Can the Lateralized Readiness Potential Detect Suppressed Manual Responses to Pure Tones?
Bibliographic record
Abstract
BACKGROUND: Willfully not responding to auditory stimuli hampers accurate behavioral measurements. An objective measure of covert manual suppression recorded during response tasks may be useful to assess the veracity of responses to stimuli. PURPOSE: To investigate whether the lateralized readiness potential (LRP), an electrophysiological measure of corticomotor response and suppression, may be of use in determining when participants hear but do not respond to pure tones. RESEARCH DESIGN: Within-subject repeated measures with a Go-NoGo paradigm. STUDY SAMPLE: Five males and five females (mean age = 38.8 years, standard deviation = 8.8) underwent electrophysiology testing. All had normal hearing, except one. INTERVENTION: Participants were tested in a condition where they consistently responded to tonal stimuli, and in a condition where intensity cued whether they should respond or not. Scalp-recorded cortical potentials and behavioral responses were recorded, along with a question that probed the perceived effort required to suppress responses to the stimuli. DATA COLLECTION AND ANALYSIS: Electrophysiology data were processed with independent component analysis and epoch-based artifact rejection. Averaged group and individual LRPs were calculated. RESULTS: Group averaged waveforms show that suppressed responses, cued by NoGo stimuli, diverge positively at approximately 300 msec poststimulus, when compared with performed (Go) responses. LRPs were comparable when Go responses were recorded in a separate condition in which participants responded to all stimuli, and when Go and NoGo trials were included in the same condition. The LRP was not observed in one participant. CONCLUSIONS: Subsequent to further investigation, the LRP may prove suitable in assessing the suppression of responses to audiometric stimuli, and, thereby, useful in cases where functional hearing loss is suspected.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".