The influence of spatio-temporal structure on sequential eye and arm movements to remembered visual targets
Bibliographic record
Abstract
Introduction: People are better at performing sequential movements to remembered targets that possess spatial structure (Fagot and De Lillo, 2011). This structure can be acquired at once (E.g., if shown 4 dots that form a square simultaneously) or over time (if the 4 dots are shown one at a time). This study investigates how the temporal presentation of spatial structure affects the ability to perform sequential saccades, reaches, and coordinated saccades & reaches. Methods: 8 head-fixed subjects in a dark room were positioned in front of a 5X5 LED display that encompassed 20° of visual space horizontally and vertically. While maintaining fixation on the central LED, 3-6 peripheral LEDs were illuminated sequentially in one of three ways: 1) The LEDs formed a connected structure and were presented temporally in a "connect the dots" temporal order (spatio-temporal structure congruent), 2) The LEDs had the same spatial structure, but were presented temporally randomly (spatio-temporal structure incongruent), or 3) LED locations were random (unstructured). LEDs then extinguished and subjects performed sequential movements to the remembered locations of the targets in the order they were presented. Results: To date, the saccade data has been collected with the following preliminary analysis. For the spatio-temporal structure incongruent and unstructured conditions, there were more saccades to incorrect target locations and trials with at least one saccade error when only 3 saccades had to be performed versus 6. This was not seen in the spatio-temporal structure congruent condition. In the 6 saccade condition, subjects were less likely to make a saccade error on the 4th or 5th saccades in the spatio-temporal structure congruent condition as compared to the other 2 conditions. Conclusion: Presenting targets that are spatio-temporally congruent reduces saccade errors. We are currently collecting reaching data on this paradigm to investigate if these results are effector specific. 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".