Use of Spatial Dimensions in Pattern Discrimination and Similarity Judgments by Pigeons
Bibliographic record
Abstract
Two experiments examined the role of spatial dimensions in pattern discrimination and judgment of similarity by pigeons. In Experiment 1, pigeons were given a symbolic matching-to-sample task in which they first learned to discriminate between two patterns (A and B) that differed in the spatial layout of an arrow inside a circle divided into four quadrants. The first training stimulus contained an arrow inside the Top Left quadrant and the tip of the arrow was pointing toward 90 degrees. The second training stimulus contained an arrow inside the Bottom Left quadrant and the arrow was pointed downwards at 180 degrees. Fourteen new patterns, consisting of all the remaining combinations of arrow orientations and arrow locations (quadrants), were then presented and their categorization by the pigeons was examined. The results showed that the two dimensions pertaining to the position of the arrow (Top/Bottom and Left/Right halves of the circle) and their interactions were more salient than the two dimensions pertaining to its orientation (Horizontal / Vertical arrows and two arrow ends). Experiment 2 showed that the position of a pattern component was encoded and used in similarity judgments even when the A and B differed along nonspatial dimensions (a rectangle vs a circle). When pigeons encountered new visual patterns and judged their similarity to old ones, they privileged the position of the pattern components over shape in their judgments.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".