The influence of state anxiety on the 'offline' planning and 'online' control of action: Is it as simple as "one or the other"?
Bibliographic record
Abstract
Attentional Control Theory (ACT) suggests the negative performance impact of anxiety results from an initial reallocation of attentional resources, which decreases goal-directed control and increases stimulus-driven behaviour. Evidence from goal-directed aiming indicates that the negative effects of anxiety unfold near the end of movement during online control (Lawrence et al., 2013). We aimed to explore whether the anxiety effect in online control was also related to changes in offline planning. Participants aimed to a target under both low and high anxiety conditions. Following initial practice, participants were instructed to aim as fast-and-accurate as possible (low) or additionally received non-contingent feedback that previous responses were in the lower thirtieth percentile of the cohort (high). A manipulation-check (cognitive sub-scale of the Mental Readiness Form-3) indicated a significant increase in anxiety following the high condition. The performance outcomes (movement time, constant error) and movement kinematics (peak acceleration, peak velocity, peak deceleration, movement end) were assessed. There were no significant differences in any of the outcome-related measures. There was a shorter time to peak deceleration and longer displacement at peak velocity for the high compared to the low condition. To assess the error-reduction online control processes, we analysed the mean within-participant correlations between the displacement to and after specific kinematic landmarks. This analysis revealed a lower negative relation (less error-reducing) at peak deceleration for the high compared to the low condition. These findings indicate that high state anxiety initially alters the offline planning of action, which may result from an overload in attentional resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".