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Increased Contrast Improves Neuroanatomy Learning for Those with Low Working Memory

2020· article· en· W3016748396 on OpenAlexaff
Beata Cheung, Esai Bishop, Angela Nguyen, Rebecca Leclair, Anthony N. Saraco, Barbara Fenesi, Bruce Wainman

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsQueen's UniversityWestern UniversityMcMaster University
Fundersnot available
KeywordsContrast (vision)NeuroanatomyRecallWorking memoryWhite matterAnalysis of varianceSet (abstract data type)PsychologyCognitionAudiologyMedicineNeuroscienceCognitive psychologyComputer scienceArtificial intelligenceInternal medicineRadiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.210
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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