Assessing Cognitive Functions Remotely Using a Music-Game-Like Program
Bibliographic record
Abstract
Abstract There is a growing need to develop ways of assessing cognitive functions remotely and providing interventions using a web-based approach. The Ipsilon Test is a music-based cognitive assessment and training tablet application. On each trial, it presents simplified musical notation with colours, spatial cues, and gestural references so that users can translate spatial information into motor actions by tapping on the screen. In this study, we examined the correlation between the performance of a group of healthy older adults on the Ipsilon test and standard measures of cognitive function—a total of 30 participants aged 58 and over were recruited for the study. Before and after one week of training using the web-based program Ipsilon, all participants completed an online version of the Montreal Cognitive Assessment (MoCA) and the visual Stroop task. Of the participants recruited, 22 participants completed the Ipsilon training and cognitive testing. In our sample, performance on the Ipsilon test was generally high, with all participants scoring above the chance level. Importantly, we found a correlation between Ipsilon test performance and pre-Ipsilon MoCA scores. In addition, participants who scored higher on the Ipsilon test also showed improvement in the visual Stroop task after Ipsilon training, particularly in the ability to inhibit irrelevant information. These results suggest the Ipsilon test is a practical web-based cognitive assessment and training tool. Future research will explore its relationship with other cognitive tests and its diagnostic power for differentiating individuals with normal cognition from those with cognitive disorders.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".