Characterizing the contributions of cue familiarity for the retrieval of autobiographical memories
Bibliographic record
Abstract
Abstract Retrieving an autobiographical memory requires a cue to initiate processes related to accessing and then elaborating on a past personal experience. Prior work has shown that the familiarity of a cue can influence the autobiographical memory retrieval process. Extending on this work, we tested how different aspects of cue familiarity—i.e., amount of past exposure and amount of semantic knowledge associated with the cue concept—can affect how we access and remember in detail autobiographical memories. In Experiment 1, we measured reaction times to access and retrieve memories in response to cue words. In Experiment 2 we examined the details with which participants described memories in response to cues. For both experiments, participants provided estimates of lifetime exposure and semantic knowledge associated with each cue. In Experiment 1, we found lifetime exposure, independently of estimates of semantic knowledge, led to quicker memory access and in Experiment 2, we found both lifetime exposure and semantic knowledge interactively enhanced the ability to described detailed memories. These results provide new evidence that distinct features of familiar cues—lifetime exposure and semantic knowledge—differently contribute to how autobiographical memories are retrieved and described.
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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 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".