Modulating Episodic Memory Alters Risk Preference during Decision-making
Bibliographic record
Abstract
When choosing between options that vary in risk, we often rely on our experience with options-our episodic memories-to make that choice. Although episodic memory has been demonstrated to be critically involved in value-based decision-making, it is not clear how these memory processes contribute to decision-making that involves risk. To investigate this issue, we tested a group of participants on a repeated-choice risky decision-making task. Before completing this task, half of the participants were given a well-validated episodic induction task-a brief training procedure in recollecting the details of a past experience-known to engage episodic memory processes, and the other half were given a general impressions induction task. Our main finding was that risk-taking following the general impressions induction task was significantly lower than following the episodic induction task. In a follow-up experiment, we tested risk-taking in another group of participants without any prior induction task and found that risk-taking from this no-induction (baseline) group was more similar to the episodic induction than to the general impression group. Overall, these findings suggest engaging episodic memory processes when learning about decision outcomes can alter apparent risk-taking behavior in decision-making from 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".