Inhibition of attention to irrelevant areas of a scene: Investigating mechanisms of attention during visual search
Bibliographic record
Abstract
Numerous studies have shown that scene context aids search due to expectations about where objects are located (Castelhano & Heaven, 2011; Neider & Zelinsky, 2006). However, it is unclear what attentional mechanisms are involved in directing gaze to target-relevant scene regions, and whether in addition to enhancing target-relevant regions, target-irrelevant regions are inhibited. In the present study, we investigated whether previously-inhibited regions of a scene would interfere with a subsequent search. Participants performed six searches for six different objects within the same image. The sixth search was always for the same object, but the first five searches could be for objects in the same context region (Same condition) or in a different region (Different condition). In the Control condition, participants did not perform a search and viewed images for 2s each for the first five trials (thus not enhancing nor inhibiting any scene regions). Analyses were performed only on the sixth search, and target-relevant regions (upper, middle, lower) were counterbalanced across all conditions. If attention is inhibited in target-irrelevant regions, then we would expect the Different condition would show longer processing times and less effective fixation placement during initial search guidance. Latency to the first saccade was significantly longer in the Different than in the Control condition. We also found that significantly fewer first fixations were placed in target-relevant regions in the Different condition than in either the Same or Control conditions. Additionally, the frequency with which initial saccades were directed towards the target were much lower in the Different condition than in either the Same or Control conditions. The pattern of results is consistent with the inhibition of target-irrelevant regions; therefore, we conclude that inhibition is a likely additional mechanism by which gaze is directed in scenes and is worthy of further study. Meeting abstract presented at VSS 2014
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".