Predicting Reading From Behavioral and Neural Measures – A Longitudinal Event-Related Potential Study
Bibliographic record
Abstract
Fluent reading is characterized by fast and effortless decoding of visual and phonological information. Here we used event-related potentials (ERPs) and neuropsychological testing to probe the neurocognitive basis of reading in a sample of children with a wide range of reading skills. We report data of 51 children who were measured at two time points, i.e., at the end of first grade (mean age 7.6 years) and at the end of fourth grade (mean age 10.5 years). The aim of this study was to clarify whether next to behavioral measures also basic unimodal and bimodal neural measures help explaining the variance in the later reading outcome. Specifically, we addressed the question of whether next to the so far investigated unimodal measures of N1 print tuning and mismatch negativity (MMN), a bimodal measure of audiovisual integration (AV) contributes and possibly enhances prediction of the later reading outcome. We found that the largest variance in reading was explained by the behavioral measures of rapid automatized naming (RAN), block design and vocabulary (46%). Furthermore, we demonstrated that both unimodal measures of N1 print tuning (16%) and filtered MMN (7%) predicted reading, suggesting that N1 print tuning at the early stage of reading acquisition is a particularly good predictor of the later reading outcome. Beyond the behavioral measures, the two unimodal neural measures explained 7.2% additional variance in reading, indicating that basic neural measures can improve prediction of the later reading outcome over behavioral measures alone. In this study, the AV congruency effect did not significantly predict reading. It is therefore possible that audiovisual congruency effects reflect higher levels of multisensory integration that may be less important for reading acquisition in the first year of learning to read, and that they may potentially gain on relevance later on.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".