Learning Multimodal Cues of Children's Uncertainty

TL;DR

This paper introduces a new multimodal dataset and machine learning model to detect children's nonverbal cues of uncertainty, advancing understanding of cognitive coordination in human-AI interactions.

Contribution

It provides the first annotated dataset of children's nonverbal uncertainty cues and a multimodal model that predicts uncertainty from video, improving upon existing baselines.

Findings

The dataset reveals different roles of uncertainty in task performance.

The multimodal model outperforms baseline transformer models.

Insights into nonverbal cues of uncertainty in children.

Abstract

Understanding uncertainty plays a critical role in achieving common ground (Clark et al.,1983). This is especially important for multimodal AI systems that collaborate with users to solve a problem or guide the user through a challenging concept. In this work, for the first time, we present a dataset annotated in collaboration with developmental and cognitive psychologists for the purpose of studying nonverbal cues of uncertainty. We then present an analysis of the data, studying different roles of uncertainty and its relationship with task difficulty and performance. Lastly, we present a multimodal machine learning model that can predict uncertainty given a real-time video clip of a participant, which we find improves upon a baseline multimodal transformer model. This work informs research on cognitive coordination between human-human and human-AI and has broad implications for gesture understanding and generation. The anonymized version of our data and code will be publicly available upon the completion of the required consent forms and data sheets.