Amygdala and cortical substrates of subjective well-being: pathways to reduced aggression in emotional processing contexts
Bibliographic record
Abstract
Subjective well-being has been implicated in the regulation of aggressive behavior, potentially through its influence on mood and neural processing. However, the underlying neuropsychological mechanisms remain insufficiently understood. This study aimed to elucidate the neural correlates of subjective well-being and examine its potential association with aggression using a two-part investigation. The first involved a functional MRI study (n = 111), focusing on amygdala responses to emotional face processing and broader cortical activation related to subjective well-being. The second involved a larger behavioral sample (n = 627) to assess the relationship between subjective well-being and aggression, as well as the mediating role of emotional variables. Behaviorally, subjective well-being was inversely associated with anger, hostility, and overall aggression, and with negative affect, anxiety, and depression. Mediation analyses demonstrated significant effects of mood (negative affect, depression, and anxiety) in linking subjective well-being to aggression. Neuroimaging results revealed that individuals with higher subjective well-being displayed attenuated amygdala reactivity to fearful faces. Additionally, intersubject representational similarity analyses demonstrated that individuals with similar subjective well-being levels shared more convergent neural activation patterns in visual (e.g., occipital pole, lateral occipital cortex, middle temporal gyrus, and fusiform) and the emotional network (e.g., insula), but not within the amygdala. These findings provide novel insights into the neuropsychological mechanisms linking well-being to emotional regulation and aggression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".