Investigating how reward retroactively improves memory for associations and items
Bibliographic record
Abstract
Given we cannot remember all our experiences, it seems intuitive that we should prioritize events of consequence, like those that lead to a reward. However, rewards have had inconsistent effects on people’s memory for preceding neutral experiences in experiments, raising questions as to whether our minds are equipped to remember adaptively. Here, we used online testing in a registered report to investigate whether rewards tend to retroactively enhance certain types of memories. Consistent with prior research and theories involving how dopamine modulates the hippocampus, we hypothesized that reward would retroactively enhance memory in a delay-dependent manner, emerging only after a period of consolidation. Crucially, we further predicted that this long-delay modulation of memory would be stronger for associative memory than for item recognition—a factor not previously considered despite its clear theoretical grounding. Our planned confirmatory analyses did not reveal any evidence for retroactive effects of reward on either associative or item memory. However, in exploratory analyses examining only individuals who were behaviourally sensitive to the reward manipulation, we found evidence that reward retroactively enhanced associative—but not item—memory in a delay-dependent manner. This work sheds light important open questions about the mechanisms by which reward retroactively enhances memory, elucidating the conditions under which this key aspect of adaptive memory emerges.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".