Task switching modulates the online control of stimulus-driven saccades
Bibliographic record
Abstract
We have shown that the vector inversion process required for antisaccades engenders a mode of control that diminishes online and feedback-based trajectory corrections (Heath et al, 2010, 2011; Weiler et al, 2011). Here we investigated how switching from this mode of control to a stimulus-driven mode of control (e.g., prosaccade) and vice versa influences the extent to which a saccade trajectory is controlled online. Participants completed pro- and antisaccades in a task switching (e.g. pro- followed by antisaccade or vice versa) and task repetition (e.g., pro- or antisaccade repeated) paradigm to a target located 9.5° left or right of a central fixation (i.e., AABB paradigm). Online control was indexed by evaluating the relationship between the spatial location of the eye at decile increments of normalized movement time relative to the eye's final location (i.e., R2 values). As expected, antisaccades demonstrated larger R2 values than their prosaccade counterparts: a result we interpret to reflect a reduced level of online control. In addition, R2 values for prosaccades, but not antisaccades, demonstrated a switch cost; that is, prosaccades completed after a task switch (i.e., following an antisaccade) exhibited reduced online control compared to their task repetition counterparts. As such, we propose that the intentional nature of the antisaccade task engenders a persistent and slow mode of cognitive control that inhibits online oculomotor corrections.
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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 | 0.001 | 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".