The benefit of prior visual experience on estimating angle size
Bibliographic record
Abstract
Our visual system provides us with useful information about the world around us. An important step in identifying an object is establishing it's size, including the size of angles which bound two lines with a common end point (Chen & Kuai, 2018). The current study looked to determine how accurately we perceive angles of different sizes. Participants (N = 30) estimated the size of an angle between two connecting lines when the availability of a reference angle was manipulated. In Task 1, participants were presented with seventeen testing angles ranging between 5 degrees to 85 degrees in five degree increments. Task 2 differed from Task 1 in that participants first viewed a reference angle with known size before estimating the size of the testing angle. The size of the reference angle differed from the testing angle by a maximum of 5 degrees. We found that participants' estimates were fairly accurate across the two tasks, demonstrating minimal errors (average absolute error = 4 degrees). That said, participants were significantly more accurate in their estimates in Task 2 compared to Task 1, suggesting that prior visual experience enhanced response accuracy. Future work will look to determine how the perception of angle size is influenced by reaching with distorted visual feedback (e.g., a cursor that is rotated 30 degrees relative to hand motion).Acknowledgments: Acknowledgements: supported by Natural Sciences and Engineering Research Council of Canada [EKC].
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".