从“嗯”到“对”：人类对话中信息流的产生、预测与调节
From "um" to "yeah": Producing, predicting, and regulating information flow in human conversation
AI总结：
本研究利用大型语言模型分析CANDOR对话语料库，估计非结构化对话信息密度约13比特/秒，并发现反馈信号调节信息产生速率，为认知资源协商理论提供新见解。
摘要：
对话需要注意力。说话者必须调用词语，听者必须理解这些词语，而双方必须共同协商这一信息流，所有这些都发生在几分之一秒内。我们使用大型语言模型在英语对话的大规模数据集CANDOR语料库中研究了这一过程。我们提供了非结构化对话信息密度的新估计，约为每秒13比特，并发现与检索和呈现该信息的认知负荷相关的显著效应。我们还揭示了反馈信号（backchannels）——即听者提供的简短的“对”、“嗯嗯”和“嗯哼”——在调节新颖性产生中的作用：反馈信号出现前的阶段与信息率下降相关，而其后的言语则回升至先前的速率。我们的结果为长期以来的理论提供了新的见解，这些理论涉及我们如何应对认知资源需求的波动，以及我们如何与他人合作协商这些需求。
英文摘要：
Conversation demands attention. Speakers must call words to mind, listeners must make sense of them, and both together must negotiate this flow of information, all in fractions of a second. We used large language models to study how this works in a large-scale dataset of English-language conversation, the CANDOR corpus. We provide a new estimate of the information density of unstructured conversation, of approximately 13 bits/second, and find significant effects associated with the cognitive load of both retrieving, and presenting, that information. We also reveal a role for backchannels -- the brief yeahs, uh-huhs, and mhmms that listeners provide -- in regulating the production of novelty: the lead-up to a backchannel is associated with declining information rate, while speech downstream rebounds to previous rates. Our results provide new insights into long-standing theories of how we respond to fluctuating demands on cognitive resources, and how we negotiate those demands in partnership with others.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
