Use of multiple dimensions in learned discriminations
Bibliographic record
Abstract
Many naturally occurring categories vary across multiple stimulus dimensions (e.g.size, color, texture).When humans categorize multidimensional stimuli on the basis of a single dimension this has been taken to indicate use of a rule that could be verbalized.Sorting on the basis of all the stimulus dimensions ('overall similarity' or 'family resemblance') has been taken to indicate a more basic, implicit, automatic, perhaps associative process.However, a review of the literature on animal discrimination learning shows that animals often discriminate on the basis of one dominant dimension.In recent experiments, situations conducive to more complex cognitive processes have increased family resemblance sorting in humans.In an effort to resolve this apparent paradox, experiments were conducted in which humans and pigeons were exposed to multidimensional category discrimination tasks under closely similar conditions.Preliminary results show no evidence that even a non-verbal rule can be said to be involved in pigeons' choices in these conditions, despite the fact that under some conditions a single dimension may dominate their behavior.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".