Does face recognition correlate with narcissim? A replication.
Bibliographic record
Abstract
Face recognition plays an essential role in our social interactions with others. To this end, understanding individual differences that relate to our face recognition ability is one facet of understanding the factors that play a role in face recognition deficits. In a recent study, Giacomin, Brinton, and Rule (2021) found that narcissistic individuals exhibited poorer facial recognition when asked to recall social and non-social stimuli compared to non-narcissistic individuals. They reasoned that narcissistic individuals exhibit visual recognition memory deficits due to an inflated self-focus, limiting their processing of the external environment. In an attempt to replicate this novel finding, 125 healthy participants completed the Cambridge Face Memory Task and completed the Narcissistic Personality Inventory (NPI-16) to quantify narcissistic personality traits. We found no relationship between face recognition ability on the CFMT and narcissism score on the NPI-16 (r = -0.08, p = 0.37). The CFMT is a well-validated tool for assessing the recognition of novel faces, and differed from the old/new face recognition test used by Giacomin et al. Therefore, differences in results may have stemmed from differences in the face recognition tool used. In future research, we hope to additionally examine the relationship between face recognition and non-face object recognition.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".