Directional biases for sequential eye movements arising from inhibition of return and neural adaptation
Bibliographic record
Abstract
The orienting phenomenon known as inhibition of return (IOR) refers to a slowing of reaction times (RTs) for the detection of a target in a previously cued location, provided the stimulus onset asynchrony between the cue and target is longer than approximately 300 ms. There is general agreement that at least two forms of IOR exist, one motoric and one sensory/attentional. Previous research examining the spatial distribution of the sensory/attentional form of IOR reveals a monotonic pattern of RTs, where RTs are slowest in a previously cued location and fastest 180°opposite. In E1, this pattern is replicated and extended to a target-target task requiring consecutive eye movements to peripheral stimuli. Surprisingly however, the spatial distribution of a purely motor form of IOR has not been explored previously. In E2, using a similar target-target with central rather than peripheral stimuli, we demonstrate that the spatial distribution of the motor form of IOR can be predicted from basic neurophysiological properties of direction encoding neurons found throughout the motor system. Specifically, based on adaptation effects within directionally selective neurons that are likely to occur in our task, we predicted and observed a non-monotonic pattern of RTs: RTs for consecutive movements offset by 90°were significantly faster than those offset by both 0°and 180°. In order to compare our results with previous IOR studies, in E3 and E4 we replicate our main findings from E1 and E2 using different stimulus configurations. Taken together, our results reveal important differences between the spatial distribution of IOR when sensory/attentional and motor effects are most likely.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".