Brain Mechanisms Implicated in the Preattentive Categorization of Speech Sounds Revealed Using fMRI and a Short-Interval Habituation Trial Paradigm
Bibliographic record
Abstract
A hallmark of categorical perception is better discrimination of stimulus tokens from 2 different categories compared with token pairs that are equally dissimilar but drawn from the same category. This effect is well studied in speech perception and represents an important characteristic of how the phonetic form of speech is processed. We investigated the brain mechanisms of categorical perception of stop consonants using functional magnetic resonance imaging and a passive short-interval habituation trial design (Zevin and McCandliss 2005). The paradigm takes advantage of neural adaptation effects to identify specific regions sensitive to an oddball stimulus presented in the context of a repeated item. These effects were compared for changes in stimulus characteristics that result in either a between-category (phonetic and acoustic) or a within-category (acoustic only) stimulus shift. Significantly greater activation for between-category than within-category stimuli was observed in left superior sulcus and middle temporal gyrus as well as in inferior parietal cortex. In contrast, only a subcortical region specifically responded to within-category changes. The data suggest that these habituation effects are due to the unattended detection of a phonetic stimulus feature.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".