Ventromedial prefrontal cortex is not critical for behavior change without external reinforcement
Bibliographic record
Abstract
Abstract Cue-approach training (CAT) is a novel paradigm that has been shown to induce preference changes towards items without external reinforcements. In the task the mere association of a neutral cue and a speeded button response has been shown to induce a behavioral change lasting months. This paradigm includes several phases whereby after the training of individual items, behavior change is manifested through binary choices of items with similar initial values. Neuroimaging data have implicated the ventromedial prefrontal cortex (vmPFC) during the choice phase of this task. However, it still remains unclear what are the underlying neural mechanisms during training. Here, we sought to determine whether the ventromedial frontal cortex (VMF) is critical for the non-reinforced preference change induced by CAT. For this purpose, eleven participants with focal lesions involving the VMF and 30 healthy age-matched controls performed the CAT. We found that at the individual level, a similar proportion of VMF and healthy participants showed a preference shift following CAT. The VMF group performed similarly to the healthy age-matched control group in the ranking and training phases. As a group the healthy age-matched controls exhibited a behavior change, but the VMF participants as a group did not. We did not find an association between individual lesion patterns and performance in the task. We conclude that a fully intact VMF is not critical to induce non-externally reinforced preference change and suggest potential mechanisms for this novel type of behavioral change.
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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 | 0.000 |
| 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) | 0.002 | 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".