Increased Contrast Improves Neuroanatomy Learning for Those with Low Working Memory
Bibliographic record
Abstract
Introduction Anatomy education is burdened by neurophobia, a student’s fear when learning neuroanatomy and clinical neurology. While neurophobia’s cause remains unknown, poor definition of structures in standard neurological specimens may affect students’ learning due to influences on cognitive load. In this study, we examined the effects of increased visual contrast between grey and white matter ‐ done via novel staining method ‐ with the goal of providing insight into how educators can adapt anatomical specimens to aid students during learning and application. Aim To determine if increasing contrast between grey and white matter on human brain slices aids students in learning neuroanatomy, and if the difference in contrast improves students’ performance during testing. Methods Undergraduate students at McMaster University with no prior neuroanatomical education (n = 102) were recruited for a 3‐day protocol. On Day 0, participants learned 12 neuroanatomical structures from a set of brain slices (transverse or coronal section, with low or high contrast). They were then asked to locate and recall these structures on unstained slices of the same section. The learn‐test phase was immediately repeated with a second set of slices counterbalancing section and contrast. Participants returned for a learning‐only session 24 hours later (Day 1) using the same specimens from Day 0, and were tested on unstained sets 48 hours after (Day 2). Participants then completed an Automated Operation Span Task (OSPAN) to assess working memory capacity (WMC), and a learning methods survey. Results Repeated Measures ANOVA tests were performed using Time (Day 0 v. Day 2) and Staining (stained v. unstained) as within‐subject variables. Though Time had an effect, with Day 0 performance being significantly better than that of Day 2 ( F (1, 101) = 26.93, p < .001, ηp 2 = 0.21), there was no effect of Staining. Participants performed relatively equally ( F (1, 101) = 0.234, p = .629, ηp 2 = .002). Participant data were sorted into quartiles based on WMC (OSPAN score). Independent sample t‐tests of the sorted data showed that participants with low WMC performed significantly worse than those with high WMC when learning from low‐contrast specimens, regardless of time ( t (47) = −2.164, p = .036, d = (5.58−4.04) ⁄ 2.49 = .618). However, performance between these groups was equalized after learning from high‐contrast slices ( t (47) = −0.53, p = .596, d = (5.38−4.92) ⁄ 2.98 = .154). Discussion/Conclusion Results show that increased contrast had little effect within individual results; however, it improved overall performance of low WMC participants, helping to match results of high WMC participants. This suggests that the use of high‐contrast brain slices may prove beneficial when teaching students with low WMC that struggle with neuroanatomy, potentially by reducing the cognitive load needed to locate structures so students can re‐allocate cognitive capacity towards learning. Further insight may be gained using eye‐tracking technology to observe student gaze patterns as they view the material to determine how structures are being viewed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".