The episodic memory system supports memory for choice: Assessing the behavioural and neural consequences
Bibliographic record
Abstract
Choices made in everyday life are highly variable.Sometimes, you may find yourself choosing between two similar items (e.g., breakfast foods to eat) and other times between two dissimilar items (e.g., what to buy with a gift certificate).The goal of the present study was to understand how the similarity of choice options affect how they are later remembered.We hypothesized that choosing between similar as compared to dissimilar options would evoke a comparison-based strategy (evaluating the items with respect to one another), fostering a relational form of encoding and leading to better memory for choice options.In Experiment 1, participants reported their strategy when choosing between pairs of similar or dissimilar items, revealing a higher likelihood of using a comparison-based strategy for similar options.In Experiment 2, we tested memory for both options after participants made choices between similar or dissimilar items, finding that memory was better for similar than dissimilar item pairs.In Experiment 3, we examined the strategies used when choosing between pairs of similar or dissimilar items as well as memory for the choice options.Confirming the results of the previous experiments, we found that participants were more likely to use a comparison-based strategy when choosing between similar than dissimilar items and that positive effect of similarity on memory was stronger for unchosen than chosen items, when controlling for strategy use.We interpret our results as evidence that option similarity impacts the mnemonic processes used during choice, altering what we remember about our choices.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".