked such ability. The fine-processing ability of HPLS and LPHS individuals was lower than that of HPHS yet higher than that of LPLS individuals. Moreover, the features of HPLS individuals were closer to HPHS, while those of LPHS individuals were closer to LPLS. CONCLUSIONS: Our findings support the hypothesis that meaning-in-life profiles have different immediate processing abilities and preferences regarding facial expression recognition and different emotional-cognitive-processing ability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".