What has driven the evolution of multiple cone classes in visual systems: object contrast enhancement or light flicker elimination?
Bibliographic record
Abstract
BACKGROUND: Two competing theories have been advanced to explain the evolution of multiple cone classes in vertebrate eyes. These two theories have important, but different, implications for our understanding of the design and tuning of vertebrate visual systems. The 'contrast theory' proposes that multiple cone classes evolved in shallow-water fish to maximize the visual contrast of objects against diverse backgrounds. The competing 'flicker theory' states that multiple cone classes evolved to eliminate the light flicker inherent in shallow-water environments through antagonistic neural interactions, thereby enhancing object detection. However, the selective pressures that have driven the evolution of multiple cone classes remain largely obscure. RESULTS: We show that two critical assumptions of the flicker theory are violated. We found that the amplitude and temporal frequency of flicker vary over the visible spectrum, precluding its cancellation by simple antagonistic interactions between the output signals of cones. Moreover, we found that the temporal frequency of flicker matches the frequency where sensitivity is maximal in a wide range of fish taxa, suggesting that the flicker may actually enhance the detection of objects. Finally, using modeling of the chromatic contrast between fish pattern and background under flickering illumination, we found that the spectral sensitivity of cones in a cichlid focal species is optimally tuned to maximize the visual contrast between fish pattern and background, instead of to produce a flicker-free visual signal. CONCLUSIONS: The violation of its two critical assumptions substantially undermines support for the flicker theory as originally formulated. While this alone does not support the contrast theory, comparison of the contrast and flicker theories revealed that the visual system of our focal species was tuned as predicted by the contrast theory rather than by the flicker theory (or by some combination of the two). Thus, these findings challenge key assumptions of the flicker theory, leaving the contrast theory as the most parsimonious and tenable account of the evolution of multiple cone classes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".