Engagement of Gaze in Capturing Targets for Future Sequential Manual Actions
Bibliographic record
Abstract
We investigated the role of saccadic gaze fixations in encoding target locations for planning a future manual task consisting of a sequence of discrete target-oriented actions. We hypothesized that fixations of the individual targets are necessary for accurate encoding of target locations and that there is a transfer of sequence information from visual encoding to manual recall. Subjects viewed four targets presented at random positions on a screen. After various delays following target extinction, the subjects marked the remembered target locations on the screen with the tip of a hand-held stick. When the targets were presented simultaneously among distracting elements, the overall accuracy of marking increased with presentation time and total number of targets fixated because the subjects had to serially fixate the individual targets to locate them. Without distractors, the marking accuracy was similarly high regardless of duration of target presentation (0.25-8 s) and number of targets fixated; it was comparable to that with distractors when all four targets had been fixated. This indicates parallel encoding of target locations largely based on peripheral vision. Location memory was stable in these tasks over the delay periods investigated (0.5-8 s). With parallel encoding there was a "shrinkage" in the visuomotor transformation, i.e., the distances between the markings were systematically smaller than the corresponding inter-target distances. When the targets were presented sequentially without distractors, marking accuracy improved with the total number of targets fixated and shrinkage in the visuomotor transformation occurred only with parallel encoding, i.e., when subjects did not fixate the targets. In all experimental conditions for trials in which targets were fixated during encoding, there was little correspondence between the marking sequence and the sequence in which the targets were fixated. We conclude that subjects benefit from fixating targets for subsequent target-oriented manual actions when the targets are presented among distractors and when presented sequentially; when distinct targets are presented simultaneously against a blank background, they are efficiently encoded in parallel largely by peripheral vision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".