Representationalism, perceptual distortion and the limits of phenomenal concepts
Bibliographic record
Abstract
This paper replies to objections from perceptual distortion (blur, perspective, double vision, etc.) against the representationalist thesis that the phenomenal characters of experiences supervene on their intentional contents. It has been argued that some pairs of distorted and undistorted experiences share contents without sharing phenomenal characters, which is incompatible with the supervenience thesis. In reply, I suggest that such cases are not counterexamples to the representationalist thesis because the contents of distorted experiences are always impoverished in some way compared to those of normal experiences. This can be shown by considering limit cases of perceptual distortion, for example, maximally blurry experiences, which manifestly lack details present in clear experiences. I argue that since there is no reasonable way to draw the line between distorted experiences that have degraded content and distorted experiences that do not, we should allow that an increase in distortion is always accompanied by a degradation in content. I also discuss the prospects for a positive account of the contents specific to distorted experiences. I argue that the prospects for such an account are dim, but that this is due to limitations of our phenomenal concepts, not to the falsity of the representationalist thesis.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.057 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".