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

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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.