Quantifying two-dimensional and three-dimensional stereoscopic learning in anatomy using electroencephalography
Bibliographic record
Abstract
Abstract Advances in computer visualization enabling both 2D and 3D representation have generated tools to aid perception of spatial relationships and provide a new forum for instructional design. A key knowledge gap is the lack of understanding of how the brain neurobiologically processes and learns from spatially presented content, and new quantitative variables are required to address this gap. The objective of this study was to apply quantitative neural measures derived from electroencephalography (EEG) to examine stereopsis in anatomy learning by comparing mean amplitude changes in N250 (related to object recognition) and reward positivity (related to responding to feedback) event related to potential components using a reinforcement-based learning paradigm. Health sciences students (n = 61) learned to identify and localize neuroanatomical structures using 2D, 3D, or a combination of models while EEG and behavioral (accuracy) data were recorded. Participants learning using 3D models had a greater object recognition (N250 amplitude) compared to those who learned from 2D models. Based on neurological results, interleaved learning incorporating both 2D and 3D models provided an advantage in learning, retention, and transfer activities represented by decreased reward positivity amplitude. Behavioral data did not have the same sensitivity as neural data for distinguishing differences in learning with and without stereopsis in these learning activities. Measuring neural activity reveals new insights in applied settings for educators to consider when incorporating stereoscopic models in the design of learning interventions.
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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 | 0.000 | 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".