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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".