Interplay between midbrain and dorsal anterior cingulate regions arbitrates lingering reward effects on memory encoding
Bibliographic record
Abstract
Rewarding events enhance memory encoding via dopaminergic influences on hippocampal plasticity. Phasic dopamine release depends on immediate reward magnitude, but presumably also on tonic dopamine levels, which may vary as a function of the average accumulation of reward over time. Using model-based fMRI in combination with a novel associative memory task, we show that immediate reward magnitude exerts a monotonically increasing influence on the nucleus accumbens, ventral tegmental area (VTA), and hippocampal activity during encoding, and enhances memory. By contrast, average reward levels modulate feedback-related responses in the VTA and hippocampus in a non-linear (inverted U-shape) fashion, with similar effects on memory performance. Additionally, the dorsal anterior cingulate cortex (dACC) monotonically tracks average reward levels, while VTA-dACC functional connectivity is non-linearly modulated (inverted U-shape) by average reward. We propose that the dACC computes the net behavioral impact of average reward and relays this information to memory circuitry via the VTA.
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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) | 0.001 | 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".