On the role of eye movements and saccade preparation in generating auditory inhibition of return.
Bibliographic record
Abstract
In three experiments, listeners were required to either localize or identify the second of two successive sounds. The first sound (the cue) and the second sound (the target) could originate from either the same or different locations, and the interval between the onsets of the two sounds (Stimulus Onset Asynchrony, SOA) was varied. Sounds were presented out of visual range at 135 azimuth left or right. In Experiment 1, localization responses were made more quickly at 100 ms SOA when the target sounded from the same location as the cue (i.e., a facilitative effect), and at 700 ms SOA when the target and cue sounded from different locations (i.e., an inhibitory effect). In Experiments 2 and 3, listeners were required to monitor visual information presented directly in front of them at the same time as the auditory cue and target were presented behind them. These two experiments differed in that in order to perform the visual task accurately in Experiment 3, eye movements to visual stimuli were required. In both experiments, a transition from facilitation at a brief SOA to inhibition at a longer SOA was observed for the auditory task. Taken together these results suggest that location-based auditory IOR is not dependent on either eye movements or saccade programming to sound locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".