Auditory cortical potentials indices in phonological awareness: a systematic review
Bibliographic record
Abstract
To investigate whether the N1, P2, and N2 auditory evoked potentials reflect performance in phonological awareness tasks. A systematic search was conducted using keywords such as “cortical auditory potential,” “N1,” “P2,” “N2,” and “phonological awareness” across databases (CINAHL, Embase, PubMed/Medline, Web of Science) and gray literature (Google Scholar, OpenGrey, ProQuest). Human studies were included if they analysed N1, P2, and/or N2 components in relation to phonological awareness tasks, either by correlating these components with task performance or comparing latency and amplitude across groups with different levels of phonological awareness. Of 371 records, seven studies (2004–2021) from Brazil, the Netherlands, China, Canada, and Finland were selected. Four were case-control studies, two were intervention studies, and one was observational. Four used phonological priming, and three used standardised phonological awareness assessments. Five studies suggested that the N1, P2, and N2 components may predict phonological awareness performance. The N1, N2, and P2 components are potential predictors of phonological processing. Tasks involving phonological priming—especially rhyme and alliteration—modulate N1 and N2, while the P2 component, though less studied, appears to be sensitive to alliteration. Future research should explore these relationships in greater detail for clinical applications.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".