Evidence for a virtual human analog of a rodent relational memory task: A study of aging and fMRI in young adults
Bibliographic record
Abstract
A radial maze concurrent spatial discrimination learning paradigm consisting of two stages was previously designed to assess the flexibility property of relational memory in mice, as a model of human declarative memory. Aged mice and young adult mice with damage to the hippocampus, learned accurately Stage 1 of the task which required them to learn a constant reward location in a specific set of arms (i.e., learning phase). In contrast, they were impaired relative to healthy young adult mice in a second stage when faced with rearrangements of the same arms (i.e., flexibility probes). This mnemonic inflexibility in Stage 2 is thought to derive from insufficient relational processing by the hippocampus during initial learning (Stage 1) which favors stimulus-response learning, a form of procedural learning. This was proposed as a model of the selective declarative and relational memory decline classically described in elderly people. As a first step to examine the validity of this model, we adapted this protocol to humans using a virtual radial-maze. (1) We showed that performance in the flexibility probes in young and older adults positively correlated with performance in a wayfinding task, suggesting that our paradigm assesses relational memory. (2) We demonstrated that older healthy participants displayed a deficit in the performance of the flexibility probes (Stage 2), similar to the one previously seen in aged mice. This was associated with a decline in the wayfinding task. (3) Our fMRI data in young adults confirmed that hippocampal activation during early discrimination learning in Stage 1 correlated with memory flexibility in Stage 2, whereas caudate nucleus activation in Stage 1 negatively correlated with subsequent flexibility. By enabling relational memory assessment in mice and humans, our radial-maze paradigm provides a valuable tool for translational research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".