The effect of a perceptual syntax on the learnability of novel concepts
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
Language theorists argue that the reason why spoken language is acquired so rapidly is that we have an innate predisposition for understanding linguistic structures. Theories of perception also hold that there may be deeply seated mechanisms for decomposing visual objects and analyzing them into both component parts and the structural interrelationships of those parts. We propose the theory that diagrams that activate the mechanisms for structural object perception should be similarly easy to learn. This builds on previous work in which we have developed diagramming principles based on the theory of structural object perception. We call these geon diagrams. We have previously shown that such diagrams are easy to remember and to analyze. To evaluate our hypothesis that geon diagrams should also be easy to understand we carried out an empirical study to evaluate the learnability of geon diagram semantics in comparison with the well-established UML convention. The results support our theory of learnability. Both "novices" and "experts" found the geon diagram syntax easier to apply in a diagram-to-textual description matching task than the equivalent UML syntax.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it