The necessity of motoric engagement in enhancing route memory
Bibliographic record
Abstract
The relative contribution of decision-making and motor engagement at encoding, on route memory, was examined using virtual reality (VR). During encoding, participants explored 12 virtual environments for 40 s each. Navigation strategy during encoding was manipulated within-subjects. On Active trials, participants made decisions about their route of travel. On Guided trials, they followed a pre-determined path overlaid on the road, removing the need for decision-making. On Passive trials, participants simply viewed a set route, without initiating decision-making nor engaging movement during encoding. Following exploration of each environment, participants were asked to 're-trace their steps' using the exact route they had just travelled. We also manipulated type of VR implementation (Desktop VR, Headset VR) between subjects. Movement in a Desktop-VR group was controlled via keyboard input, limiting motoric engagement. Movement in a Headset-VR group occurred using a VR-compatible steering wheel, requiring relatively greater motoric engagement. We found an effect of navigation strategy only in the Headset-VR group: route memory was significantly better following Active and Guided relative to Passive trials. Memory did not differ following Active relative to Guided trial types, suggesting that decision-making does not underlie the memory benefit. We suggest route memory is enhanced when initiating physical movement during encoding.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".