Vision and Semantics: Insights into Rock Category Learning Among Geology Undergraduates
Bibliographic record
Abstract
To the untrained eye, rocks offer limited perceptual information to aid in accurate categorizations—making geology an ideal domain to study the development of semantic and visuoperceptual knowledge. This study examined the formation and restructuring of perceived rock-type categories in undergraduates (N=48) enrolled in an introductory-level geology course. Through this work, we addressed three questions: 1) How do categories for rock knowledge develop? 2) How does the acquisition of real-world expertise reshape semantic and visuoperceptual categories? 3) Is the nature of one’s category restructuring indicative of academic performance? We quantified category restructuring trajectories using PsiZ, a machine learning package that generates a multi-dimensional category representation (i.e., psychological embedding) based on the participant’s similarity judgments (Roads & Love, 2020). On each trial, participants were presented with a visual array of nine rocks—depicted by images on visuoperceptual trials and labels (e.g., “basalt”) on semantic trials—and were asked to select the two most similar peripherally presented rocks to the reference rock. Visuoperceptual and semantic category structures were assessed at the start and end of the course. How does category structure relate to academic success? A comparison of the top and bottom 25% of students, based on lab-test performance, revealed diverging trajectories. Post-instruction, image and label judgments of high-performers were highly correlated demonstrating strong integration of visuoperceptual and semantic knowledge. Contrastingly, low-performers displayed faulty visuoperceptual and semantic knowledge as demonstrated by poorly differentiated and conceptually inaccurate clusters of rock images and labels. Strikingly, despite no differences in prior exposure to the field, the two groups showed distinct pre-instruction visuoperceptual structures. High-performers exhibited significantly greater exemplar differentiation; this differentiation (among high-performers) was then maintained, but reconfigured (to assemble conceptually-accurate rock-type clusters) post-instruction. These findings suggest that the groups approached the task, at both timepoints, with markedly different levels of perceptual sensitivity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".