Internal consistency reliability of the P300 to novelty in infants: The influence of trial number and data loss due to artifacts
Bibliographic record
Abstract
The P300 is an event-related potential component that reflects attention to motivationally salient stimuli and may be a promising tool to examine individual differences in cognitive-affective processing very early in development. However, the psychometric properties of the P300 in infancy are unknown, a fact that limits the component's utility as an individual difference measure in developmental research. To address this gap, 38 infants completed an auditory three-stimulus oddball task that included frequent standard, infrequent deviant, and novel stimuli. We quantified the P300 at a single electrode site and at region of interest (ROI) and examined the internal consistency reliability of the component, both via split-half reliability and as a function of trial number. Results indicated that the P300 to standard, deviant, and novel stimuli fell within moderate to high internal consistency reliability thresholds, and that scoring the component at an ROI led to slightly higher estimates of reliability. However, the percentage of data loss due to artifacts increased across the course of the task, suggesting that including more trials will not necessarily improve the reliability of the P300. Together, these results suggest that robust and reliable measurement of the P300 will require designing tasks that minimize trial number and maximize infant tolerability.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".