Trajectory deviations towards, and away from predicted locations based on symbolic cues in reaching tasks
Bibliographic record
Abstract
Rapidly integrating information in our environment for response planning is critical for accurate actions. Predictive cues and attention orienting help us to predict and plan an action relative to what may come next. It is unclear how cues with no spatial information to orient the actor to the upcoming action affect action planning. The purpose of the current study was to determine whether participants subconsciously pre-plan an action following non-spatial, symbolic, predictive cues. High and low predictive cues preceded target appearance. It was hypothesized that as participants subconsciously became aware of the predictability of the cues, that when the target appeared on the non-predicted side, the trajectory of their movements would reflect a pre-planned response associated with that cue; i.e., deviate towards the predictive side before correcting their movement to bring their hand towards the target. No such deviation was expected for the low predictive cue. Results contradicted the hypothesis, demonstrating that participants actually deviated away from the predicted side following the predictive cue. These results indicate that learned, non-spatial symbolic cues may produce inhibition of return type behaviour.
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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 | 0.006 |
| 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 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".