Is There a Common Network for Processing Reward and Aversion in the Brain?
Bibliographic record
Abstract
How do our brains process and attach positive and negative value to the objects around us, the sensations we feel, and the experiences that we have? One method of examining these questions is to detect, using functional magnetic resonance imaging (fMRI), which areas of the human brain are activated when subjects are exposed to rewarding and aversive stimuli. Although many fMRI studies have concentrated on identifying a network of areas that become active in processing either reward or aversion, there is evidence of significant overlap between the “reward” and “aversion” networks, suggesting that the brain might process rewarding and aversive stimuli in a similar manner regardless of valence. Thus, a meta-analysis of fMRI studies involving rewarding and aversive stimuli was undertaken to determine the areas of the brain that are commonly and differentially activated by reward and aversion. The preliminary results indicate that regions of the prefrontal cortex, anterior cingulate cortex, amygdala, nucleus accumbens, hippocampus, and basal ganglia were commonly activated by rewarding and aversive stimuli, while areas including the insula, midcingulate cortex, and parts of the hippocampus were differentially activated. Locating such commonalities and differences might help in our understanding of how the brain ascribes value to our environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".