Age-related changes in frequency of mind-wandering and task-related interferences during memory encoding and their impact on retrieval
Bibliographic record
Abstract
During the performance of cognitive tasks such as memory encoding, attention can become decoupled from the external environment and instead focused on internal thoughts related to the appraisal of the current task (task-related interferences; TRI), or personal thoughts unrelated to the task at hand (mind-wandering; MW). However, the association between the frequency of these thoughts experienced at encoding and retrieval accuracy in young and older adults remains poorly understood. In this study young and older adults encoded lists of words using one of two encoding tasks: judging whether words are man-made/natural (objective task), or whether they are pleasant/neutral (subjective task). We measured the frequency of TRI and MW at encoding, and related them to retrieval accuracy in both age groups. We found that encoding task influenced the type of internal thoughts experienced by young, but not older, adults: young exhibited greater MW in the subjective vs the objective task, and greater TRI in the objective vs subjective encoding task. Second, across both tasks we found marked age-related decreases in both MW and TRI at encoding, and frequency of these thoughts negatively impacted memory retrieval in young adults only. We discuss these findings in relation to current theories of ageing, attention and memory.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".