On the role of eye movements in encoding spatial information
Bibliographic record
Abstract
Mapping the world requires visuospatial working memory (VSWM), which facilities the encoding of objects' features and location. Attention is a mechanism guiding the selection process of behaviourally relevant information which is encoded VSWM. Two mechanisms have been proposed for deploying attention: overt and covert. The overt attentional mechanism is mediated via saccadic eye movements, whereas the covert attentional mechanism could be deployed without saccades. However, a few recent studies have suggested that covert attention might be associated with microsaccades. The main goal of our study was to examine the role of eye movements in encoding of spatial information. Our hypothesis is that encoding visuospatial information will be facilitated by saccadic eye movements. A well-established neuropsychological test, the Corsi Block Task, was employed to investigate the role of eye movements in encoding of spatial information. Sixteen healthy participants were tested (8 females & 8 males; age=22±1.3 years). Participants were instructed to memorize the location and order of targets (number of targets ranged between 2 and 8) presented on a computer screen. Targets were presented in 2 experimental conditions: 1)FIX- participants fixated on a central fixation cross, and 2)MOVE- participants moved their eyes to each target. Visuospatial working memory was assessed by asking participants to move a mouse curser to each remembered target during the recall phase. Eye tracker was used to record eye position during the task. Eye movement analysis has been conducted on 4 participants thus far. Our preliminary results demonstrated no significant difference between the experimental conditions. The overall error rate for target recall was 20% in both FIX and MOVE conditions. Microsaccades were found in 24% of trials during the acquisition phase in the FIX condition, and the majority of these trials (21%) were recalled correctly. Thus, microsaccades might play a role in encoding of spatial location
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".