The effects of concurrent verbal and visual tasks on category learning.
Bibliographic record
Abstract
Current theories of category learning posit separate verbal and nonverbal learning systems. Past research suggests that the verbal system relies on verbal working memory and executive functioning and learns rule-defined categories; the nonverbal system does not rely on verbal working memory and learns non-rule-defined categories (E. M. Waldron & F. G. Ashby, 2001; D. Zeithamova & W. T. Maddox, 2006). However, relatively little research has explored the importance of visual working memory or visual processing for either system. The authors investigated the role of working memory (Experiment 1a and 1b), visual processing (Experiment 2), and executive functioning for each system, using a concurrent task methodology. It was found that visual tasks with high executive functioning demands and verbal tasks with high or low executive demands disrupted rule-defined learning, whereas any visual task, regardless of executive functioning demand, disrupted non-rule-defined learning. Taken together, these results confirm the importance of verbal working memory and executive functioning for the verbal system and provide new evidence for the importance of visual processing for the nonverbal system. These results help to clarify understanding of the nonverbal system and have implications for multiple systems theories of category learning (F. G. Ashby, L. A. Alfonso-Reese, A. U. Turken, & E. M. Waldron, 1998).
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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 | 0.001 | 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".