Out of Sight, Out of Mind? The Effect of Visual Field of View Restrictions on Working Memory
Bibliographic record
Abstract
Visual field of view (FoV) restrictions have been found to impair performance on a variety of tasks, including estimating distances, locomotion, target tracking, and the development of spatial representations, as well as in specific domains such as aviation.Explanations for these impairments have largely focused on the restrictions to the physical environment and lack of peripheral information available to the user.The goal of this thesis was to provide a more complete account of these performance impairments by examining whether restrictions to the visual field affected working memory, a cognitive component responsible for the maintenance, integration, and retrieval of visual information.Three experiments were conducted in which the impact of restricted FoV on memory for simple geons was examined.In Experiment 1, restricting FoV was shown to decrease performance on a Geon Memory Task (GMT).Experiment 2 established the relative difficulties of four secondary tasks commonly used in dual-task paradigms (counting, counting/tapping, spatial tapping, and random tapping) to tax individual working memory components and used as secondary working memory tasks in Experiment 3. Experiment 3 examined the relationship between FoV, working memory, and performance on the GMT using a dual-task paradigm.Results from Experiment 3 were consistent with those of Experiment 1 and further supported the hypothesis that FoV negatively affects phonological and visuo-spatial working memory.The central executive was not negatively affected by FoV restrictions.The present findings suggest a more complete account of the impact of FoV restrictions than the lack of peripheral information alone and offer important contributions to the working memory literature.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".