Personality modulates the effects of emotional arousal and valence on brain activation
Bibliographic record
Abstract
The influence of personality on the neural correlates of emotional processing is still not well characterized. We investigated the relationship between extraversion and neuroticism and emotional perception using functional magnetic resonance imaging (fMRI) in a group of 23 young, healthy women. Using a parametric modulation approach, we examined how the blood oxygenation level dependent (BOLD) signal varied with the participants' ratings of arousal and valence, and whether levels of extraversion and neuroticism were related to these modulations. In particular, we wished to test Eysenck's biological theory of personality, which links high extraversion to lower levels of reticulothalamic-cortical arousal, and neuroticism to increased reactivity of the limbic system and stronger reactions to emotional arousal. Individuals high in neuroticism demonstrated reduced sustained activation in the orbitofrontal cortex (OFC) and attenuated valence processing in the right temporal lobe while viewing emotional images, but an increased BOLD response to emotional arousal in the right medial prefrontal cortex (mPFC). These results support Eysenck's theory, as well as our hypothesis that high levels of neuroticism are associated with attenuated reward processing. Extraversion was inversely related to arousal processing in the right cerebellum, but positively associated with arousal processing in the right insula, indicating that the relationship between extraversion and arousal is not as simple as that proposed by Eysenck.
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 | 0.000 | 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 | 0.000 |
| 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 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".