Continuous Measures of Decision-Difficulty Captured Remotely: I. Mouse-tracking sensitivity extends to tablets and smartphones
Bibliographic record
Abstract
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. In this first of a set of two studies, we replicated classic reach-decision paradigms 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. We build upon this in the second study in this series (Bertrand et al., 2023) with the use of webcam eye-tracking to further elucidate, earlier in time, the decision process. This subsequent work provides additional support for tools that enable remote collection of rich decision data in ecologically-valid environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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) | 0.002 | 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".