SAM (Automated Attachment Analysis Using the School Attachment Monitor) is a three-year project funded by the EPSRC, starting in September 2015. We are working alongside the School of Mental Health & Wellbeing at University of Glasgow and Alessandro Vinciarelli in the Social Signal Processing group within our department.
Project website: http://www.attachment-monitor.org.uk/
Our aim in SAM is to develop a computer-based tool which can measure parent-child Attachment across the population in a cost-effective way. The National Children’s Bureau states that “secure attachment promotes health and wellbeing” while the Early Childhood Forum advocates “the right of children to […] form secure, long lasting attachment relationships […] which shape their future capacities for wellbeing”. When the problem is neglected, the consequences are dire: children who have abnormal family attachments are at much higher risk of aggressive behaviours. By early adulthood, individuals with aggressive behaviour cost society 10 times more than their peers and have a mortality rate almost 10 times higher, in part due to increased risk of suicide and violent behaviour, but also due to physical problems such as coronary heart pathologies. Identifying Attachment problems early, at a population level, would be of significant benefit to society and drastically reduce the costs of dealing with the resulting issues.
Large-scale screenings of Attachment insecurity should be routine among children. The problem is that Attachment assessment methods are expensive and time-consuming. MCAST (Manchester Child Attachment Story Task) is the standard method used in middle childhood. During MCAST administration, assessors show vignettes to the child, using a dolls-house, which portray mildly stressful situations. They are then asked to act out what happens in the rest of the story using dolls that represent both the child and a caregiver. The way the child completes the story and their behaviour during the test provides the cues necessary to assess their Attachment status. Each MCAST takes 30 minutes to administer and a further two hours to be transformed into a usable medical record. Furthermore, professionals must attend expensive courses followed by lengthy reliability training to use MCAST, so accredited Attachment assessors are a rare commodity. This means that MCAST cannot be applied on a large scale, as needed to make a significant impact on population health and wellbeing.
We will develop a computer-based tool which can be used to measure Attachment across the population in a rapid, cost-effective way to support MCAST assessors. The children will be guided through the story vignettes by an on-screen avatar. The detailed movements and positions of the dolls in space will be captured in real time. Using these data, we will develop novel algorithms to categorise Attachment patterns automatically and rapidly, locating each child in one of the four Attachment categories (Secure; Insecure Resistant-Ambivalent; Insecure Avoidant and Insecure Disorganised/Disorientated) with a level of confidence. To do this, we will develop novel techniques based on Social Signal Processing.
With SAM, the screening sessions and preliminary data analysis can be done without the presence of trained MCAST assessors; they would only be needed if a child was tagged as being in one of the problem categories, where a standard MCAST assessment would be undertaken, allowing large-scale population screening of Attachment patterns for the first time. The development of SAM and the rapid screening of Attachment in large groups will create a paradigm shift in the treatment of child psychiatric disorders.