The Interplay of Stop Signal Inhibition and Inhibition of Return
Bibliographic record
Abstract
Inhibition of return (IOR) refers to slower responding to a stimulus that appears in the same rather than a different location as that of a preceding stimulus. The goal of the present study was to examine the relationship between IOR and stop signal inhibition. Participants were presented with two stimuli (S1 and S2) on each trial. On half of the trials (go trials), participants were required to make a speeded button-press response to report the location of S1; on the other half of trials (stop trials), they were required to cancel the response to S1, as indicated by the appearance of a stop signal at a variable delay (stop signal delay, SSD) after the appearance of S1. Success in cancelling an S1 response varied directly as a function of the SSD: The longer the delay, the more difficult it was for participants to cancel the prepared response. We examined the magnitude of IOR in the S2 reaction times as a function of whether participants made a correct go response to S1, made an erroneous non-cancelled response to S1, or successfully cancelled a response to S1. Our results indicated that the presentation of a stop signal increased the magnitude of IOR, even when the S1 response was not successfully cancelled. However, this was true only when the to-be-cancelled response involved the same effectors as the response used to reveal IOR. These results suggest that there may be a motor component to IOR that is sensitive to the same inhibitory processes that are used to cancel responses in a stop signal paradigm.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".