Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.
Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
PLoS One. 2023 Jul 19;18(7):e0288104. doi: 10.1371/journal.pone.0288104. eCollection 2023.
The early recognition of fundamental social actions, like questions, is crucial for understanding the speaker's intended message and planning a timely response in conversation. Questions themselves may express more than one social action category (e.g., an information request "What time is it?", an invitation "Will you come to my party?" or a criticism "Are you crazy?"). Although human language use occurs predominantly in a multimodal context, prior research on social actions has mainly focused on the verbal modality. This study breaks new ground by investigating how conversational facial signals may map onto the expression of different types of social actions conveyed through questions. The distribution, timing, and temporal organization of facial signals across social actions was analysed in a rich corpus of naturalistic, dyadic face-to-face Dutch conversations. These social actions were: Information Requests, Understanding Checks, Self-Directed questions, Stance or Sentiment questions, Other-Initiated Repairs, Active Participation questions, questions for Structuring, Initiating or Maintaining Conversation, and Plans and Actions questions. This is the first study to reveal differences in distribution and timing of facial signals across different types of social actions. The findings raise the possibility that facial signals may facilitate social action recognition during language processing in multimodal face-to-face interaction.
早期识别基本的社交动作,如问题,对于理解说话者的意图信息和在对话中及时做出回应至关重要。问题本身可能表达不止一种社交动作类别(例如,信息请求“几点了?”,邀请“你会来参加我的派对吗?”或批评“你疯了吗?”)。尽管人类语言主要在多模态环境中使用,但先前关于社交动作的研究主要集中在言语模态上。本研究通过调查对话中的面部信号如何映射到通过问题传达的不同类型的社交动作的表达,开辟了新的研究领域。在一个丰富的自然主义、面对面的荷兰对话语料库中,分析了社交动作中面部信号的分布、时间和时间组织。这些社交动作是:信息请求、理解检查、自我导向问题、立场或情绪问题、他人发起的修复、积极参与问题、用于构建、发起或维持对话的问题,以及计划和行动问题。这是第一个揭示不同类型社交动作中面部信号分布和时间差异的研究。研究结果提出了一种可能性,即面部信号可能有助于在多模态面对面互动中进行语言处理时识别社交动作。