Title :
An arousal-based neural model of infant attachment
Author :
Cittern, David ; Edalat, Abbas
Author_Institution :
Imperial Coll. London, London, UK
Abstract :
We develop an arousal-based neural model of infant attachment using a deep learning architecture. We show how our model can differentiate between attachment classifications during strange situation-like separation and reunion episodes, in terms of both signalling behaviour and patterns of autonomic arousal, according to the sensitivity of previous interaction.
Keywords :
learning (artificial intelligence); neurophysiology; paediatrics; arousal-based neural model; attachment classification; autonomic arousal pattern; deep learning architecture; infant attachment; interaction sensitivity; reunion episode; separation episode; signalling behaviour; Adaptation models; Artificial intelligence; Brain modeling; Delays; Robots; Security; Sensitivity;
Conference_Titel :
Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CCMB.2014.7020694
