Pupillary responses and memory-guided visual search reveal age-related and Alzheimer’s-related memory decline
Bibliographic record
Abstract
Episodic memory - composed of memory for unique spatiotemporal experiences - is known to decline with aging, and even more severely in Alzheimer 's disease (AD). Memory for trial-unique objects in spatial scenes depends on the integrity of the hippocampus and interconnected structures that are among the first areas affected in AD. We reasoned that memory for objects-in-scenes would be impaired with aging, and that further impairments would be observed in AD. We asked younger adults, healthy older adults, older adults at-risk for developing cognitive impairments, and older adults with probable early AD to find changing items ('targets') within images of natural scenes, measuring repeated-trial changes in search efficiency and pupil diameter. Compared to younger adults, older adults took longer to detect target objects in repeated scenes, they required more fixations and those fixations were more dispersed. Whereas individuals with AD showed some benefit of memory in this task, they had substantially longer detection times, and more numerous, dispersed fixations on repeated scenes compared to age-matched older adults. Correspondingly, pupillary responses to novel and repeated scenes were diminished with aging and further in AD, and the memory-related changes were weaker with aging and absent in AD. Our results suggest that several nonverbal measures from memory-guided visual search tasks can index aging and Alzheimer's disease status, including pupillary dynamics. The task measurements are sensitive to the integrity of brain structures that are associated with Alzheimer's-related neurodegeneration, the task is well tolerated across a range of abilities, and thus, it may prove useful in early diagnostics and longitudinal tracking of memory decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".