Age‐related impairment in a complex object discrimination task that engages perirhinal cortex
Bibliographic record
Abstract
Previous lesion studies have shown compromised complex object discrimination in rats, monkeys, and human patients with damage to the perirhinal cortical region (PRC) of the medial temporal lobe. These findings support the notion that the PRC is involved in object discrimination when pairs of objects have a high degree of overlapping features but not when object discrimination can be resolved on the basis of a single feature (e.g., size or color). Recent studies have demonstrated age-related functional changes to the PRC in animals (rats and monkeys) resulting in impaired complex object discrimination and object recognition. To date, no studies have compared younger and older humans using paradigms previously shown to engage the PRC. To investigate the influence of age on complex object discrimination in humans, the present study used an object matching paradigm for blob-like objects that have previously been shown to recruit the PRC. Difficulty was manipulated by varying the number of overlapping features between objects. Functional MRI data was acquired to determine the involvement of the PRC in the two groups during complex object discrimination. Results indicated that while young and older adults performed similarly on the easy version of the task, most older adults were impaired relative to young participants when the number of overlapping features increased. fMRI results suggest that older adults do not engage bilateral anterior PRC to the same extent as young adults. Specifically, complex object matching performance in older adults was predicted by the degree to which they engage left anterior PRC. These results provide evidence for human age-related changes in PRC function that impact complex object discrimination.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".