Competitive processes in free-choice, action-based decisions: Effect of decisional uncertainty in an obstacle avoidance task
Bibliographic record
Abstract
Decisions about actions are thought to arise from a biased competition between simultaneously activated movement plans. In support, reaction times (RT) are longer in situations of increased competition, such as during incongruent trials in Flanker tasks or equiprobable target locations (point of subjective equality; PSE) in hand selection tasks. Still, uncertainty has mostly been studied in contexts in which the target goal is instructed or in which overt execution of both responses is possible, limiting the evidence for a lower-level, action-based competitive process evolving within a single cortical hemisphere. Therefore, we aimed to investigate the effect of decisional uncertainty on RTs in a free choice task where overt execution of both responses was impossible. We developed an obstacle avoidance task in which participants (n = 11), using their dominant hand, had to reach toward a visual target while avoiding an obstacle positioned along the movement path. The target was positioned straight ahead of participants and remained unchanged across trials. Uncertainty was manipulated by placing a 9cm obstacle on participants’ direct path to the target (at the PSE) (maximal uncertainty condition), or an 18cm obstacle shifted laterally by ±4.5cm from the PSE location (minimal uncertainty condition). Importantly, this setup equated movement trajectories across conditions, allowing us to isolate the influence of uncertainty on RTs. Results revealed RTs were significantly slower in the maximal uncertainty condition (375ms ± 32) as compared to the minimal uncertainty condition (355ms ± 28; p=0.0005). This suggests that a competitive process also underlies action-based free-choice decisions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".