The Devil in the Details: Local Processing Sometimes Results in Suboptimal Category Learning
Bibliographic record
Abstract
Prototype and exemplar theories of categorization have been compared for decades. However, little research has examined why individuals adopt different approaches to categorization. The aim of the present studies was to investigate individual differences in prototype and exemplar categorization. Study 1 examined how global and local processing, autistic traits, and analytic-holistic cognition relate to category representations using Medin and Schaffer’s (1978) 5–4 categorization task. Participants completed the classification task, followed by a Navon task to assess attentional processing and self-report trait measures. Results showed that reaction times on the Navon task had some associations to categorization strategy, suggesting that global processing abilities may be related to category learning. Study 2 experimentally manipulated attentional focus by priming participants’ attention either globally or locally before completing the 5–4 categorization task. Although priming did not significantly influence strategy use, attention weight analyses revealed nuanced differences in how participants allocated attention to stimulus features. Specifically, participants who focused on the details, attended to less optimal stimulus features when classifying. These findings contribute to our understanding of individual differences in categorization and category learning and highlight the role of attentional focus in shaping category representations
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".