Spatial updating of gaze position in younger and older adults – A path integration-like process in eye movements
Bibliographic record
Abstract
Path integration (PI) is a navigation process that allows an organism to update its current location in reference to a starting point. PI can involve updating self-position continuously with respect to the starting point (continuous updating) or creating a map representation of the route which is then used to compute the homing vector (configural updating). One of the brain areas involved in PI, the entorhinal cortex, is modulated similarly by whole-body and eye movements, suggesting that if PI updates self-position, an analogous process may be used to update gaze position, and may undergo age-related changes. Here, we created an eyetracking version of a PI task in which younger and older participants followed routes with their eyes as guided by visual onsets; at the end of each route, participants were cued to return to the starting point or another enroute location. When only memory for the starting location was required for successful task performance, younger and older adults were generally not influenced by the number of locations, indicative of continuous updating. However, when participants could be cued to any enroute location, thereby requiring memory for the entire route, processing times increased, accuracy decreased, and overt revisits to enroute locations increased with the number of locations in a route, indicative of configural updating. Older participants showed evidence for similar updating strategies as younger participants, but they were less accurate and made more overt revisits to mid-route locations. These findings suggest that spatial updating mechanisms are generalizable across effector systems.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".