Prospective memory in Parkinson disease during a virtual week: Effects of both prospective and retrospective demands.
Bibliographic record
Abstract
OBJECTIVE: This study investigated the effect of Parkinson's disease (PD) on event-based prospective memory tasks with varying demand on (1) the amount of strategic attentional monitoring required for intention retrieval (prospective component), and (2) the retrospective memory processes required to remember the contents of the intention or the entire constellation of prospective memory tasks. METHOD: Twenty-four older adults with PD and 28 healthy older adults performed the computerized Virtual Week task, a multi-intention prospective memory paradigm that simulates everyday prospective memory tasks. The Virtual Week included regular (low retrospective memory demand) and irregular (high retrospective memory demand) prospective memory tasks with cues that were focal (low strategic monitoring demand) or less focal (high strategic monitoring demand) to the ongoing activity. RESULTS: For the regular prospective memory tasks, PD participants were impaired when the prospective memory cues were less focal. For the irregular prospective memory tasks, PD participants were impaired regardless of prospective memory cue type. PD participants also had impaired retrospective memory for irregular tasks, which was associated with worse prospective memory for these tasks during the Virtual Week. CONCLUSIONS: When retrospective memory demands are minimized, prospective memory in PD can be supported by cues that reduce the executive control demands of intention retrieval. However, PD-related deficits in self-initiated encoding or planning processes have strong negative effects on the performance of prospective memory tasks, with increased retrospective memory demand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".