Dissociation between temporal and spatial precueing in the neural dynamics of action preparation
Bibliographic record
Abstract
The time necessary to initiate a goal-directed reaching movement depends on knowing where and when to move. It is well documented that providing advanced information regarding either the spatial location of a target stimulus or its timing of occurrence reduces reaction times (RT). Yet, it remains unclear whether the RT gains attributable to spatial or temporal precueing are subtended by common preparatory dynamics at the neural level. An experiment was designed in which participants (n=21) reached toward appearing visual targets while electroencephalography (EEG) was recorded. In the FullPrecue condition, participants were precued regarding the location (i.e. straight-ahead) and the timing of target onset (TTO) (i.e. 2s post-Precue). In the SpatialPrecue condition, they were precued regarding the location of the target, but its TTO was uncertain (i.e. 1.25, 2 or 2.75s post-Precue). In the TemporalPrecue condition, they were precued regarding the TTO, but its location was uncertain (i.e. left, straight-ahead, right). Results revealed that RTs were significantly faster in the FullPrecue condition (304ms) as compared to the SpatialPrecue (342ms) and TemporalPrecue (334ms) conditions*. Spectral analysis of EEG activity late in the preparatory period showed that spatial precueing was associated with greater synchronization in the theta (3-7 Hz) and alpha (8-13 Hz) frequency bands over midcentral and parietal regions, respectively*. However, temporal precueing was associated with greater desynchronization in the beta-band (20-35 Hz) over contralateral parietal regions*. These results demonstrate that although the RT gains incurred by spatial and temporal precueing are similar, they are subtended by different preparatory dynamics. *p-values
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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 | 0.003 |
| 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) | 0.002 | 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".