RF shape channels: The processing of compound Radial Frequency patterns.
Bibliographic record
Abstract
Radial Frequency (RF) patterns are quasi-circular contours that are frequently used to investigate intermediate stages of shape processing. Combinations of RF patterns have been used to construct more complex shapes such as head contours. Previous studies have suggested that complex shapes may be encoded by multiple, narrowly-tuned RF shape channels. The aim of this study was to test the hypothesis that complex shape processing may be based on multiple, independent RF channels and to demonstrate the limitations such shape descriptors. Thresholds were determined for detection (circle vs. RF compound) and discrimination (RF compound vs. RF compound) of various weighted combinations (symmetrical and asymmetrical) of two RF components (RF3&RF5 RF3&RF8 RF4&RF7). If both RF components were processed by a common broadband channel, one would expect a substantial increase in sensitivity as the information from both components would be summed within the same channel (additive summation: AS). If the two components were processed independently by separate channels, one would expect only a slight increase in sensitivity for the compound compared to the components (probability summation: PS). The data were analyzed by a model for probability (PS) and additive summation (AS) under Signal Detection Theory (Kingdom, Baldwin & Schmidtmann, Journal of Vision, 15(5):1). Results show that summation of information from different RF components is consistent with AS. This suggest that the shapes tested here are processed by a broadly tuned mechanism. In addition, we demonstrate the mathematical limitations of RF patterns which make them an unlikely candidate for universal shape descriptors. Meeting abstract presented at VSS 2017
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".