Listening under difficult conditions: An activation likelihood estimation meta‐analysis
Bibliographic record
Abstract
The brain networks supporting speech identification and comprehension under difficult listening conditions are not well specified. The networks hypothesized to underlie effortful listening include regions responsible for executive control. We conducted meta-analyses of auditory neuroimaging studies to determine whether a common activation pattern of the frontal lobe supports effortful listening under different speech manipulations. Fifty-three functional neuroimaging studies investigating speech perception were divided into three independent Activation Likelihood Estimate analyses based on the type of speech manipulation paradigm used: Speech-in-noise (SIN, 16 studies, involving 224 participants); spectrally degraded speech using filtering techniques (15 studies involving 270 participants); and linguistic complexity (i.e., levels of syntactic, lexical and semantic intricacy/density, 22 studies, involving 348 participants). Meta-analysis of the SIN studies revealed higher effort was associated with activation in left inferior frontal gyrus (IFG), left inferior parietal lobule, and right insula. Studies using spectrally degraded speech demonstrated increased activation of the insula bilaterally and the left superior temporal gyrus (STG). Studies manipulating linguistic complexity showed activation in the left IFG, right middle frontal gyrus, left middle temporal gyrus and bilateral STG. Planned contrasts revealed left IFG activation in linguistic complexity studies, which differed from activation patterns observed in SIN or spectral degradation studies. Although there were no significant overlap in prefrontal activation across these three speech manipulation paradigms, SIN and spectral degradation showed overlapping regions in left and right insula. These findings provide evidence that there is regional specialization within the left IFG and differential executive networks underlie effortful listening.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".