Intrinsic orientation and learning viewpoint in shape recognition
Bibliographic record
Abstract
Two experiments examined the independent roles of the intrinsic reference direction (intrinsic orientation) of a shape and the learning viewpoint of the observer in recognizing the shape. Participants viewed a hexagon for one minute and then they made same-different judgments on two quadrilaterals derived from the hexagon or from the mirror version of the hexagon at the same view or at a novel view. The two quadrilaterals were derived by splitting the hexagon along an intrinsic axis, which connected two opposite vertices of the hexagon. In Experiment 1, the hexagon was presented with one intrinsic axis parallel to the learning viewpoint and another intrinsic axis parallel to the orientation of an external rectangle misaligned with the learning viewpoint. The results showed that participants were quicker in recognizing the two quadrilaterals split along the intrinsic axis parallel to the orientation of the rectangle than those split along the intrinsic axis parallel to the learning viewpoint and were quicker at the same view than at the novel view. The intrinsic axis effect and the view change effect were independent. In Experiment 2, the external rectangle was removed. The results showed that participants were quicker in recognizing the testing quadrilaterals split along the intrinsic axis parallel to the learning viewpoint than those split along the intrinsic axis not parallel to the learning viewpoint and were quicker at the same view than at the novel view. The intrinsic axis effect and the view change effect were independent. These results suggest that people establish an intrinsic reference direction to represent the geometric structure of a shape using an available cue of an external frame or their egocentric viewpoint and also represent their egocentric learning viewpoint with respect to the same intrinsic reference direction.
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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 | 0.011 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".