The spatial topography of a motoric form of inhibition of return
Bibliographic record
Abstract
Inhibition of return (IOR) refers to a slowing of reaction times for the detection or localization of a target object presented in the same location as a previously attended cue, provided the cue-target stimulus onset asynchrony is at least 300ms. Currently, at least two forms of IOR are thought to exist: one that affects early (sensory) processes, and the other that affects late (motoric) processes. When sensory processes are inhibited, the topography of IOR is greatest at the cued location (i.e. a 0°cue-target offset) and decreases monotonically to 180°opposite. However, to date, the topography of purely motoric IOR has not been compared to IOR involving sensory inhibition. This is surprising, because one would expect sensory and motoric inhibition to occur within different neural substrates. In order to compare sensory and motoric IOR, in two separate experiments, participants made consecutive eye movements in response to either (1) peripheral onsets, when sensory inhibition is likely or (2) arrows presented at the point of fixation, when only motoric inhibition is likely. Four possible target locations were presented in each trial (marked by placeholders). When peripheral onsets were used to signal consecutive eye movements, we observed a typical pattern of IOR, where RTs were slowest at the cued location (0°), intermediate for 90°, and fastest 180° opposite. In contrast, when central arrows were used to signal consecutive eye movements, RTs were faster only when offset by 90° degrees from the initial movement. The present results reveal a difference in the topography of purely motoric IOR compared to IOR involving sensory inhibitory processes.
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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 | 0.001 |
| 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) | 0.002 | 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".