The impact of walking and visual distraction on lexicality judgements
Bibliographic record
Abstract
Abstract Walking has been the focus of much of the existing work on multitasking given its complexity as a cognitive process and importance in daily life. This complexity is evidenced by gait variation observed in dual-task contexts. However, an open question concerns how walking effects concurrently performed cognitive tasks. Thus, we use virtual reality to investigate how walking and visual distraction modulate language processing in an ecologically valid, yet controlled manner. In this novel experimental paradigm, we gradually increase the cognitive burden on participants’ lexical decision responses by adding visual distractors and concurrent walking demands. Accordingly, healthy, young participants performed a lexical decision task as either (1) a single-task+, while seated and with randomly appearing visual distractors, or as (2) a multitask, while walking on a self-paced treadmill through a VR city scape including visual distractors. Participants generally made faster lexical decisions while walking. However, in the single-task+ condition, participants made more errors when a distractor was present. These effects were somewhat modulated by individual differences in visual processing. Crucially, no clear dual-task cost was observed; rather, behavior adapted to increased demands within a specific domain. Overall, these findings suggest an interplay of both task-related and individual characteristics determining multitask performance.
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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 | 0.000 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".