Spatial updating in amnesia using an eye movement analogue of a path integration task
Bibliographic record
Abstract
Path integration (PI) allows organisms to navigate home by updating their location in reference to the route's starting point. We previously demonstrated a PI-like process in eye movements using an eyetracking version of commonly used PI tasks. As the hippocampus/medial temporal lobes (MTL) have been implicated in updating self-position via whole-body PI, we investigated whether the hippocampus/MTL is necessary for the spatial updating of gaze position. Using our eyetracking PI-analog task, we tested two individuals with hippocampal lesions, DA and BL; BL's hippocampal damage is relatively circumscribed to his dentate gyrus, but he has additional volume loss in the right precuneus and left superior-posterior parietal cortex. Participants followed routes with their eyes guided by visual onsets and, when subsequently cued, returned to the starting point or mid-route location. Surprisingly, despite DA's extensive MTL damage, his accuracy was comparable to that of control participants, but unlike the control participants, he showed increased saccade latency and little to no gaze revisits to enroute locations when returning to the start location. BL's accuracy was reduced relative to that of the control participants. Additionally, in contrast to DA, BL demonstrated an increased reliance on overt, enroute revisits. The behavior of the two amnesic cases, who each differ from the control participants and show distinct patterns from one another, suggests that spatial updating of gaze position reflects interactive processes supported by the hippocampus/MTL and posterior parietal cortex.
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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) | 0.001 | 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 | 0.001 |
| 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".