Machine learning used to successfully measure attachment in children

For the first time, researchers have used machine learning to successfully measure attachment in children - the vital human bond that humans first develop as infants to their caregivers. In new multi-disciplinary research, led by the University of Glasgow and published in PLOS ONE, the study team present a quick and easy way to measure attachment through a computer game, that has the potential to be used in largescale public health monitoring. Attachment is a term used to describe the lasting emotional bond and feeling of connectedness between human beings. The attachment between a young child and their primary caregiver is known to be vitally important for emotional development - disruption to or loss of this bond can affect a child emotionally and psychologically into adulthood, impacting future relationships. Insecure attachment in children is not necessarily abnormal, and is often an emotional adaptation to less than optimal environmental circumstances. However, insecure attachment is associated with increased risk of psychopathology of various types. As a result, there may be potential for SAM, in the future, to form part of an early school-based screening programme to identify children at risk of psychopathology.