Advancing the study of emotional development through novel modeling and coding approaches

通过新颖的建模和编码方法推进情感发展的研究

• 批准号: RGPIN-2022-03517
• 负责人: Hayden, Elizabeth
• 金额: $ 2.84万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764038
• 关键词: Advancing; study; emotional; development; through

Human emotion serves the key functions of motivating adaptive behaviour and facilitating communication with others, thereby shaping individuals' environments to achieve desirable goals. However, scientific progress has been stymied by the lack of consensus regarding the conceptualization and measurement of key emotion constructs in childhood and adolescence. While observable emotion expressions (e.g., facial affect) are used in the study of human emotion and are especially useful for the study of childhood emotion, these methods are expensive and time-consuming to implement. The three programmatic studies proposed here build upon my past NSERC-funded work to inform the nature and optimal assessment of early emotion through the use of multiple methods and new technologies. In Study 1, I will use novel data analytic approaches (e.g., growth modeling) and machine learning to develop new models of children's emotion. I have access to video-recorded data collected from several large samples of children, ranging in age from 3-11, who previously participated in observational laboratory tasks. In this study, I will capitalize on these rich datasets to examine the utility of 1) novel but theory-driven emotion modeling approaches and 2) machine learning approaches to modeling emotion data. In Study 2, I will draw upon more recently developed automated technologies to code existing datasets to derive new indices of children's emotion, comparing these to indices based on traditional coding methods. Extant indices of natural language relevant to emotion rely on specific modalities (e.g., written language) and have been primarily developed using information derived from adults' social media usage. Whether these models yield valid data when applied to youths' spoken language is unclear, although deriving valid emotion indices from children's natural language would be of tremendous benefit by reducing the bias and expense of extant methods of assessing children's emotion.In Study 3, I will collect data from a new sample of children to further validate the emotion parameters and coding methods derived from Studies 1 & 2 by linking them to psychophysiological and neural imaging data.

人类的情感是激励适应性行为并促进与他人进行交流的关键功能，从而塑造了个人的环境以实现理想的目标。然而，由于缺乏关于童年和青春期关键情感结构的概念化和衡量的共识，科学进步受到了困扰。尽管可观察到的情绪表情（例如，面部影响）用于人类情感的研究，对于研究儿童情绪的研究特别有用，但这些方法既昂贵又耗时。这里提出的三项程序研究基于我过去由NSERC资助的工作，以通过使用多种方法和新技术来为早期情绪的性质和最佳评估提供信息。在研究1中，我将使用新颖的数据分析方法（例如增长建模）和机器学习来开发儿童情绪的新模型。我可以访问从几个大型儿童样本中收集的视频记录数据，年龄在3-11岁之间，他们以前参加过观察性实验室任务。在这项研究中，我将利用这些丰富的数据集，以检查1）新颖但以理论驱动的情感建模方法和2）机器学习方法来建模情绪数据的实用性。在研究2中，我将利用最近开发的自动化技术来编码现有数据集，以获取儿童情绪的新指数，并将这些索引与基于传统编码方法的索引进行比较。与情感相关的自然语言的现有索引取决于特定的方式（例如，书面语言），主要是使用成人社交媒体使用的信息开发的。这些模型应用于年轻人的口语时是否产生有效的数据尚不清楚，尽管从儿童的自然语言中得出有效的情感指标将通过减少评估儿童情绪的现有方法的偏见和费用，从而获得巨大的好处。在评估儿童的情绪3中，我将从儿童的新样本中收集一个新样本，以进一步验证情感参数和编码方法，并从研究中验证他们的数据和2链接，以链接到1＆2链接1＆2和2链接到1＆2。