Is the auditory evoked P2 response a biomarker of learning?
Bibliographic record
Abstract
Even though auditory training exercises for humans have been shown to improve certain perceptual skills of individuals with and without hearing loss, there is a lack of knowledge pertaining to which aspects of training are responsible for the perceptual gains, and which aspects of perception are changed. To better define how auditory training impacts brain and behavior, electroencephalography (EEG) and magnetoencephalography (MEG) have been used to determine the time course and coincidence of cortical modulations associated with different types of training. Here we focus on P1-N1-P2 auditory evoked responses (AEP), as there are consistent reports of gains in P2 amplitude following various types of auditory training experiences; including music and speech-sound training. The purpose of this experiment was to determine if the auditory evoked P2 response is a biomarker of learning. To do this, we taught native English speakers to identify a new pre-voiced temporal cue that is not used phonemically in the English language so that coinciding changes in evoked neural activity could be characterized. To differentiate possible effects of repeated stimulus exposure and a button-pushing task from learning itself, we examined modulations in brain activity in a group of participants who learned to identify the pre-voicing contrast and compared it to participants, matched in time, and stimulus exposure, that did not. The main finding was that the amplitude of the P2 auditory evoked response increased across repeated EEG sessions for all groups, regardless of any change in perceptual performance. What's more, these effects are retained for months. Changes in P2 amplitude were attributed to changes in neural activity associated with the acquisition process and not the learned outcome itself. A further finding was the expression of a late negativity (LN) wave 600-900 ms post-stimulus onset, post-training exclusively for the group that learned to identify the pre-voiced contrast.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".