Concurrent Assessment of Sequential Auditory ERPs Using an Optimized Paired-stimulus Local-global Paradigm
Bibliographic record
Abstract
Comprehensive assessment of auditory processing is crucial for understanding perceptual and attentional functions, as well as detecting related deficits in clinical populations. Auditory event-related potentials (ERPs) track key stages through time-locked components that emerge from early sensory processing (P1-N1-P2 complex) and automatic deviance detection (mismatch negativity, MMN) to involuntary attention orienting (P3a) and voluntary attention engagement (P3b). However, current approaches predominantly focus on isolated ERP components demonstrated through group-level statistical difference, while paradigms capable of capturing sequential components with high individual sensitivity remain scarce. Here, we optimized the local-global paradigm with a paired-stimulus design, strategically capturing pre-attentive to voluntary processing by contrasting responses to within-pair violations (local effect) versus across-pair violations (global effect). We evaluated this paradigm in 30 healthy participants under both active (target counting) and passive (visual distraction) conditions. Results demonstrated that both conditions reliably elicited complete pre-attentive components (P1-N1-P2 and MMN) as confirmed by cluster-based permutation tests, achieving 30/30 individual-level sensitivity validated through intrasubject classification analysis. Furthermore, comparison between active and passive conditions revealed significant differences specifically in the 272-392ms and 272-400ms window ( p < 0.05) under two levels of global deviants. This contrast successfully dissociated voluntary from involuntary attention with 86.67% and 93.33% individual sensitivity, respectively. Moreover, the active-passive discrimination depended primarily on the number of epochs sampled ( p <0.001) rather than the number of sensors used ( p >0.05). These findings validate our paired-stimulus local-global paradigm as a reliable approach for assessing sequential auditory ERPs, offering significant advantages with potential applications in clinical evaluation of perceptual and attentional impairments.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".