Phonological Representations Are Unconsciously Used when Processing Complex, Non-Speech Signals
Bibliographic record
Abstract
Neuroimaging studies of speech processing increasingly rely on artificial speech-like sounds whose perceptual status as speech or non-speech is assigned by simple subjective judgments; brain activation patterns are interpreted according to these status assignments. The naïve perceptual status of one such stimulus, spectrally-rotated speech (not consciously perceived as speech by naïve subjects), was evaluated in discrimination and forced identification experiments. Discrimination of variation in spectrally-rotated syllables in one group of naïve subjects was strongly related to the pattern of similarities in phonological identification of the same stimuli provided by a second, independent group of naïve subjects, suggesting either that (1) naïve rotated syllable perception involves phonetic-like processing, or (2) that perception is solely based on physical acoustic similarity, and similar sounds are provided with similar phonetic identities. Analysis of acoustic (Euclidean distances of center frequency values of formants) and phonetic similarities in the perception of the vowel portions of the rotated syllables revealed that discrimination was significantly and independently influenced by both acoustic and phonological information. We conclude that simple subjective assessments of artificial speech-like sounds can be misleading, as perception of such sounds may initially and unconsciously utilize speech-like, phonological processing.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".