Automatic text simplification measures correlate poorly with human judgments and are disconnected from psycholinguistic research on comprehension. We propose a multidimensional analysis framework grounded in Cognitive Load Theory and the Construction-Integration model, organizing over 400 linguistic features into six simplicity dimensions targeting distinct comprehension stages. Using this framework, we analyze four human-annotated and six aligned simplification datasets. We find that (1) our dimensions predict human simplicity judgments better than established measures on most datasets; (2) current datasets overwhelmingly target lexical and syntactic dimensions while under-targeting personalization the dimension psychological research identifies as most impactful for comprehension; and (3) existing measures overall do not capture any specific dimension. These findings provide actionable guidance for building measures and datasets better aligned with how readers actually process text.

Recent technologies enable the exploitation of full body expressions in applications such as interactive arts but are still limited in terms of dyadic subtle interaction patterns. Our project aims at full body expressive interactions between a user and an autonomous virtual agent. The currently available databases do not contain full body expressivity and interaction patterns via avatars. In this paper, we describe a protocol defined to collect a database to study expressive full-body dyadic interactions. We detail the coding scheme for manually annotating the collected videos. Reliability measures for global annotations of expressivity and interaction are also provided.