Engagement and cognitive load of upper‐year medical trainees during mixed reality–enhanced dissection
Bibliographic record
Abstract
Mixed reality (MR) offers a way to visualize and manipulate complex digital objects in three dimensions, which is particularly beneficial for human anatomy. However, implementing MR effectively requires a deep understanding of its effects on cognitive processes. The purpose of this study was to evaluate cognitive markers of students' engagement and cognitive load while they used MR technology to overlay donor-specific diagnostic imaging onto the corresponding body donors in a fourth-year medical elective course. During two separate dissection sessions, each participant (n = 12) used the imaging on (1) a head-mounted Microsoft HoloLens and (2) an Apple iPad to examine the underlying anatomy of their body donor before beginning dissection. During each activity, participants wore portable five-lead electroencephalographic (EEG) devices to collect cognitive processing data. Separate indexes were calculated from those data to quantify engagement (engagement index; EI) and cognitive load (theta-alpha ratio; TAR), which were compared between HoloLens and iPad usage. Mean EI calculated from EEG data collected while using the HoloLens (0.499 ± 0.038) was significantly higher than the mean EI while using an iPad (0.297 ± 0.037; p = 0.002). Conversely, the mean TAR calculated from EEG data collected while using the HoloLens (1.508 ± 0.047) was significantly lower than that collected while using an iPad (1.813 ± 0.071; p = 0.012). These results indicate that the use of HoloLens to superimpose radiographic images onto a human body donor during dissection is significantly more engaging and requires less cognitive effort than the same task on an iPad.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".