The reciprocal relationship between episodic memory and future thinking: How the outcome of predictions is subsequently remembered
Bibliographic record
Abstract
Evidence suggests that memory is involved in making simulations and predictions about the future (i.e., future thinking), but less work has examined how the outcome of those predictions (whether events play out as predicted or expected) subsequently affects episodic memory. In this investigation, we examine whether memory is better for outcomes that are consistent with predictions, or whether memory is enhanced for outcomes that are inconsistent with predictions, after the predicted event occurs. In this experiment, participants learned a core trait associated with social targets (e.g., high in extroversion), before making predictions about behaviors targets would perform. Participants then were shown behaviors the social targets actually performed (i.e., prediction outcome), which was either consistent or inconsistent with predictions. After that, participants completed a memory test (recognition; recall) for the prediction outcomes. For recognition, the results revealed better memory for outcomes that were consistent with traits associated with targets (i.e., trait-consistent outcomes), compared to outcomes that were inconsistent (i.e., trait-inconsistent outcomes). Finding a memory advantage for trait-consistent outcomes suggests that outcomes that are in line with the contents of memory (e.g., what one knows; schemas) are more readily remembered than those that are inconsistent with memory, which may reflect an adaptive memory process. For recall, memory did not differ between trait-consistent and trait-inconsistent outcomes. Altogether, the results of this experiment advance understanding of the reciprocal relationship between episodic memory and future thinking and show that outcome of predictions has an influence on subsequent episodic memory, at least as measured by recognition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".