Digital questionnaire response time (DQRT): A ubiquitous and low-cost digital assay of cognitive processing speed
Bibliographic record
Abstract
Digital survey tools have all but replaced paper and pen in the psychological sciences, and consequently new forms of potentially useful research paradata are now routinely gathered. A particularly common byproduct of research is questionnaire timestamps, which some have suggested can be used as a measure of cognitive function. Here, we conducted a comprehensive validation of this measure, which we call the "digital questionnaire response time," or "DQRT." Using data from N = 2,977 users of a smartphone app, we first ran a data-driven bootstrapping approach to examine how best to quantify DQRT. DQRT was slower in older adults (r = 0.26) and in those with lower educational attainment and socioeconomic status. Testing the association between DQRT and working memory (range r = 0.11-0.14), model-based planning (range r = 0.03-0.06), and processing speed (range r = 0.29-0.39) across cross-sectional and longitudinal subsamples, we found support for a cognitive characterization of DQRT as a measure of cognitive processing speed. DQRT was more strongly correlated with nine out of 13 lifestyle and health factors, and four out of nine mental health factors than a task-based measure of processing speed. DQRT showed good test-retest reliability, and associations between DQRT and task-based processing speed were higher within individuals (r = 0.35) than between individuals (r = 0.25). Finally, we highlight substantial, but addressable, potential confounds inherent in the measure. We conclude that DQRT has important limitations, but overall can serve as a valid and reliable index of cognitive processing speed that can be gathered at unprecedented scale, unobtrusively, and repeatedly, during a variety of real-world digital behaviors.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".