Ultra-brief Assessment of Working Memory Capacity: Ambulatory Assessment Study Using Smartphones
Bibliographic record
Abstract
BACKGROUND: The development of mobile technology with substantial computing power (ie, smartphones) has enabled the adaptation of performance-based cognitive assessments to remote administration and novel intensive longitudinal study designs (eg, measurement burst designs). Although an "ambulatory" cognitive assessment paradigm may provide new research opportunities, the adaptation of conventional measures to a mobile format conducive to intensive repeated measurement involves balancing measurement precision, administration time, and procedural consistency. OBJECTIVE: Across 3 studies, we adapted "complex span" tests of working memory capacity (WMC) for ultra-brief, smartphone-based administration and examined their reliability, sufficiency, and associations with full-length, laboratory-based computerized administrations. METHODS: In a laboratory-based setting, study 1 examined associations between ultra-brief smartphone adaptations of the operation span, symmetry span, and rotation span tasks and full-length computerized versions. In study 2, we conducted a 4-day ecological momentary assessment (EMA) study (4 assessments per day), where we examined the reliability of ultra-brief, ambulatory administrations of each task. In study 3, we conducted a 7-day EMA study (5 assessments per day) involving the ultra-brief rotation span task, where we examined reliability in the absence of extensive onboarding and training. RESULTS: Measurement models in study 1 suggest that comparable estimates of latent WMC can be recovered from ultra-brief complex span task performance on smartphones. Significant correlations between the ultra-brief tasks and respective full-length versions were observed in study 1 and 2, ranging from r=0.4 to r=0.57. Results of study 2 and study 3 suggest that reliable between-person estimates of operation span, symmetry span, rotation span, and latent WMC can be obtained in 2-3 ultra-brief administrations (equivalent to <1 day of testing in an EMA study design). The results of study 3 replicated our findings, showing that reliable between-person estimates of rotation span may be obtained in as few as 2 ultra-brief administrations in the absence of extensive onboarding and training. In addition, the modification of task parameterization for study 3 improved the estimates of reliability of within-person change. CONCLUSIONS: Ultra-brief administration of complex span tasks on smartphones in a measurement burst design can generate highly reliable cross-sectional estimates of WMC. Considerations for future mobile cognitive assessment designs and parameterizations are discussed.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".