ABBA Letter Alternation: A telehealth inspired measure of executive functioning/inhibitory control
Bibliographic record
Abstract
Objective: To introduce ABBA Letter Alternation (ABBA) as a computerized measure of response inhibition/response alternation developed for telehealth following restrictions of in-person testing due to COVID-19. ABBA consists of two PowerPoint-administered trials: Letter Reading of 25 capital As or Bs individually presented, and Letter Alternation with instructions to say the opposite letter to what is presented. Method: We obtained initial normative ABBA performance from 899 healthy research volunteers participating in the Emory Healthy Brain Study (EHBS) with Montreal Cognitive Assessment (MoCA) scores 24/30 and higher. Cutpoints derived from the EHBS sample were applied to a series of 32 Parkinson disease (PD) patients being evaluated for deep brain stimulation to provide preliminary clinical validation. Trail Making B (TMT B) was also examined in both groups. Results: 775 (86.2%) EHBS participants made 0–1 ABBA Letter Alternation errors, 58 (6.5%) EHBS participants had 2 ABBA alternation errors, and 66 (7.3%) made 3+ errors. Applying these thresholds to PD patients, 22 (68.8%) made 0–1 alternation errors, 3 PD (9.4%) patients made 2 errors, and 7 PD subjects (21.8%) made 3+ errors, which significantly differed in frequency from the EHBS group (χ2=9.8, p=.007). EHBS vs. PD differed on MoCA, a medium effect (p<.00001; η2=.076), and on TMT B (p<.00001; η2=.158), which is considered a large effect. Conclusion: These results provide initial support for ABBA Letter Alternation as a response inhibition/response alternation. Application in larger clinical samples, in both telehealth and face-to-face settings, will be needed to more fully establish ABBA’s clinical utility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".