The Impact of Motion Cues on Memory for Object Location and Appearance
Bibliographic record
Abstract
Two experiments were conducted to investigate the notion that working memory is fractionated into visual and spatial components by demonstrating that a motion discrimination task selectively interferes with the spatial component.Also, because the motion cues used in the motion discrimination task were similar to those used in flight simulators, an attempt was made to further understand which aspects of working memory are required to monitor those motion cues.These experiments examined participants' ability to remember either the location or the appearance of visual stimuli while concurrently discriminating between left/right motion cues produced by a motion seat.Motion cues occurred either during stimulus encoding (E1) or retention (E2) of the visual stimuli.The ability to remember the location of visual stimuli was significantly impaired by motion cues presented during either encoding or retention.In contrast, the motion cues did not interfere with memory for the stimulus' appearance.This study supports the multi-component model of working memory and the notion that encoding/retention of location and appearance information is served by separable mechanisms in working memory.The finding that there is a cognitive cost of processing of visual-spatial information while interpreting motion cues, highlights the importance of including some form of motion cueing in flight simulators to more accurately represent the true mental demands of dynamic flight.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".