Bridging the WEIRD Gap in Category Learning: Exploring an Online-Based Solution
Bibliographic record
Abstract
This study evaluated the feasibility of global, cross-cultural classification research by examining category learning in online and in-person settings. Using the six Shepard, Hovland, and Jenkins (SHJ; 1961) category Types (i.e., Type I-VI) paired with Choi et al.’s Analysis-Holism Scale (AHS; 2007) and a demographics questionnaire, we aimed to underscore the potential of online research to address data limitations in generalizability regarding cognitive universals. The main objectives were to examine category learning across modalities, evaluate data reliability, and compare these results against the literature, and explore analytic vs. holistic thinking vis-à-vis cultural variations. The primary research question centred on the viability of investigating category learning strategies online. The results revealed a significant main effect of modality on SHJ performance, indicating better performance in-person. There was also a significant main effect of SHJ Type on performance, with Type I being the easiest and Type VI the most challenging, in line with the existing literature. No interaction was observed between modality and SHJ Type. Exploratory findings showcased similar rank-order differences in SHJ Type for both modalities and confirmed a positive relationship between holistic thinking and Type IV performance. This study paves the way for future online-based category learning paradigms and highlights the need for larger, more diverse samples when studying cognitive universality.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".