Indirect cueing elicits distinct types of autobiographical event representations
Bibliographic record
Abstract
Studies that distinguish among believed memories, believed-not-remembered events (e.g., family stories), and nonbelieved memories (i.e., memories no longer believed to have occurred) typically rely on experimenter provided or overtly elicited events. These methods may mis-estimate the frequency and nature of such events in everyday memory. Three studies examined whether such events would be elicited via indirect cueing. Participants recalled and rated events on autobiographical belief, recollection, and other characteristics associated with remembering. All three event types resulted, but with a low rate of nonbelieved memories. Believed and nonbelieved memories received similar perceptual and re-experiencing ratings, and both exceeded believed-not-remembered events. Lifespan cueing found nonbelieved memories to be most frequent in middle childhood (ages 6-11). Cueing for "events" vs. "memories" revealed that "memory" cues lead to retrieval of a more homogeneous set of events and differences when predicting autobiographical belief and recollection. These studies support the distinction between autobiographical belief and recollection for autobiographical events.
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 | 0.000 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".