The role of episodic memory in imagining autobiographical events: the influence of event expectancy and context familiarity
Bibliographic record
Abstract
Episodic memory plays a common role in constructing mental representations of past and imagined autobiographical events. Research has suggested that certain factors will affect how episodic memory is used during mental construction, such as the expectancy that an event will occur and the familiarity with an event's context. The aim of the current study was to understand how these factors affect episodic memory engagement and subjective experience during event imagination. In a within-subjects design, participants viewed context cues (high or low in familiarity), described imagined autobiographical events (expected or not expected to occur in these contexts) and rated their experience. 24-hours later, participants recalled and described the same events. We found that expectancy of the imagined events was associated with quicker access and increased episodic detail generation, regardless of context familiarity. Additionally, both event expectancy and context familiarity affected the subjective quality of the imagined events. Examining the episodic details in descriptions after the delay revealed comparable effects of these two factors. Our results underscore the importance of event expectancy in recruiting episodic memory for imagined autobiographical experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".