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首页> 外文期刊>Journal of personality and social psychology >It Doesn't Hurt to Ask: Question-Asking Increases Liking
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It Doesn't Hurt to Ask: Question-Asking Increases Liking

机译:询问并没有伤害:问题询问增加了

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摘要

Conversation is a fundamental human experience that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational behavior: question-asking. Across 3 studies of live dyadic conversations, we identify a robust and consistent relationship between question-asking and liking: people who ask more questions, particularly follow-up questions, are better liked by their conversation partners. When people are instructed to ask more questions, they are perceived as higher in responsiveness, an interpersonal construct that captures listening, understanding, validation, and care. We measure responsiveness with an attitudinal measure from previous research as well as a novel behavioral measure: the number of follow-up questions one asks. In both cases, responsiveness explains the effect of question-asking on liking. In addition to analyzing live get-to-know-you conversations online, we also studied face-to-face speed-dating conversations. We trained a natural language processing algorithm as a "follow-up question detector" that we applied to our speed-dating data (and can be applied to any text data to more deeply understand question-asking dynamics). The follow-up question rate established by the algorithm showed that speed daters who ask more follow-up questions during their dates are more likely to elicit agreement for second dates from their partners, a behavioral indicator of liking. We also find that, despite the persistent and beneficial effects of asking questions, people do not anticipate that question-asking increases interpersonal liking.
机译:谈话是一种基本的人类体验,是在无数背景,关系和沟通方式上追求顽固的顽固和人际球目标。在目前的研究中,我们隔离了一个深入的对话行为的作用:问问。在3种实时谈话的研究中,我们确定了问询和喜欢之间的强大和一致的关系:询问更多问题,特别是后续问题的人,他们的对话伙伴更好地喜欢。当人们被指示提出更多问题时,它们被视为响应性的更高,是捕捉听力,理解,验证和关怀的人际态度。我们衡量以前研究的态度措施以及小说行为措施的响应能力:提出后续问题的数量。在这两种情况下,响应性解释了问题的效果。除了在线分析现场上知识的对话外,我们还研究了面对面的速度约会对话。我们培训了一种自然语言处理算法作为我们应用于我们的速度约会数据的“后续问题检测器”(并且可以应用于任何文本数据,以更深刻地理解问询动态)。该算法建立的后续问题率表明,在日期期间提出更多后续问题的速度数据师更有可能从合作伙伴那里获得第二个日期的协议,这是一种喜欢的行为指标。我们还发现,尽管提出了问题的持续和有益的效果,但人们不会预测问题,提出质疑会增加人际关系的喜好。

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