High Working Memory Load Impairs Language Processing during a Simulated Piloting Task: An ERP and Pupillometry Study
Bibliographic record
Abstract
Given the important amount of visual and auditory linguistic information that pilots have to process, operating an aircraft generates a high working-memory load (WML). In this context, the ability to focus attention on relevant information and to remain responsive to concurrent stimuli might be altered. Consequently, understanding the effects of WML on the processing of both linguistic targets and distractors is of particular interest in the study of pilot performance. In the present work, participants performed a simplified piloting task in which they had to follow one of three colored aircraft, according to specific written instructions (i.e., the written word for the color corresponding to the color of one of the aircraft) and to ignore either congruent or incongruent concurrent auditory distractors (i.e., a spoken name of color). The WML was manipulated with an n-back sub-task. Participants were instructed to apply the current written instruction in the low WML condition, and the 2-back written instruction in the high WML condition. Electrophysiological results revealed a major effect of WML at behavioral (i.e., decline of piloting performance), electrophysiological, and autonomic levels (i.e., greater pupil diameter). Increased WML consumed resources that could not be allocated to the processing of the linguistic stimuli, as indexed by lower P300/P600 amplitudes. Also, significantly, lower P600 responses were measured in incongruent vs. congruent trials in the low WML condition, showing a higher difficulty reorienting attention toward the written instruction, but this effect was canceled in the high WML condition. This suppression of interference in the high load condition is in line with the engagement/distraction trade-off model. We propose that P300/P600 components could be reliable indicators of WML and that they allow an estimation of its impact on the processing of linguistic stimuli.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".