Using Speed and Accuracy and the Simon Effect to Explore the Output Form of Inhibition of Return
Bibliographic record
Abstract
Inhibition of return (IOR) refers to slower responses to targets presented at previously cued locations. Contrasting target discrimination performance over various eye movement conditions has shown the level of activation of the reflexive oculomotor system determines the nature of the effect. Notably, an inhibitory effect of a cue nearer to the input end of the processing continuum is observed when the reflexive oculomotor system is actively suppressed, and an inhibitory effect nearer the output end of the processing continuum is observed when the reflexive oculomotor system is actively engaged. Furthermore, these two forms of IOR interact differently with the Simon effect. Drift diffusion modeling has suggested that two parameters can theoretically account for the speed-accuracy tradeoff rendered by the output-based form of IOR: increased threshold and decreased trial noise. In Experiment 1, we demonstrate that the threshold parameter best accounts for the output-based form of IOR by measuring it with intermixed discrimination and localization targets. Experiment 2 employed the response-signal methodology and showed that the output-based form has no effect on the accrual of information about the target's identity. These results converge with the response bias account for the output form of IOR.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".