The role of video game experience in spatial learning and memory
Bibliographic record
Abstract
Abstract Video game playing has been associated with improvements in cognitive abilities that predict success in STEM fields, and therefore understanding this relationship is important. In two experiments, we used a virtual Morris Water Maze (VMWM) with and without proximal cues to measure spatial learning as a total of 82 video game experts and novices completed a search task across several trials. We measured the participants’ path lengths and tested their mental rotation abilities. The results showed that proximal cues improved overall performance. With no visible cues, experts exhibited better performance than novices when their memory for the general location of the platform was probed. With visible cues, video game experts travelled shorter path lengths than novices to the exact location of the hidden platform. Mental rotation ability correlated with overall maze performance only when no cues were visible, and only novices’ scores correlated with path length in this condition. These studies showed that the VMWM is a useful paradigm in examining how past video game experience influences human spatial cognition.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".