The perception of multi-dimensional regularities
Bibliographic record
Abstract
Regularities are prevalent in many aspects of the environment. How does the visual system extract structured information from multiple sources? One possibility is that the visual system selectively focuses on one source. Alternatively, it may incorporate all sources to form a weighted representation of the regularities. To address this question, we generated matrices containing cells that varied independently on the color dimension (red/blue) or the shape dimension (circle/square). Each matrix could be divided into two equal halves either horizontally or vertically. One half was fully random, whereas the other half was structured (i.e., organized in chunks). Observers discriminated the boundary between the two halves in three conditions (Experiment 1). In the color condition, the cells were structured only on the color dimension; in the shape condition, the cells were structured only on the shape dimension; and in the color+shape condition, the cells were structured both on the color and the shape dimensions. Importantly, each dimension contained an equal amount of regularities. We found that the boundary discrimination accuracy was higher in the color+shape condition than that in the shape condition, but not different from the color condition. This suggests that color was prioritized when regularities were present in both dimensions. To examine whether this prioritization was specific to color, we introduced a new surface dimension (solid/hollow) in the matrices (Experiment 2). Now the boundary discrimination accuracy was the highest in the surface condition, compared to the other dimensions. Critically, the accuracy was equally high when the cells were structured on all three dimensions. This suggests that the surface dimension was prioritized over the others. These findings demonstrate that the visual system relies on one feature dimension to extract regularities, even though every dimension is equally informative. Moreover, such extraction did not benefit from the presence of multiple sources of regularities. Meeting abstract presented at VSS 2015
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".