Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Labeling Emotion01:20

Labeling Emotion

237
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
237
Cognitive Theories: Lazarus Mediational Theory of Emotion01:17

Cognitive Theories: Lazarus Mediational Theory of Emotion

1.2K
Richard Lazarus' cognitive mediational theory highlights the pivotal role of cognitive appraisal in shaping emotional responses. According to this theory, the evaluation of a stimulus — based on personal values, goals, beliefs, and expectations — mediates the emotional response. This appraisal process is immediate and often occurs unconsciously, influencing the intensity and nature of the resulting emotion.
Cognitive Appraisal and Emotional Response
Lazarus proposed that...
1.2K
Nonconscious Mimicry01:13

Nonconscious Mimicry

4.6K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.6K
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

579
Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
579
Emotional Expression01:26

Emotional Expression

367
Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
Universal Facial Expressions
Psychologist Paul Ekman identified seven basic...
367
Coping Strategies: Emotion Focused01:20

Coping Strategies: Emotion Focused

147
Emotion-focused coping refers to a set of strategies aimed at managing the emotional impact of stressors, rather than directly addressing their causes. This approach involves altering one's emotional response to stressful situations to reduce their psychological effects. For example, individuals might talk with a friend or engage in activities like journaling to express their feelings. Such actions can help achieve emotional clarity or release, providing the psychological stability needed...
147

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Ag/Ag<sub>2</sub>S plasmonic heterostructure promotes piezoelectric photocatalytic activity of BiFeO<sub>3</sub> nanofibers for degradation of ciprofloxacin and energy conversion.

Journal of environmental sciences (China)·2025
Same author

Fibrous MoS<sub>2</sub>/Bi<sub>2</sub>S<sub>3</sub>/BiFeO<sub>3</sub> ternary heterojunction boosts piezoelectric photocatalytic performance.

Journal of colloid and interface science·2024
Same author

Wear-free sliding electrical contacts with ultralow electrical resistivity.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Scaling Laws for the Influence of Gravity and Its Gradient on Dropwise Condensation: A Simulation Study.

Langmuir : the ACS journal of surfaces and colloids·2024
Same author

Copper-based electro-catalytic nitrate reduction to ammonia from water: Mechanism, preparation, and research directions.

Environmental science and ecotechnology·2024
Same author

Aligned Dipoles Induced Electric-Field Promoting Zinc-Ion De-Solvation toward Highly Stable Dendrite-Free Zinc-Metal Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2023
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

感情の特徴を備えたLLMを統合した欺瞞検出モデル

Chucheng Zhou1, Yingqian Zhang2,3, Chengcong Lin4

  • 1School of Computing and Data Science, Xiamen University Malaysia, Sepang, Selangor, 43900, Malaysia.

Scientific reports
|September 1, 2025
PubMed
まとめ
この要約は機械生成です。

この研究では,感情強化の欺瞞検出モデルである XGBoost と RoBERTa ベースの Emotion Features (LieXBerta) を用いて,法廷での正確性を向上させています. LieXBertaモデルは87.50%の精度を達成し,従来の方法を上回りました.

キーワード:
法廷での尋問詐欺の検出感情的な特徴機械学習ロバート

さらに関連する動画

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
07:36

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

Published on: May 3, 2016

8.6K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

関連する実験動画

Last Updated: Sep 9, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K
An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
07:36

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

Published on: May 3, 2016

8.6K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

科学分野:

  • 人工知能
  • 機械学習
  • 自然言語処理

背景:

  • 伝統的な嘘発見方法は 人間の尋問者に大きく依存しており 主観性と誤判を 引き起こす可能性があります
  • 客観的で正確な欺瞞検出システムの開発は 法的および法医学的な応用において極めて重要です

研究 の 目的:

  • 客観性と正確性を向上させるために,XGBoostとRoBERTaベースのEmotion Features (LieXBerta) を使用したエモーション強化の欺瞞検出モデルを提案し,評価する.
  • 感情的,顔面的,行動的特徴を統合して より堅実な欺瞞検出を可能にします

主な方法:

  • 尋問テキストから感情的な特徴を抽出するために,強固に最適化されたBERTプレトレーニングアプローチ (RoBERTa) を利用しました.
  • 顔面と行動の特徴を組み合わせた 抽出した感情的な特徴
  • Extreme Gradient Boosting (XGBoost) 分類器を欺瞞検出タスクのために使用しました.
  • モデル検証のための詳細な感情的な特徴で充実したテストテキストデータセットを開発しました.

主要な成果:

  • 感情的な特徴を組み込んだLieXBertaモデルは,従来の特徴のみを使用したベースラインモデルと他の古典的な機械学習モデルと比較して優れたパフォーマンスを示しました.
  • パラメータチューニングの結果,LieXBertaモデルの精度は87.50%となり,ベースラインより6.5%改善しました.
  • 調整された LieXBerta モデルの機能の減少は,実行時間を 42% 減少させ,トレーニング効率と予測パフォーマンスを向上させました.

結論:

  • 提案された LieXBerta モデルは,感情的な特徴を統合することで,欺瞞検出の客観性と精度を大幅に高めます.
  • このモデルは法廷での応用の可能性があり,従来の方法よりも信頼性の高い代替案を提供しています.
  • 感情の抽出のための RoBERTa と分類のための XGBoost の統合は,高度な欺瞞検出のための効果的な枠組みを提供します.