Auditory Perceptual Category Formation Does Not Require Perceptual Warping
Bibliographic record
Abstract
Categorical perception occurs when a perceiver's stimulus classifications affect their ability to make fine perceptual discriminations and is the most intensively studied form of category learning. On the basis of categorical perception studies, it has been proposed that category learning proceeds by the deformation of an initially homogeneous perceptual space ("perceptual warping"), so that stimuli within the same category are perceived as more similar to each other (more difficult to tell apart) than stimuli that are the same physical distance apart but that belong to different categories. Here, we present a significant counterexample in which robust category learning occurs without these differential perceptual space deformations. Two artificial categories were defined along the dimension of pitch for a perceptually unfamiliar, multidimensional class of sounds. A group of participants (selected on the basis of their listening abilities) were trained to sort sounds into these two arbitrary categories. Category formation, verified empirically, was accompanied by a heightened sensitivity along the entire pitch range, as indicated by changes in an EEG index of implicit perceptual distance (mismatch negativity), with no significant resemblance to the local perceptual deformations predicted by categorical perception. This demonstrates that robust categories can be initially formed within a continuous perceptual dimension without perceptual warping. We suggest that perceptual category formation is a flexible, multistage process sequentially combining different types of learning mechanisms rather than a single process with a universal set of behavioral and neural correlates.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".