Use of multiple dimensions in learned discriminations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 it