The effect of covert auditory attention in multiple targets aiming: Kinematic evidence
Bibliographic record
Abstract
A central issue in selective attention for action concerns the nature of covert attention. While this issue has been extensively investigated with what we can see, much less is known with respect to auditory stimuli and the possible influences on selective attention for action. The present experiment examined the effect of covert auditory attention in multiple target aiming. Eleven participants initiated goal-directed reaching movements within 320ms to one of four targets, of which the actual target was unknown until 200ms following movement initiation. Auditory cues were presented at movement initiation and attention was manipulated by informing participants of the probability of auditory cues predicting the target side (left or right). The task was completed under three conditions: Neutral-sound, 80/20, and 50/50. Movement trajectories were recorded using Optotrak 3D Investigator at 500Hz. Dependent variables were analyzed using a 3 (condition) x 2(Target location) and trajectories, using a 3 (condition) x 2(Target location) x 6 (MT points) ANOVAs. The present findings indicate that the auditory cue did not have any effect on RT. There was a significant MT advantage to the right compared to left targets regardless of cueing. In addition, a larger proportion of time was spent before PV when aiming to the right compared to left side. Of particular interest, the effect of the valid auditory manifested in the early stages of movement execution as was evident by the reaching trajectories. Therefore, regardless of cue validity the presence of a valid covert auditory attention impacted subsequent goal-directed action.Acknowledgments: Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".