Modeling continuous outcome color decisions with the circular diffusion model: Metric and categorical properties.
Bibliographic record
Abstract
The circular diffusion model is extended to provide a theory of the speed and accuracy of continuous outcome color decisions and used to characterize eye-movement decisions about the hues of noisy color patches in an isoluminant, equidiscriminability color space. Heavy-tailed distributions of decision outcomes were found with high levels of chromatic noise, similar to those found in visual working memory studies with high memory loads. Decision times were longer for less accurate decisions, in agreement with the slow error property typically found in difficult 2-choice tasks. Decision times were shorter, and responses were more accurate in parts of the space corresponding to nameable color categories, although the number and locations of the categories varied among participants. We show that these findings can be predicted by a theory of across-trial variability in the quality of the evidence entering the decision process, represented mathematically by the drift rate of the diffusion process. The heavy-tailed distributions of decision outcomes and the slow-error pattern can be predicted by either of 2 models of drift rate. One model is based on encoding failures and the other is based on a nonlinear transformation of the stimulus space. Both models predict highly inaccurate stimulus representations on some trials, leading to heavy-tailed distributions and slow errors. The color-category effects were successfully modeled as stimulus biases in a similarity-choice framework, in which the drift rate is the vector sum of the encoded metric and categorical representations of the stimulus. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".