SVM neural decoding of EEG for words and non-words across speakers, dialects, and genders
Bibliographic record
Abstract
Decades of behavioural research have shown that word recognition is an incremental process involving competition among multiple lexical candidates (Huettig et al. 2011). Recent work by McMurray et al. (2022) demonstrated that SVM-based machine learning can decode the neural spatiotemporal encoding of phonetically similar words and non-words from EEG signals, and that decoding response patterns closely mirror prototypical lexical competition effects. Here we describe two studies that (i) replicate McMurray et al. (2022) and (ii) extend this paradigm one step further, by decoding EEG responses to words and non-words across different speakers, dialects, and sexes. Additionally, we assess the decoder’s sensitivity to individual differences by correlating its performance with behavioral task data. We conclude that this algorithmically simple decoder can be a powerful tool for uncovering neural psycholinguistic dynamics, but that it requires an amount of data that currently limits applications to developmental or clinical populations.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".