EEG Investigations of Duration Discrimination: The Intermodal Effect Is Induced by an Attentional Bias
Bibliographic record
Abstract
Previous studies indicated that empty time intervals are better discriminated in the auditory than in the visual modality, and when delimited by signals delivered from the same (intramodal intervals) rather than from different sensory modalities (intermodal intervals). The present electrophysiological study was conducted to determine the mechanisms which modulated the performances in inter- and intramodal conditions. Participants were asked to categorise as short or long empty intervals marked by auditory (A) and/or visual (V) signals (intramodal intervals: AA, VV; intermodal intervals: AV, VA). Behavioural data revealed that the performances were higher for the AA intervals than for the three other intervals and lower for inter- compared to intramodal intervals. Electrophysiological results indicated that the CNV amplitude recorded at fronto-central electrodes increased significantly until the end of the presentation of the long intervals in the AA conditions, while no significant change in the time course of this component was observed for the other three modalities of presentation. They also indicated that the N1 and P2 amplitudes recorded after the presentation of the signals which delimited the beginning of the intervals were higher for the inter- (AV/VA) compared to the intramodal intervals (AA/VV). The time course of the CNV revealed that the high performances observed with AA intervals would be related to the effectiveness of the neural mechanisms underlying the processing of the ongoing interval. The greater amplitude of the N1 and P2 components during the intermodal intervals suggests that the weak performances observed in these conditions would be caused by an attentional bias induced by the cognitive load and the necessity to switch between modalities.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".