CAREER: Modeling Mental Representation in Judgment

机器学习的最新进展，加上大型自然语言在线数据集的可用性增加，为理解人类行为开辟了新的机会。有了这些新方法，现在就有可能观察人们读了什么、说了什么，从而对各种各样的普通物体和事件进行思考和感受。该项目的目标是研究如何将这些新方法和数据集与现有的心理学理论相结合，以预测和理解人类的判断，主要应用于风险感知领域。作为该项目的一部分，提出的计算技术允许对自然风险的感知进行自动、大规模的分析，因此可用于识别问题并制定涉及风险沟通和风险管理的干预措施。更一般地说，通过将数据科学的前沿方法应用于人类行为的研究，该项目开发了预测和理解态度、信念和偏好的新技术。通过这样做，它促进了一系列广泛的政策和商业应用，使用行为科学中现有的经验方法是不可行的。我们如何揭示和量化作为日常判断目标的所有对象和概念的丰富的心理表征？我们如何使用这些丰富的表征来预测判断和判断错误，研究判断的领域和个人层面的差异，并表征判断背后的复杂关联网络？最后，我们如何研究不同文化和时期的心理表征和判断的差异？这个项目将使用“语义向量”——从大规模语言数据中获得的单词的高维表示——来解决这些问题。语义向量为人们头脑中的知识结构和关联提供了一个很好的代理，并且可以与各种机器学习算法相结合，以预测对自然对象和概念的判断，例如风险来源。这些向量还可以识别判断的关键关联，促进对各种心理维度（包括情感、道德概念和个性）的定向假设检验。最后，当在不同类型的语言数据集上训练时，语义向量可以揭示判断与文化、历史和社会之间复杂的相互作用。请注意，语义向量可以以这种方式用于几乎任何涉及公共对象、个人和事件的判断领域。因此，该项目的一个关键目标是评估语义向量的适用性，以预测和理解人们在日常基础上做出的许多不同类型的判断（不仅包括风险判断，还包括健康判断和消费者判断）。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。

Recent advances in machine learning, combined with the increased availability of large natural language online datasets, have opened up new opportunities for understanding human behavior. With these new methods, it is now possible to observe what people read and talk about, and thus think and feel about, a wide range of common objects and events. The goal of this project is to study how these novel methods and datasets can be combined with existing psychological theory to predict and understand human judgment, with the primary application to the domain of risk perception. The computational techniques proposed as part of this project allow for the automatic, large-scale analysis of the perception of naturalistic risks, and thus can be used to identify problems and develop interventions involving risk communication and risk management. More generally, by applying cutting edge methods in data science to the study of human behavior, this project develops novel technologies for predicting and understanding attitudes, beliefs, and preferences. By doing so, it facilitates a wide array of policy and commercial applications not feasible using existing empirical methodologies in the behavioral sciences.How can we uncover and quantify rich mental representations for all of the objects and concepts that are the target of everyday judgment? How can we use these rich representations to predict judgments and judgment errors, to study domain and individual-level differences in judgment, and to characterize the complex web of associations that underlie judgment? Finally, how can we study differences in mental representations and judgments across cultures and time periods? This project will use "semantic vectors" -- high dimensional representations for words obtained from large-scale language data -- to address these questions. Semantic vectors provide a good proxy for the structure of knowledge and association in people's minds and can be combined with various machine learning algorithms to predict judgments for naturalistic objects and concepts, such as sources of risk. These vectors can also identify the key associates of judgments, facilitating directed hypothesis tests for a diverse array of psychological dimensions, including emotions, moral concepts, and personality. Finally, when trained on different types of language datasets, semantic vectors can shed light on the complex interaction between judgment and culture, history, and society. Note that semantic vectors can be used in this manner for nearly any judgment domain that involves common objects, individuals, and events. Thus one key goal of this project is to evaluate the applicability of semantic vectors for predicting and understanding the many different types of judgments that that people make on a day-to-day basis (including not only risk judgment, but also, for example, health judgment and consumer judgment).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.