Psychological effect computation of courtroom arguments: A deep learning approach of EEG signal data
Bibliographic record
Abstract
Previous studies have shown that the attorney?s speeches can exert significant impacts on the cognition and judgment of the jury in court arguments. However, the psychological effects induced by these speeches are intricately tied to subconscious brain states, making them challenging to accurately and comprehensively describe through subjective self-reports. This study aims to explore a neural reaction observation method for psychological effect analysis of the attorney?s speeches in courtroom scenarios. We utilized a corpus of courtroom arguments from legal movies and television series as source material. Participants? psychological responses to these speeches were monitored using wearable electroencephalography (EEG) devices. Building upon this data, we employed a deep learning model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to compute attention intensity, cognitive load, and emotional changes. Our test results demonstrate that this approach enables continuous and dynamic computation within courtroom argument contexts, providing a more accurate assessment of attorneys? language skills.
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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 | 0.002 |
| 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".