Temporal Evolution of Color Representations Measured with Magnetoencephalography Reveals a “Coarse to Fine” Dynamic
Bibliographic record
Abstract
Color perception is based on the differential spectral responses of the L-, M-, and S-cones and subsequent subcortical and cortical computations and may include the influence of higher-order factors such as language. Although the early subcortical stages of color vision are well characterized, the organization of cortical representations of color remain elusive, despite numerous models based on discrimination thresholds, appearance, and categorization. An underexplored aspect of cortical color representations is how they unfold over time. Here, we compare the dynamic reorganization of three different color representations over time using magnetoencephalography. We measured neural responses to 14 hues at each of three achromatic luminances (increment, isoluminant, and decrement) while participants attended either to the exact color of the stimulus or its color category. We used a series of classification analyses, combined with multidimensional scaling and representational similarity analysis, to ask how cortical representations of color unfold over time from stimulus onset. We compared the performance of "higher order" models based on hue and color category with a model based simply on stimulus cone contrast and found that all models had significant correlations with the data. However, the unique variance accounted for by each model revealed a dynamic change in hue responses over time, which was consistent with a "coarse to fine" transition from a broad clustering into categorical groups to a finer within-category representation. Notably, these dynamics were replicated across data sets from both tasks, suggesting they reflect a robust reorganization of cortical hue responses over time.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".