DocumentCode
240557
Title
An arousal-based neural model of infant attachment
Author
Cittern, David ; Edalat, Abbas
Author_Institution
Imperial Coll. London, London, UK
fYear
2014
fDate
9-12 Dec. 2014
Firstpage
57
Lastpage
64
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2014 IEEE Symposium on
Conference_Location
Orlando, FL
Type
conf
DOI
10.1109/CCMB.2014.7020694
Filename
7020694
Link To Document