Effect of Machine Reliability on the Cognitive Processes of the Task Performance
Bibliographic record
Abstract
Brain machine interfaces (BMI) are becoming increasingly prevalent in diverse applications including motor rehabilitation, virtual reality training, etc. Two critical aspects of an effective BMI are machine reliability and cognitive workload (CWL). Previous studies have reported a notable effect of machine reliability on the 6 factors of the CWL. However, it remains unclear whether this effect can be detected in cognitive processes. Electroencephalography (EEG) is a widely used technique to explore cognitive processes by recording brain activities as signals. Therefore, we utilized the event-related spectral power (ERSP) feature of EEG signals to determine the cognitive processes regarding the effect of machine reliability. The results revealed that machine reliability affected the CWL factor of performance which was reflected in the$y$band activities of the right prefrontal cortex. The findings indicate the potential of cognitive processes in detecting the effect of machine reliability. The detection could pave the way for designing adaptive BMI to balance the machine reliability and the CWL.
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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 | 0.002 | 0.023 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".