The School Attachment Monitor - a novel computational tool for assessment of attachment in middle childhood

Abstract

BackgroundAttachment research has been limited by the lack of quick and easy measures. We report development and validation of the School Attachment Monitor (SAM), a novel measure for largescale assessment of attachment in children aged 5-9, in the general population. SAM offers automatic presentation, on computer, of story-stems based on the Manchester Child Attachment Story Task (MCAST), without the need for trained administrators. SAM is delivered by novel software which interacts with child participants, starting with warm-up activities to familiarise them with the task. Children’s story completion is video recorded and augmented by ‘smart dolls’ that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users’ age range to establish their task understanding and incorporate their innovative ideas for improving SAM software.Methods130 5-9 year old children were recruited from mainstream primary schools. In Phase 1, sixty-one children completed both SAM and MCAST. Inter-rater reliability and rating concordance was compared between SAM and MCAST. In Phase 2, a further 44 children completed SAM complete and, including those children completing SAM in Phase 1 (total n=105), a machine learning algorithm was developed using a “majority vote” procedure where, for each child, 500 non-overlapping video frames contribute to the decision.ResultsUsing manual rating, SAM-MCAST concordance was excellent (89% secure versus insecure; 97% organised versus disorganised; 86% four-way). Comparison of human ratings of SAM versus the machine learning algorithm showed over 80% concordance.ConclusionsWe have developed a new tool for measuring attachment at the population level, which has good reliability compared to a gold-standard attachment measure and has the potential for automatic rating – opening the door to measurement of attachment in large populations.

Using video calls to study children's conversational development: The case of backchannel signaling

“…While much of previous work has focused on verbal or vocal signals (e.g., Snow, 1977;Warlaumont et al, 2014;Hazan et al, 2017;Clark, 2018), a comprehensive study of conversational skills requires that we also investigate how children learn to coordinate with the interlocutor using visual signals. It is worth mentioning that some previous studies have proposed procedures to collect and analyze videos of school-age children (e.g., Roffo et al, 2019;Vo et al, 2020;Rooksby et al, 2021), however, these studies do not provide a method to collect face-toface conversational data. Rather, they focus on clinical tasks such as the Manchester Child Attachment Story Task.…”

“…Thus, we settled on a manageable sample size. That said, progress in the automatic annotation of children's multimodal data (Sagae et al, 2007;Nikolaus et al, 2021;Rooksby et al, 2021;Erel et al, 2022;Long et al, 2022) should alleviate the constraint on large-scale data collection in future research. The current work also contributes to this effort by providing substantial hand-annotated data that can be used for the automatic models' training and/or validation.…”