Spatial working memory impedes search efficiency in interrupted but not continuous scene search
Bibliographic record
Abstract
When searching through a scene, saccades tend to be slower to return to recently fixated locations (saccadic inhibition), whereas during scene memorization saccades tend to be faster to such locations (Dodd et al., 2009). Is the lack of evidence for inhibited saccades in tasks emphasizing scene memory a consequence of a goal-dependent mechanism or is the inhibition simply masked by other processes involved in scene memorization but not search? We provide evidence for the latter. Using an interrupted scene search task (Experiment 1) in which scenes abruptly expanded in size—which is considered to invoke memorial processes (cf. Thomas & Lleras, 2009)—we were able to eliminate saccadic inhibition. Moreover, applying a spatial working memory load prior to search further reduced search efficiency such that saccades were now faster to return to recently fixated locations. To test whether an additional memory process—brought online by scene interruption—obscured saccadic inhibition, Experiment 2 employed a continuous scene search task in which scenes remained continuously present throughout search. In this case, saccadic inhibition was observed, regardless of whether a spatial working memory load was applied or not. Thus, spatial working memory influenced interrupted but not continuous scene search. These data suggest that when confronting visual instability, processes related to working memory are brought online, obscuring saccadic inhibition and thus rendering search less efficient in the sense that rather than the eyes being propelled through the scene, the eyes are instead biased to return to an already fixated location. Meeting abstract presented at VSS 2018
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".