Attentional modulation of the perception of illusory vowels and sound onsets: A functional magnetic resonance imaging (fMRI) study
Bibliographic record
Abstract
In a previous study, we exploited the Gestalt principle of closure to create illusory vowel sounds and examined the neural correlates of their perception using fMRI. When two formants of a synthetic vowel are presented in an alternating pattern, filling the gaps in each formant with bursts of noise causes the formants to be heard as continuous and more vowel-like. When this "Illusion" condition was modified by increasing the formant-to-noise ratio (FNR), the formants were heard as interrupted (“Illusion Break” condition) and less vowel-like. BOLD signal in the Middle Temporal Gyrus (MTG) was greater for Illusion than for Illusion-Break stimuli, reflecting the difference in speechlikeness. Primary auditory areas (PAC) exhibited the opposite pattern, probably because Illusion-Break stimuli contain more perceived sound onsets than the Illusion stimuli. In the current study we examine whether the neural activation to illusory vowels and sound onsets is modulated by attention. Participants were scanned while listening to Illusion, Illusion Break, and two types of intact vowels and simultaneously directing their attention either to the vowel stimuli or to auditory or visual distractors. Preliminary analyses suggest that activation to intact and illusory vowels in MTG, and to sound onset in PAC, is modulated by attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".