Temporal order judgments are disrupted more by reflexive than by voluntary saccades
Bibliographic record
Abstract
We do not always perceive the sequence of events as they actually unfold. For example, when two events occur before a rapid eye movement (saccade), the interval between them is often perceived as shorter than it really is and the order of those events can be sometimes reversed (Morrone MC, Ross J, Burr DC. Nat Neurosci 8: 950-954, 2005). In the present article we show that these misperceptions of the temporal order of events critically depend on whether the saccade is reflexive or voluntary. In the first experiment, participants judged the temporal order of two visual stimuli that were presented one after the other just before a reflexive or voluntary saccadic eye movement. In the reflexive saccade condition, participants moved their eyes to a target that suddenly appeared. In the voluntary saccade condition, participants moved their eyes to a target that was present already. Similarly to the above-cited study, we found that the temporal order of events was often misjudged just before a reflexive saccade to a suddenly appearing target. However, when people made a voluntary saccade to a target that was already present, there was a significant reduction in the probability of misjudging the temporal order of the same events. In the second experiment, the reduction was seen in a memory-delay task. It is likely that the nature of the motor command and its origin determine how time is perceived during the moments preceding the motor act.
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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 | 0.021 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".