CAREER: Tracking correlation or inferring causation: How human language processing adapts to the environment

当人们面对面交谈时，在电话中，或通过视频会议，信号往往是嘈杂和不完整的。幸运的是，交流通常仍然顺利进行，因为听者可以使用其他信息来源（语境、视觉线索等）对说话者所说的内容做出合理的推断。该职业奖旨在通过研究听众如何灵活地适应不同背景下的推断和期望，扩展我们对这一过程的理论理解，并完善现有的计算账户。例如，当与孩子交谈而不是与另一个成年人交谈或与其他口音不同的成年人交谈时，成年人如何调整他们的期望？这项工作的结果将推进认知过程的知识，使人类能够沟通，并可能对支持人与人或人与人工智能对话的技术发展具有重要意义。PI在一个为西班牙裔服务的机构，计划在多个层面上扩大对STEM科学的参与，从对本科生的一对一指导到高级本科课程和外展讲座系列。该项目研究了听众如何灵活地适应不同的语境（例如，不同的说话者/环境），从人类交流的角度出发，作为不确定输入下的理性推理。两种可能的机制是听者跟踪环境中的相关性，或者他们部署和更新说话者和传输过程的因果模型。一系列的眼球追踪实验，加上计算机模拟，将捕捉听众在听演讲时“匆忙”做出的推断。通过改变错误和噪音的性质和数量，听众如何根据上下文学习和调整他们的表述将被探索。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

When people converse face-to-face, on the phone, or by video conference, the signal is often noisy and incomplete. Luckily, communication usually still proceeds smoothly because listeners can use other sources of information (the context, visual cues, etc.) to make rational inferences about what the speaker said. This CAREER award aims to expand our theoretical understanding of this process and refine existing computational accounts, by investigating how listeners flexibly adapt their inferences and expectations in different contexts. For example, how do adults adapt their expectations when speaking with a child rather than with another adult or when speaking with other adults with different accents? Results of this work will advance knowledge of the cognitive processes that enable human communication and may have important implications for the development of technologies that support human-to-human or human-to-AI conversations. The PI is in a Hispanic-Serving Institution and plans to broaden participation in STEM science on multiple scales, from one-on-one mentorship of undergraduates to an upper-level undergraduate course and an outreach talk series.The project investigates how listeners flexibly adapt to different contexts (e.g., different speakers/environments), within the perspective of human communication as rational inference under uncertain input. Two possible mechanisms are that listeners track correlations in the environment or that they deploy and update a causal model of the speaker and transmission process. A series of eye-tracking experiments, paired with computational simulations, will capture the inferences that listeners make “on the fly,” when listening to speech. How listeners learn and adjust their representations to the context will be probed by varying the nature and quantity of errors and noise.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.