Virtual navigation strategies from childhood to senescence: evidence for changes across the life span
Bibliographic record
Abstract
This study sought to investigate navigational strategies across the life span, by testing 8-years old children to 80-years old healthy older adults on the 4 on 8 virtual maze (4/8VM). The 4/8VM was previously developed to assess spontaneous navigational strategies, i.e., hippocampal-dependent spatial strategies (navigation by memorizing relationships between landmarks) versus caudate nucleus-dependent response strategies (memorizing a series of left and right turns from a given starting position). With the 4/8VM, we previously demonstrated greater fMRI activity and gray matter in the hippocampus of spatial learners relative to response learners. A sample of 599 healthy participants was tested in the current study. Results showed that 84.4% of children, 46.3% of young adults, and 39.3% of older adults spontaneously used spatial strategies (p < 0.0001). Our results suggest that while children predominantly use spatial strategies, the proportion of participants using spatial strategies decreases across the life span, in favor of response strategies. Factors promoting response strategies include repetition, reward and stress. Since response strategies can result from successful repetition of a behavioral pattern, we propose that the increase in response strategies is a biological adaptive mechanism that allows for the automatization of behavior such as walking in order to free up hippocampal-dependent resources. However, the down-side of this shift from spatial to response strategies occurs if people stop building novel relationships, which occurs with repetition and routine, and thereby stop stimulating their hippocampus. Reduced fMRI activity and gray matter in the hippocampus were shown to correlate with cognitive deficits in normal aging. Therefore, these results have important implications regarding factors involved in healthy and successful aging.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".