Compensating for anterograde amnesia: A new training method that capitalizes on emerging smartphone technologies
Bibliographic record
Abstract
Following a neuropathological event, individuals left with moderate-to-severe memory impairment are unable to reliably form new memories. The most common challenges involve the capacity to perform a task in the future and to consciously recall a recent event. Disruption of these memory processes leaves the individual trapped in the present, unable to stay on track, and alienated from ongoing events. Memory research has demonstrated that implicit memory is often preserved despite severe explicit memory impairment and that preserved memory systems can provide avenues for acquiring new skills and knowledge. A within-subject single-case A1-B1-A2-B2 experimental design was used to introduce an established theory-driven training program of technology use for individuals with moderate-to-severe memory impairment. We describe its application to enabling RR, an individual with memory impairment postcolloid cyst removal, to independently support her memory using a commercial smartphone. RR showed successful outcome on both objective and qualitative measures of memory functioning. Moreover, she demonstrated consistent and creative generalization of acquired smartphone skills across a broad range of real-life memory-demanding circumstances. Our findings suggest that individuals with moderate-to-severe memory impairment are able to capitalize on emerging commercial technology to support their 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".