Valenced tactile information is evoked by neutral visual cues following emotional learning
Bibliographic record
Abstract
Learning which stimuli in our environment co-occur with painful or pleasurable events is critical for survival. Previous research has established the basic neural and behavioral mechanisms of aversive and appetitive conditioning; however, it is unclear precisely what information content is learned. Here we examined the degree to which aspects of the unconditioned stimulus (US)-sensory information versus affective salience-are transferred to the conditioned stimulus (CS). To decode what stimuli features (e.g., valence vs. discriminative somatosensation) are represented in patterns of brain activation elicited during appetitive (soft touch) and aversive (painful touch) conditioning to faces, a novel approach to using modeling with representational similarity analysis (RSA) based on theoretically driven representational patterns of interest (POIs) was applied to fMRI data. Once associations were learned through conditioning, globally, the CS reactivated US representational patterns showing conditioning-dependent reactivation in specific high-order brain regions: In the dorsal anterior cingulate cortex, the CS reactivated patterns associated with the affective salience of the US-suggesting that, with affective conditioning, these regions carry forward the affective associations of the experience.
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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) | 0.002 | 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".