Perceptual clarity of speech modulates activity in left temporal-lobe regions: fMRI correlates of top-down influences
Bibliographic record
Abstract
Behavioral evidence supports the idea that perception is guided by mechanisms that compute an input's most probable interpretation. For example, four-band noise-vocoded speech, which is largely unintelligible to naive listeners, becomes perceptually clear when listeners possess prior knowledge of the signal content - a phenomenon we call “pop-out”. Feedback connections within and among auditory cortical regions may allow cognitively “higher” levels of processing to predict and thus “explain” the bottom-up signal. In the present study, we use fMRI to investigate the network underlying pop-out. We use written primes (matching or nonmatching text strings) to manipulate a subject's perception of single noise-vocoded words, thereby creating acoustically matched conditions that elicit the perceptions of intelligible or unintelligible speech. Whole brain fMRI data were gathered from 21 subjects using a sparse-imaging procedure. Preliminary analysis reveals regions of statistically significant signal change along the left superior and middle temporal gyri and superior temporal sulcus for matching trials that induce perceptual popout. Functional connectivity analysis will allow us to investigate coupling among these auditory regions, and how connectivity is modulated depending on the perceptual coherence of a noise-vocoded utterance.
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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 | 0.002 |
| 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) | 0.002 | 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".