Learning Novel Words in an Immersive Virtual‐Reality Context: Tracking Lexicalization Through Behavioral and Event‐Related‐Potential Measures
Bibliographic record
Abstract
Abstract The present study used immersive virtual‐reality (iVR) technology to simulate a real‐life environment and examined its impact on novel‐word learning and lexicalization. On Days 1–3, Chinese‐speaking participants learned German words in iVR and traditional picture–word (PW) association contexts. A semantic‐priming task was used to measure word lexicalization on Day 4, and again 6 months later. The behavioral findings of an immediate posttest showed a larger semantic‐priming effect on iVR‐learned words compared to PW‐learned words. Moreover, electrophysiological results of the immediate posttest demonstrated significant semantic‐priming effects only for iVR‐learned words, such that related prime–target pairs elicited enhanced N400 amplitude compared to unrelated prime–target pairs. However, after 6 months, there were no differences between the iVR and PW conditions. The findings support the embodied‐cognition theory and dual‐coding theory and suggest that a virtual real‐life learning context with multimodal enrichment facilitates novel‐word learning and lexicalization but that these effects seem to disappear over time.
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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 | 0.001 |
| 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) | 0.001 | 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".