Employing Eye Tracking Technology to Understand How Novices Learn Neuroanatomy
Bibliographic record
Abstract
Introduction Neurophobia is the fear of neuroscience experienced by students who are learning and applying content. Although the cause remains unclear, neurophobia presents a major barrier to student success in neuroanatomy. A preliminary study suggested that individuals with low working memory capacity (WMC) benefit from using high‐contrast anatomical images when learning neuroanatomy. However, the reason for the improvement is unknown. Aim This study uses eye tracking technology to better understand how individuals of varying WMC and expertise study neuroanatomical images. We aim to: 1) assess differences in gaze patterns between students of high and low WMC when viewing high and low contrast brain slices and 2) compare novices and experts to uncover potential competency‐related differences. Methods Undergraduate students with no prior anatomical education (n=120) and neuroanatomical experts were recruited. During an eye tracking session, participants were given 5 minutes to study twelve structures on digital images of 4 brain slices (either coronal or transverse slices in high or low contrast) and were then tested on their ability to identify the learned structures on similar low‐contrast images. This procedure was repeated such that all participants were exposed to a set of coronal/transverse and high/low contrast images in a randomized fashion. All participants’ eye tracking data and test accuracy were recorded. After testing, participants completed the Automated Operation Span Task (OSPAN) to quantify WMC. Results Preliminary results show that students with high WMC are more accurate in identifying neuroanatomical structures when compared to low WMC students [ F (1, 32), p =.088]. Dwell time was a significant predicting factor for accuracy [ r (32)=40, p =.02]. High WMC students fixated longer on neuroanatomical structures when compared to low WMC students [ F (1, 32)=6.50, p =.02]. Data collection from expert participants is ongoing. Discussion/Conclusion Overall, these results offer a quantitative measure of how neuroanatomical information is viewed and learned to gain insight into neurophobia and influence future teaching practices in neuroanatomy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".