Is Our Self Nothing but Reward? Neuronal Overlap and Distinction between Reward and Personal Relevance and Its Relation to Human Personality
Bibliographic record
Abstract
BACKGROUND: The attribution of personal relevance, i.e. relating internal and external stimuli to establish a sense of belonging, is a common phenomenon in daily life. Although previous research demonstrated a relationship between reward and personal relevance, their exact neuronal relationship including the impact of personality traits remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: Using functional magnetic resonance imaging, we applied an experimental paradigm that allowed us to explore the neural response evoked by reward and the attribution of personal relevance separately. We observed different brain regions previously reported to be active during reward and personal relevance, including the bilateral caudate nucleus and the pregenual anterior cingulate cortex (PACC). Additional analysis revealed activations in the right and left insula specific for the attribution of personal relevance. Furthermore, our results demonstrate a negative correlation between signal changes in both the PACC and the left anterior insula during the attribution of low personal relevance and the personality dimension novelty seeking. CONCLUSION/SIGNIFICANCE: While a set of subcortical and cortical regions including the PACC is commonly involved in reward and personal relevance, other regions like the bilateral anterior insula were recruited specifically during personal relevance. Based on our correlation between novelty seeking and signal changes in both regions during personal relevance, we assume that the neuronal response to personally relevant stimuli is dependent on the personality trait novelty seeking.
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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".