Attentional Engagement During Mobile Application Skill Learning Among Patients With Memory Impairment: A Case Series Exploration
Bibliographic record
Abstract
IMPORTANCE: Attentional engagement is essential for successful cognitive rehabilitation, but little is known about longitudinal interactions with skill learning. OBJECTIVE: To examine how attentional engagement is associated with mobile application skill learning for memory compensation. We hypothesized that patients with greater functional capacity would demonstrate faster learning and attentional engagement drop with skill acquisition, whereas patients with lesser functional capacity would have to maintain attentional engagement to progress throughout training. DESIGN: A case series approach was used with longitudinal skill learning and electroencephalographic (EEG) data recorded across multiple trials and sessions of mobile calendar application training. SETTING: The study was run in a hospital-based neuropsychology clinic. PARTICIPANTS: Seven participants (5 with acquired brain injury, 1 with mild cognitive impairment, and 1 healthy older adult) were recruited. INTERVENTION: Mobile application operation was trained for the purpose of memory compensation. Skill learning was facilitated through a structured rehabilitation protocol, including large amounts of guided practice with the integration of errorless learning. OUTCOMES AND MEASURES: We quantified learning using the proportion of application steps completed independently at each session. We measured attentional engagement using an EEG marker: the Brain Engagement Index. RESULTS: For fast learners, attentional engagement generally decreased as mobile application learning progressed. In contrast, slow learners exhibited stable engagement over time with consistent, yet much slower, progress in skill learning. CONCLUSIONS AND RELEVANCE: The present data indicate that when cognitive impairment is more substantial, skill learning may involve greater attentional engagement. What This Article Adds: Patients undergoing memory rehabilitation may benefit from methods to enhance attentional engagement during skill learning when executive dysfunction is a considerable element of their cognitive profile. Monitoring attentional engagement during cognitive rehabilitation may be useful in identifying and addressing barriers to learning in real time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".