Bootstrap analysis of the single subject with event related potentials
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
Neural correlates of cognitive states in event-related potentials (ERPs) serve as markers for related cerebral processes. Although these are usually evaluated in subject groups, the ability to evaluate such markers statistically in single subjects is essential for case studies in neuropsychology. Here we investigated the use of a simple test based on nonparametric bootstrap confidence intervals for this purpose, by evaluating three different ERP phenomena: the face-selectivity of the N170, error-related negativity, and the P3 component in a Posner cueing paradigm. In each case, we compare single-subject analysis with statistical significance determined using bootstrap to conventional group analysis using analysis of variance (ANOVA). We found that the proportion of subjects who show a significant effect at the individual level based on bootstrap varied, being greatest for the N170 and least for the P3. Furthermore, it correlated with significance at the group level. We conclude that the bootstrap methodology can be a viable option for interpreting single-case ERP amplitude effects in the right setting, probably with well-defined stereotyped peaks that show robust differences at the group level, which may be more characteristic of early sensory components than late cognitive effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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