Extraversion predicts superior face-specific recognition ability, but through experience, not positive affect
Bibliographic record
Abstract
Experience is an important factor in developing face recognition ability. Given that extraverts show increased social involvement, extraversion may be associated with greater experience with faces, thereby leading to enhanced face recognition ability. However, extraverts also characteristically display high positive affect – an affective state thought to bias visual processing to be more global or holistic in nature. Given the large body of evidence suggesting that faces are processed holistically, positive affect may lead to superior face processing for extraverts aside from their increased social experiences (i.e. positive affect may mediate any relationship between extraversion and face recognition ability). To examine the relationships between extraversion, positive affect, and face and non-face recognition ability, university student participants completed self-report measures of personality and affect before completing the Cambridge Face Memory Task (CFMT), and a matched control task assessing recognition of cars (Cambridge Car Memory Task, CCMT). Each measure was taken twice, separated by one week. All measures showed very high test-retest reliability and scores were therefore averaged across both sessions. A face-specific recognition advantage was observed for individuals high in extraversion in that extraversion predicted better face recognition, even when controlling for non-face recognition. No relationships were observed between state or trait positive affect and recognition ability. Further, statistically controlling for affect strengthened the relationship between extraversion and face-specific recognition ability, suggesting that there is something inherent to extraversion aside from positive affect that benefits face recognition. We suggest that extraverts gregariousness allows greater opportunities for developing face expertise. Meeting abstract presented at VSS 2015
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 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".