Canonical Field Approximation: A Method for Mapping Perceptually Privileged Viewpoints around Objects
Bibliographic record
Abstract
Canonicalness is a psychological variable that represents viewing preference around a three-dimensional object. Object perspectives with higher canonicalness are perceptually privileged when compared to perspectives with lower canonicalness. Various methods have attempted to quantify canonicalness around objects, employing Likert ratings, best/worst view selection, and Thurstonian Case V scaling. Canonical field approximation (CFA) is an updated, robust method for measuring the canonicalness of a set of discrete perspectives around an object. Using CFA measurements, two-dimensional interpolation facilitates generation of an approximate canonical field around an object. We deployed the CFA methodology on two sets of participants (exploratory and replication) to 1. Validate the CFA method, 2. Attempt replication of observations from past canonicalness research, and 3. Generate and test behavioral hypotheses. Results suggest CFA is a valid method for measuring canonicalness, with high Spearman correlation between canonicalness scores and ordinal perspective ranks and high agreement between participants (within and between samples). Observations from past canonicalness rating studies, relating to observer agreement and rating variance, were replicated. Alongside validation of CFA, two behavioral predictions were generated and tested. Decision time for choosing the more canonical perspective in the 2-alternative forced choice task that underlies CFA is negatively correlated with the absolute difference between the canonicalness ratings of the two perspectives. This is true for both a participant’s individual canonicalness ratings and the mean sample canonicalness ratings for predicting decision time, with almost equivalent model parameter estimates. We also predicted and observed an affordance effect on canonicalness, where right- and left-handed participants preferred opposite, mirror-image perspectives of graspable objects with one handle, with the handle oriented towards their dominant hand. This effect was overlooked in past research that employed lower resolution canonicalness rating methods. Ultimately, CFA is a robust canonicalness rating method that provides insights into object perception processes and behavior arising therefrom.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".