Spatiotemporal analysis of experimental differences in event‐related potential data with partial least squares
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One challenge in the analysis of event-related potentials (ERPs) is to identify task-related differences in scalp topography. The multivariate Partial Least Squares (PLS) analysis was used to identify the spatiotemporal distribution of ERP differences related to experimental manipulations. Two simulations included latency shifts and amplitude changes at peaks with temporal overlap. PLS identified effects only at modeled timepoints and electrodes. In contrast, principal components analysis identified differences at most timepoints. We also demonstrated that PLS identified combinations of waveform differences, not isolated sources. ERP components in an auditory oddball task were also assessed with PLS. The primary distinction was between ERPs on hit and correct rejection trials, expressed at multiple timepoints and electrodes. PLS provides a mechanism to describe experimental differences in ERP waveforms, simultaneously across the head.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it