Gaming Experience and Spatial Learning in a Virtual Morris Water Maze
Bibliographic record
Abstract
Experience playing video games has been associated with perceptual and cognitive improvements (e.g., Castel, Pratt, & Drummond, 2005; Boot, Kramer, Simons, Fabiani, & Gratton, 2008; Colzato, van den Wildenberg, & Hommel, 2013; Oei & Patterson, 2013) For instance, video gamers show superior spatial abilities than non-gamers (Greenfield, Graig, & Lohr, 1994; Feng, Spence, and Pratt, 2007; Green & Bavelier, 2003). Given that such abilities have been associated with educational and vocational success in STEM fields (Wai, Lubinski, & Benbow, 2009), it is important to understand the relationship between them and video game experience. In past research, virtual versions of the Morris Water Maze (VMWM) have been used to investigate spatial learning in non-human subjects. Yet, the extent of VMWM’s ability to reliably and validly assess human spatial learning is relatively unknown. We developed a VMWM within the Second Life (2015) virtual world and conducted a pilot study with 12 eighth grade students. In the experiment, the participants learned to find the location of a platform in the VMWM. We analyzed performance on the task to identify data trends indicative of spatial learning. Specifically, we compared performance between males and females with varying levels of gaming expertise. In this article, we report on an analysis of navigation strategies as measured by participants’ path lengths and patterns, and we discuss the implications of these results in assessing 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 | 0.002 |
| 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".