The perception of frequency modulated sounds in tone and non-tone language speakers: An electroencephalography study
Bibliographic record
Abstract
Speakers of tone languages acquire expertise in discriminating and identifying frequency modulated auditory signals [Chandrasekaran et al., Brain Res., 1128, 148–156 (2007); Kaan et al., Brain Res. 1148, 113–122 (2007)], as their native language utilizes such acoustic properties to cue lexical differences. How this expertise influences auditory neurophysiological responses to non-speech stimuli, however, remains poorly understood. We tested adult tone and non-tone language speakers in their automatic brain processing of non-speech frequency modulated (FM) tone chirps. Participants were presented with a series of FM tones in a many-to-one oddball mismatch negativity [MMN; Näätänen and Winkler, Psychol. Bull. 125, 826–856 (1999)] paradigm that varied in whether the modulation was concave or convex in nature and whether the difference between the tone chirp onset and offset frequencies was relatively large or small. The results revealed that the tone group produced a larger MMN than the non-tone group. Moreover, tone language participants produced significantly larger negative deflections in the event-related potential than the non-tone participants in response to both deviant types across a large post-stimulus time-window. Consequently, tone language speakers’ expertise in processing frequency cues impacts their neurophysiological responses to non-linguistic stimuli that vary along similar acoustic properties to linguistic stimuli.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".