Examining the influence of ttask and scene alternations and repetitions on eye movements during scene viewing
Bibliographic record
Abstract
The extent to which tasks and stimuli repeat or alternate across trials is known to influence performance, with oculomotor behavior being impacted by task-switching (Mills et al., 2015). Previous examinations of task set and visual behavior, however, have generally required observers to perform different tasks on different scenes, thus neglecting the fact that an observer's task can change even when the visual input remains the same. The present study examined how task switching influences visual behavior when scenes are presented multiple times—does scene repetition moderate the effects of task switching and, if so, is repetition beneficial or detrimental to subsequent processing? Participants viewed scenes while performing either a search (is there a small N or Z embedded in the scene), memorization (post trial forced-choice recognition), or evaluation (rate pleasantness of scene on 7 point scale) task. Task and scenes were presented with varying lags such that a) the same task could be performed on the same image up to 3 times, b) the same task could be performed on different images up to 3 times, or c) an image could repeat up to three times with a different task performed on each trial. Thus, both the task and the scene could either alternate (full alternation) or repeat (full repetition) across lags, or either the task or the scene could alternate while the other was repeated (partial repetition). We found that scene repetition enhanced the effect of task-switching on eye movements, whereas scene alternations diminished it. These results suggest that perceptual processing transferred between tasks and memory for the previous task/scene exposure moderated the impact of task switching on visual behavior. Meeting abstract presented at VSS 2017
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".