Perceptual expansion under cognitive guidance: Lessons from language processing
Bibliographic record
Abstract
This paper aims to provide an empirically informed sketch of how our perceptual capacities can interact with cognitive processes to give rise to new perceptual attributives. In section 1, I present ongoing debates about the reach of perception and direct focus toward arguments offered in recent work by Tyler Burge and Ned Block. In section 2, I draw on empirical evidence relating to language processing to argue against the claim that we have no acquired, culture‐specific, high‐level perceptual attributives. In section 3, I turn to the cognitive dimension; I outline how cognitive procedures (including conceptual representation and explicit inference) can be involved in the acquisition of what ought to, nonetheless, be recognized as genuinely perceptual capacities. Finally, in section 4, I argue for the importance of distinguishing these conclusions from more familiar and radical claims about rampant “cognitive penetration” into the perceptual domain.
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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 | 0.013 |
| 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 | 0.014 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".