What does this research mean for the field?

AI-generated facial expressions influence client acceptance based on whether they are informed that the expressions are AI-generated. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To investigate the mechanisms of dialogue and empathy in psychotherapy using advanced technologies.

March 12, 2026The Proceedings of the Annual Convention of the Japanese Psychological Association0 citationsOpen Access

Decoding Dialogue and Empathy

TITakayuki IwayamaShowa Women's University ASAyako SaneyoshiSANE Mental Health Charity SYSatoshi YokoyamaJapanese Red Cross Society Wakayama Medical Center

Key Points

To investigate the mechanisms of dialogue and empathy in psychotherapy using advanced technologies.
Examined the acceptance of AI-generated facial images based on prior labeling as artificial.
Analyzed the impact of therapist self-disclosure on client brain synchronization and gaze patterns in a multimodal study.
Predicted clients' subjective evaluations of therapist expressions in remote sessions using machine learning.
AI-generated images were evaluated differently based on prior knowledge of their artificial nature.
Therapist self-disclosure influenced brain synchronization and gaze patterns between client and therapist.
Machine learning successfully predicted client assessments of therapist expressions during non-face-to-face sessions.

Abstract

心理療法における効果的な介入には、言語的・非言語的側面を含む対人相互作用のプロセスを精緻に把握することが不可欠である。本シンポジウムでは、ai、脳間同期、機械学習などの先端技術を活用し、心理療法における対話と共感形成のメカニズムを解明しようとする試みを紹介する。まず、aiで生成された表情画像が、事前に「ai生成物である」と教示されたか否かによって、受容のされ方にどのような変化が生じるかを検討した研究を紹介する。続いて、セラピストの自己開示の内容がクライエントとの脳間同期や視線パターンに与える影響を分析したマルチモーダル研究を紹介する。最後に、非対面セッションにおけるセラピストの表情に対するクライエントの主観的評価を機械学習によって予測する研究を紹介する。これらの話題提供を踏まえ、臨床心理学と先端技術の両面から睡眠・共感・医療コミュニケーションを研究する指定討論者を迎え、先端技術の応用可能性や今後の課題について多角的に議論を深める。

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Cite This Study

Iwayama et al. (2025) studied this question.

synapsesocial.com/papers/69b2575e96eeacc4fcec5e93 https://doi.org/10.4992/pacjpa.89.0_87

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March 12, 2026The Proceedings of the Annual Convention of the Japanese Psychological Association0 citationsOpen Access

Decoding Dialogue and Empathy

TITakayuki IwayamaShowa Women's University ASAyako SaneyoshiSANE Mental Health Charity SYSatoshi YokoyamaJapanese Red Cross Society Wakayama Medical Center

Key Points

To investigate the mechanisms of dialogue and empathy in psychotherapy using advanced technologies.
Examined the acceptance of AI-generated facial images based on prior labeling as artificial.
Analyzed the impact of therapist self-disclosure on client brain synchronization and gaze patterns in a multimodal study.
Predicted clients' subjective evaluations of therapist expressions in remote sessions using machine learning.
AI-generated images were evaluated differently based on prior knowledge of their artificial nature.
Therapist self-disclosure influenced brain synchronization and gaze patterns between client and therapist.
Machine learning successfully predicted client assessments of therapist expressions during non-face-to-face sessions.

Abstract

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View Full Paper

Cite This Study

Iwayama et al. (2025) studied this question.

synapsesocial.com/papers/69b2575e96eeacc4fcec5e93 https://doi.org/10.4992/pacjpa.89.0_87

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