The tortoise and the hare: Fast and slow learners in an object categorization task
Bibliographic record
Abstract
In a typical object training study, participants are trained to categorize objects (e.g., cars, birds) to a pre-determined level of performance (e.g., 90% accuracy). After training, category retention is tested by asking participants to identify trained and novel exemplars from the learned category. One limitation of this approach is that it fails to consider how the learning characteristics of the individual might affect their subsequent retention of category knowledge. In this study, participants (n = 34) completed an online study where they were trained to identify four species of warblers (Capemay, Townsend, Prairie, Magnolia) or four species of mushrooms (Jacksonii, Flavoconnia, Muscaria, Persicna). Trials consisted of a preview stage where participants pressed the keyboard space bar to view the stimulus (warblers or mushrooms) and a selection stage where participants released the space bar to categorize the stimulus according to its species via a key press response. In the training phase, participants received feedback on their selection and continued until the participant categorized the four species of warblers (or mushrooms) to a 90% accuracy criterion. Based on their total number of trials-to-criterion (TTC), participants were split into “fast” or “slow” learning groups. Following training, participants were asked to identify 40 new images of warblers (or mushrooms) without feedback. Despite having required fewer training trials, participants in the fast group were more accurate than participants in the slow group (fast: 95% versus slow: 88%, p < .02). Interestingly, participants in the slow group showed faster reaction times than participants in the fast group (slow: 1756 ms versus fast: 1885 ms, t = 14.13, p < .001), driven by shorter preview times (slow: 350 ms versus fast: 535 ms, p < .001). Collectively, these results suggest that individual differences in category learning influence the characteristics of category retrieval.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".