Building a comprehensive theory of pragmatic language through large-scale experiments, computation, and neurodiversity

该奖项是作为NSF社会、行为和经济学博士后研究奖学金(SPRF)计划的一部分提供的。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。在Edward Gibson博士和Evelina Fedorenko博士的赞助下，这一博士后奖学金奖项支持一位早期职业科学家研究人类和机器如何理解非字面语言。人类交流的很多内容并不是直接用文字来表达的：我们可以说‘时间不早了’来礼貌地表示我们想要离开，或者叫芭蕾舞演员‘天鹅’来捕捉她的优雅。这种语言被称为语用学，通常被认为包含了从幽默到善意的谎言、隐喻、含蓄、韵律等等的一切。虽然已经对这些现象进行了单独的研究，但尚不清楚它们是否得到了相同的机制的支持，也不知道它们在个体内部是如何联系的，这对面临沟通挑战的神经分化个体有影响。而且，尽管目前的语言模型显示了令人印象深刻的结果，但很少有研究探索什么样的计算可能支持语用语言理解，而语用语言理解是人工智能成功的关键。在行为和计算方法上，当前的项目将通过识别人类中相关的语用推理簇并探索它们如何在模型中计算来解决这些限制。这个项目应用新的方法来阐明语用语言的统一框架，目标是将神经分化的个体作为研究人员纳入这一过程。大规模的个体差异研究将开发一套全面的语用语言，并揭示能力之间的关系(例如，理解善意的谎言是否与理解讽刺最相关？)非语言认知评估将确定哪些类别与社会推理、字面语言理解和执行能力有关。通过评估非字面语言解释的现有语言模型，该项目还揭示了语言模型在语用语言方面的可学性、范围和概括性，并将使我们能够将模型活动与人类研究中发现的集群进行比较。通过在人类和当前的计算模型中测试语用语言，这项研究将使我们更接近于理解语用语言理解所需的输入和计算。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Edward Gibson and Dr. Evelina Fedorenko, this postdoctoral fellowship award supports an early career scientist examining how non-literal language is understood by humans and machines. Much of human communication is not encoded directly in words: we may say ‘It’s getting late’ to politely indicate that we would like to leave, or call a ballerina a ‘swan’ to capture her grace. This kind of language is called pragmatics and often thought to comprise everything from humor, to white lies, metaphors, implicature, prosody, and more. Although there have been investigations into each of these phenomena individually, it is unknown whether they are supported by the same mechanisms, nor how they relate within the individual, which has implications for neurodivergent individuals facing challenges in communication. And, while current language models show impressive results, little research has explored what kinds of computations might support pragmatic language understanding, which is crucial for success in artificial intelligence. Across both behavioral and computational approaches, the current project will address these limitations by identifying clusters of related pragmatic inferences in humans and exploring how they are computed in models. This project applies new methods to shed light on a unified framework for pragmatic language, with the goal of including neurodivergent individuals as researchers in the process. Large-scale individual-differences studies will develop a comprehensive battery of pragmatic language and expose relationships between abilities (e.g., does understanding white lies correlate best with understanding of irony?). Non-linguistic cognitive assessments will identify which clusters relate to social reasoning, literal language understanding, and executive abilities. By evaluating current language models on non-literal language interpretation, the project with also uncover the learnability, scope, and generalization of language models’ performance on pragmatic language, and will allow us to compare model activity to the clusters found in the human studies. By testing pragmatic language in humans and current computational models, this research will bring us closer to understanding the necessary input and computations for pragmatic language comprehension.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.