Bibliographic record
Abstract
Inhibition of return (IOR) is the mechanism that is thought to bias attention away from previously attended stimuli and towards novel stimuli. This inhibitory mechanism makes sense in a foraging context, in which it would be beneficial for attention to be directed towards locations that provide new information for the visual system. But what happens when we come across some useful information in the course of our visual foraging? Cueing paradigms designed to investigate IOR effects almost exclusively use cues that are completely uninformative; they do not provide actionable information of any kind. A question then arises: does IOR still occur when visual cues contain meaningful information about the task? It is important to note that by meaningful we do not mean that the cues we are interested in provide any information about the spatial locations of the upcoming targets (such spatial information is known to eliminate IOR). Rather, our question focusses on cues that provide some information critical to the completion of the task; they are both task relevant and spatially uninformative. To examine if meaningful cues affect IOR, a typical IOR cueing paradigm with two cue/target locations was modified to include task-relevant information assigned to cues; target detection was made to be dependent on cue colour (respond to targets following green cues, without responses to targets following red cues). We found that IOR did occur with meaningful cues but the time course was more restricted when compared to traditional non-meaningful cues. Thus, IOR persists but is modified by the presence of meaningful information.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".