Testing anxiety's effect on movement planning and correction: Online upper-limb corrections are not completely automatic
Bibliographic record
Abstract
Via three experiments, we investigated heightened anxiety's effect on the offline planning and online correction of upper-limb target-directed aiming movements. In Experiment 1, the majority of task trials allowed for the voluntary distribution of offline planning and online correction to achieve task success, while a subset of cursor jump trials necessitated the use of online correction to achieve task success. Experiments 2 and 3 replicated and elaborated Experiment 1 by assessing movement-specific reinvestment propensity and manipulating the self-control resources of participants. This allowed more detailed inference of cognitive resource utilisation to tease apart the effects of conscious processing and distraction-based anxiety mechanisms. For the first time, we demonstrate that: anxiety-induced online-to-offline motor control shifts can be overridden when the need for online correction is necessitated (i.e., in jump trials); anxiety-induced online-to-offline shifts seem to be positively predicted by conscious processing propensity; and optimal spatial efficacy of limb information-based online correction seems to require cognitive resources. We conclude that long-standing definitions of limb information-based online correction require revision, and that both conscious processing and distraction theories appear to play a role in determining the control strategies of anxiety induced upper limb target directed aiming movements.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".