Pain feedback interferes with reward positivity production
Bibliographic record
Abstract
The reinforcement learning (RL) theory of the reward positivity (RewP) proposes that RewP indexes a reward prediction error (RPE) signal processed in the anterior cingulate cortex (ACC). According to this theory, RewP is an event-related potential (ERP) that is more positive-going for feedback stimuli that predict better-than-expected outcomes (positive feedback) than for feedback stimuli that predict worse-than-expected outcomes (negative feedback). Despite strong evidence for this hypothesis, findings have been equivocal for tasks involving painful outcomes. We hypothesized that the RewP is modulated by high-level task goals such that outcomes that are congruent with the goals elicit positive RPEs even if their immediate consequences are negative. Accordingly, changes in high-level task goals should modulate RewP amplitude for tasks that involve seeking pain compared to tasks that involve avoiding pain. We recorded the electroencephalogram from participants who were instructed to navigate a virtual T-Maze to find reward-predictive feedback in a reward condition and pain-predictive feedback in a pain condition. We expected more positive-going ERPs to reward feedback in the reward condition and more positive-going ERPs to pain feedback in the pain condition. Despite behavioral results indicating that participants complied with task instructions, contrary to our predictions, we did not find a RewP to pain feedback. We suggest that pain feedback interfered with the effect of high-level task goals on RewP amplitude, which is indicative of conflict at different levels of task hierarchy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".