Landmark-dependent Navigation Strategy Declines across the Human Life-Span: Evidence from Over 37,000 Participants
Bibliographic record
Abstract
Humans show a remarkable capacity to navigate various environments using different navigation strategies, and we know that strategy changes across the life span. However, this observation has been based on studies of small sample sizes. To this end, we used a mobile app-based video game (Sea Hero Quest) to test virtual navigation strategies and memory performance within a distinct radial arm maze level in over 37,000 participants. Players were presented with six pathways (three open and three closed) and were required to navigate to the three open pathways to collect a target. Next, all six pathways were made available and the player was required to visit the pathways that were previously unavailable. Both reference memory and working memory errors were calculated. Crucially, at the end of the level, the player was asked a multiple-choice question about how they found the targets (i.e., a counting-dependent strategy vs. a landmark-dependent strategy). As predicted from previous laboratory studies, we found the use of landmarks declined linearly with age. Those using landmark-based strategies also performed better on reference memory than those using a counting-based strategy. These results extend previous observations in the laboratory showing a decreased use of landmark-dependent strategies with age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".