Cognition and Learning in Decision-Making as Compensatory Mechanism for Emotional Processing Deficit
Bibliographic record
Abstract
The functional roles of ventromedial prefrontal cortex (VMPF) and amygdala in affecting emotional processing in decision-making have been raised in support of the somatic marker hypothesis. However, later studies demonstrated challenges to such support based on preserved cognition in the form of reversal learning in VMPF damaged patients tested with a shuffled variant of Iowa Gambling Task. This finding provides implications for cognitive neglect in somatic marker hypothesis with its magnified emphasis on the link between somatic markers and emotion-guided decision-making. It also suggests that cognition could compensate for emotion impairment in the absence of crucial prefrontal cortical region needed for low-risk choice and decision-making. Emotional somatic marker signaling is proposed to be an assistive initiation mechanism for choice decision-making between gains and losses instead of a fixated necessity in the process, and that it works in concert with concurrent conscious knowledge and cognition of the situation, building upon the nature of close connections between the VMPF and other brain region(s).
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".