Eye Movements in the “Morris Maze” Spatial Working Memory Task Reveal Deficits in Strategic Planning
Bibliographic record
Abstract
Analysis of eye movements can provide insights into processes underlying performance of cognitive tasks. We recorded eye movements in healthy participants and people with idiopathic Parkinson disease during a token foraging task based on the spatial working memory component of the widely used Cambridge Neuropsychological Test Automated Battery. Participants selected boxes (using a mouse click) to reveal hidden tokens. Tokens were never hidden under a box where one had been found before, such that memory had to be used to guide box selections. A key measure of performance in the task is between search errors (BSEs) in which a box where a token has been found is selected again. Eye movements were found to be most commonly directed toward the next box to be clicked on, but fixations also occurred at rates higher than expected by chance on boxes farther ahead or back along the search path. Looking ahead and looking back in this way was found to correlate negatively with BSEs and was significantly reduced in patients with Parkinson disease. Refixating boxes where tokens had already been found correlated with BSEs and the severity of Parkinson disease symptoms. It is concluded that eye movements can provide an index of cognitive planning in the task. Refixations on locations where a token has been found may also provide a sensitive indicator of visuospatial memory integrity. Eye movement measures derived from the spatial working memory task may prove useful in the assessment of executive functions as well as neurological and psychiatric diseases in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".