Perceptual noise and the bell curve objection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Perceptual experience supports the assignment of confidences in belief – doxastic confidences. To explain this fact, many philosophers appeal to Perceptual Indeterminacy, which holds that perceptual content can be more or less determinate. Others instead appeal to Perceptual Confidence, which says that perceptual experience supports doxastic confidences because it assigns confidences too. Morrison argues that a primary reason to favour Perceptual Confidence is that it is uniquely capable of accounting for bell-shaped doxastic confidence distributions; we call this the bell curve objection to Perceptual Indeterminacy. Here we show that two recent defences of Perceptual Indeterminacy, due to Nanay and Raleigh and Vindrola, fail to adequately address the bell curve objection. But we also argue that all is not lost for proponents of Perceptual Indeterminacy. They can counter the bell curve objection by embracing a third view, which we call Perceptual Noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it