Continuous measures of decision-difficulty captured remotely: Mouse-tracking sensitivity extends to tablets and smartphones
Bibliographic record
Abstract
As decisions require actions to have an effect on the world, measures derived from movements such as using a mouse to control a cursor on a screen provide powerful and dynamic indices of decision-making. By adapting classic reach-decision paradigms and deploying them across computers, tablets, and smartphones, we show that portable touch-devices can sensitively capture decision-difficulty. We see this in pre- and during-movement temporal and motoric measures across diverse decision domains. We found touchscreen interactions to more sensitively reflect decision-difficulty during movement compared to computer interactions, and the latter to be more sensitive before movement initiation. Paired with additional evidence for the flexibility and unique utility of pre- and during-movement measures, this substantiates the use of widely available touch-devices to massively extend the reach of decision science. • Mouse-tracking during choice tasks charts decision difficulty differences over time. • Tablets and smartphones show similar sensitivity but resemble real-world interactions. • Computers show better sensitivity pre-movement; touch devices during-movement. • Pre- and during-movement measures offer flexible, non-redundant decision information. • Remote data collection from consumer devices extends the reach of decision science.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".