Bibliographic record
Abstract
Using memory to guide decisions allows past experience to improve future outcomes. However, the circumstances that modulate how and when memory influences decisions are not well understood. Here, we report that the use of memories to guide decisions depends on the context in which these decisions are made. We show that decisions made in the context of familiar images are more likely to be influenced by past events than are decisions made in the context of novel images (Experiment 1), that this bias persists even when a temporal gap is introduced between the image presentation and the decision (Experiment 2), and that contextual novelty facilitates value learning whereas familiarity facilitates the retrieval and use of previously learned values (Experiment 3). These effects are consistent with neurobiological and computational models of memory, which propose that familiar images evoke a lingering "retrieval state" that facilitates the recollection of other episodic memories. Together, these experiments highlight the importance of episodic memory for decision-making and provide an example of how computational and neurobiological theories can lead to new insights into how and when different types of memories guide our choices. (PsycINFO Database Record
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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 | 0.012 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".