Prospective memory rehabilitation based on visual imagery techniques
Bibliographic record
Abstract
Despite the frequency of prospective memory (PM) problems in the traumatic brain injury (TBI) population, there are only a few rehabilitation programmes that have been specifically designed to address this issue, other than those using external compensatory strategies. In the present study, a PM rehabilitation programme based on visual imagery techniques expected to strengthen the cue-action association was developed. Ten moderate to severe chronic TBI patients learned to create a mental image representing the association between a prospective cue and an intended action within progressively more complex and naturalistic PM tasks. We hypothesised that compared to TBI patients (n = 20) who received a short session of education (control condition), TBI patients in the rehabilitation group would exhibit a greater improvement on the event-based than on the time-based condition of a PM ecological task. Results revealed however that this programme was similarly beneficial for both conditions. TBI patients in the rehabilitation group and their relatives also reported less everyday PM failures following the programme, which suggests generalisation. The PM improvement appears to be specific since results on cognitive control tasks remained similar. Therefore, visual imagery techniques appear to improve PM functioning by strengthening the memory trace of the intentions and inducing an automatic recall of the intentions.
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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 | 0.000 |
| 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".