Dual-task interference as a function of varying motor and cognitive demands
Bibliographic record
Abstract
Multitasking is a critical feature of our daily lives. Using a dual-task paradigm, this experiment explored adults' abilities to simultaneously engage in everyday motor and cognitive activities, counting while walking, under conditions varying the difficulty of each of these tasks. Motor difficulty was manipulated by having participants walk forward versus backward, and cognitive difficulty was manipulated by having participants count forward versus backward, employing either a serial 2 s or serial 3 s task. All of these manipulations were performed in single-task conditions (walk only, count only) and dual-task conditions (walk and count simultaneously). Both motor performance variables (cycle time, stride length, walking velocity) and cognitive variables (counting fluency, counting accuracy) were assessed in these conditions. Analyses of single-task conditions revealed that both motor and cognitive manipulations predictably influenced performance. Analyses of dual-task performance revealed influences of motor and cognitive factors on both motor and cognitive performance. Most centrally, dual-task costs (normalized difference between single- and dual-task conditions) for motor variables revealed that such costs occurred primarily for temporal or spatiotemporal gait parameters (cycle time, walking velocity) and were driven by cognitive manipulations. Dual-task cost analyses for cognitive measures revealed negative dual-task costs, or dual-task benefits, for cognitive performance. Finally, the effects of dual-task manipulations were correlated for motor and cognitive measures, indicating dual-task performance as a significant individual difference variable. These findings are discussed with reference to theories of attentional allocation, as well as the possible role of auditory-motor entrainment in dual-task conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".