Narcissistic personality differences in facial emotional expression categorization
Bibliographic record
Abstract
Narcissistic Personality Disorder has been linked to a lack of empathy and a disrupted recognition of facial emotional expressions (Marissen, Deen, & Franken, 2012). To further investigate the link between narcissism and categorization of facial expressions, the performance and visual strategies in facial expression categorization of 20 healthy subjects were assessed using Bubbles (Gosselin & Schyns, 2001) and a separate expression categorization task involving fully-visible faces. The Bubbles task consisted in presenting sparse versions of emotional faces created by sampling facial information at random spatial locations and at five non-overlapping spatial frequency bands. Narcissism levels were evaluated using the Narcissistic Personality Inventory (NPI; Raskin & Hall, 1979). Each participant performed two categorization tasks with 4 facial expressions (anger, disgust, fear, happiness). NPI scores correlated positively with the number of Bubbles needed to maintain performance at 65% (r = 0.4634, p <0.05) and with reaction times in the task involving full faces (r = 0.4977, p <0.05). Classification images (CI) revealing what visual information correlated with participants accuracy were constructed separately for the most and less narcissistic subjects (z-scores higher than 0.5 or lower than -0.5) by performing a multiple linear regression on the bubbles locations and accuracy. The results shows that CIs for fear differ across groups (Zcrit = 3.36, p <0.05; corrected for multiple comparisons). Both groups use the mouth region but differ on which eye they use: narcissistic subjects using the left one. Our results are congruent with the alteration observed with clinical subjects in the performance at recognizing facial expressions (Marissen, Deen, & Franken, 2011). Furthermore, we show that in a non-clinical sample, the variations in performance are coupled with a different lateralisation bias in the eye utilisation during the processing of the fearful expression. Meeting abstract presented at VSS 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".