Multisensory interactions and expression of the remote distractor effect
Bibliographic record
Abstract
The location of a distractor influences oculomotor planning times. In particular, a remote – but not proximal – distractor presented concurrent with target onset increases prosaccade reaction time (RT) (i.e., the remote distractor effect: RDE). The competitive integration model asserts that the RDE reflects the time needed to resolve conflict between target- and distractor-related saccade generating commands in the superior colliculus. To our knowledge however, no previous research has examined whether the RDE manifests for multimodal target and distractor combinations. To that end, we included two experiments wherein a visual (N=10) and an acoustic (N=9) target was presented with different modality (i.e., acoustic or visual) remote and proximal distractors. Further, pro- and antisaccades were used to determine if top-down demands in saccade generation influence RT. Results for the visual target/acoustic distractor experiment showed that pro- and antisaccade RTs for proximal and distal distractors were shorter than their no-distractor counterparts; that is, results did not elicit a RDE. In turn, results from the acoustic target/visual distractor experiment showed that prosaccade RTs for distractor and no-distractor conditions did not reliably differ, whereas antisaccade RTs for proximal and distal distractors were respectively shorter and longer than their no-distractor counterparts. These results suggest that the modality of the target and distractor influence expression of the RDE, and that such an effect is limited to a response requiring the top-down decoupling of stimulus-response spatial relations.Acknowledgments: Supported by 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".