The TIE-93: a Facial Emotion Recognition Test Adapted for Culturally, Linguistically, and Educationally Diverse Alzheimer’s Dementia Patients in France
Bibliographic record
Abstract
OBJECTIVE: Emotion recognition tests are essential for differential diagnostics when assessing patients with Alzheimer's disease (AD) dementia. However, there remains a lack of emotion recognition tests appropriate for culturally and educationally diverse populations. The aim of this study was to develop an emotion recognition test (the TIE-93) appropriate for these populations. We then examined whether the TIE-93 could reduce emotion recognition performance differences between populations with a native French versus a culturally and educationally diverse background (participants who had immigrated to France). This was assessed by comparing performance between controls of each cultural group. We also assessed the effect of demographic variables on TIE-93 test performance and whether performance in an AD patient group was consistent with the research literature. METHODS: Fifty-seven patients with AD dementia and 240 healthy controls, from native French and culturally and educationally diverse backgrounds, were included in the study. The TIE-93 is composed of eight panels with photos of actors displaying six basic emotions. Participants were asked to identify which of the six facial expressions displayed matched an oral description of a context. RESULTS: When comparing French and culturally and educationally diverse controls, Quade's ANCOVA revealed that there remained an effect of culture and education on TIE-93 test performance. Nonetheless, while controlling for years of education, age, sex, and cultural group, patients with AD dementia scored significantly more poorly than controls, specifically for most negative emotions. CONCLUSION: The TIE-93 represents a first step toward developing appropriate emotion recognition tests for culturally and educationally diverse populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".