Sentimental perceptualism and the challenge from cognitive bases
Bibliographic record
Abstract
According to a historically popular view, emotions are normative experiences that ground moral knowledge much as perceptual experiences ground empirical knowledge. Given the analogy it draws between emotion and perception, sentimental perceptualism constitutes a promising, naturalist-friendly alternative to classical rationalist accounts of moral knowledge. In this paper, we consider an important but underappreciated objection to the view, namely that in contrast with perception, emotions depend for their occurrence on prior representational states, with the result that emotions cannot give perceptual-like access to normative properties. We argue that underlying this objection are several specific problems, rooted in the different types of mental states to which emotions may respond, that the sentimental perceptualist must tackle for her view to be successful. We argue, moreover, that the problems can be answered by filling out the theory with several independently motivated yet highly controversial commitments, which we carefully catalogue. The plausibility of sentimental perceptualism, as a result, hinges on further claims sentimental perceptualists should not ignore.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".