How does the infant brain process speech? An fNIRS meta-analysis
Bibliographic record
Abstract
The neural mechanisms of infant speech perception are an important topic of cognitive neuroscientific research. One key theoretical question in this area surrounds the lateralization of infant neural speech responses. Specifically, it is important to determine whether infants have patterns of left lateralization for speech in their frontal and temporal lobes, as is commonly seen in adults. However, the spatial patterns of infant findings are inconsistent; some studies show the frontal and temporal regions of both hemispheres activating, while others demonstrate activation predominantly or only in the left hemisphere. This meta-analysis, for the first time in the field, examines studies using functional near-infrared spectroscopy (fNIRS) to determine the field-wide pattern of activation of the frontal and temporal lobes when infants are exposed to speech. In this pre-registered meta-analysis, we first completed a literature search using keywords through various popular online databases: PubMed, Web of Science, and the UBC Library. Papers were screened according to a pre-determined inclusion criterion. Those selected for our review would be analyzed for their methodologies and activation results. We coded the results for each contrast for each paper (e.g., neural activation patterns to speech stimuli vs. baseline). Overall, there was not strong evidence for left lateralization in either the temporal or the frontal lobes. We also did not find evidence that any lateralization pattern changed with age. One analysis revealed that patterns of lateralization in the left hemisphere may differ depending on language familiarity, with a marginally significant pattern of left lateralization when infants hear a familiar language. Overall, this meta-analysis provides an overview of the current state of the field concerning localized neural responses to speech stimuli and provides a field-wide answer to the question of the patterns of lateralization to speech stimuli found in infancy.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".