The inter-trial spatial biases of stimuli and goals in saccadic programming
Bibliographic record
Abstract
Prior studies have shown an ‘alternate antisaccade-goal bias’, in that the saccadic landing points of antisaccades were displaced towards the location of antisaccade goals used in other trials in the same experimental block. Thus the motor response in one trial induced a spatial bias of a motor response in another trial. In this study we investigated whether sensory information, i.e. the location of a visual stimulus, might have a spatial effect on a motor response too. Such an effect might be attractive as for the alternate antisaccade-goal bias or repulsive. For this purpose we used block of trials with either antisaccades, prosaccades or mixed trials in order to study the alternate-trial biases generated by antisaccade goals, antisaccade stimuli, and prosaccade goals. in contrast to the effects of alternate antisaccade goals described in prior studies, alternate antisaccade stimuli generated a significant repulsive bias of about 1.8°: furthermore, if stimulus and motor goal coincide, as with an alternate prosaccade, the repulsive effect of a stimulus prevails, causing a bias of about 0.9°. Taken together with prior results, these findings may reflect averaging of current and alternate trial activity in a salience map, with excitatory activity from the motor response and inhibitory activity from the sensory input..
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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 | 0.010 |
| 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".