Relying on procedural memory to enhance independence in daily living activities: Smartphone use in a case of semantic dementia
Bibliographic record
Abstract
Relying on procedural memory is a promising approach for interventions that address the cognitive difficulties found in semantic dementia. The aim of this study was to determine if procedural memory could be used to optimise learning of relevant smartphone functions in MH, a 55-year-old man with semantic dementia. The impact of learning to use specific smartphone applications, which display concepts and their semantic characteristics, on relearning useful significant concepts, was also explored in MH. This patient, who showed no deficits in procedural learning on a serial reaction time paradigm, was able to learn manipulations related to 15 smartphone functions although, because of his deficit in word comprehension, he generally needed verbal cues to clarify which functions he was asked to perform. Six months after the end of the intervention, he was still using 8 of the 15 functions regularly. However, repeated exposure to concepts through the use of two applications did not improve naming or retrieval of semantic attributes. This study showed the potential of relying on procedural memory to optimise learning of new technologies in the ecological rehabilitation of semantic dementia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".