Exploring the influence of temporal factors on age differences in working memory dual task costs.
Bibliographic record
Abstract
Working memory is defined by many as the system that allows us to simultaneously store information over brief time periods while engaging in other information processing activities. In a previous study (Rhodes, Jaroslawska et al. (2019) Journal of Experimental Psychology: General, 148, 1204-1227.) we found that retention of serially presented letters was disrupted by the introduction of an arithmetic processing task during a 10 second delay period. Importantly, the magnitude of this dual task disruption increased with age from 18 to 81. The demands of each task were adjusted prior to dual task so that age differences did not reflect baseline differences in single task performance. Motivated by these findings, theories of working memory, and additional analyses of processing reaction times from this previous experiment, we report two experiments, using the same tasks and adjustment procedure, attempting to modulate the magnitude of age differences in dual task effects via manipulations focused on time for encoding to-be-remembered material. Providing a delay prior to processing activities, to facilitate switching between the two tasks, did not modulate age differences. Neither did separating the to-be-remembered material temporally, to allow for the creation of more distinct representations. These findings provide two replications of our initial finding and suggest that age differences in working memory dual tasking are not due to limitations in the speed of encoding. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 |
| 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 | 0.001 |
| 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".