The Multimodal Assessment of Adult Attachment Security: Developing the Biometric Attachment Test (bibtex)
by Federico Parra, Raphaële Miljkovitch, Gwenaelle Persiaux, Michelle Morales, Stefan Scherer
Abstract:
Background: Attachment theory has been proven essential for mental health, including psychopathology, development, and interpersonal relationships. Validated psychometric instruments to measure attachment abound but suffer from shortcomings common to traditional psychometrics. Recent developments in multimodal fusion and machine learning pave the way for new automated and objective psychometric instruments for adult attachment that combine psychophysiological, linguistic, and behavioral analyses in the assessment of the construct.
Reference:
The Multimodal Assessment of Adult Attachment Security: Developing the Biometric Attachment Test (Federico Parra, Raphaële Miljkovitch, Gwenaelle Persiaux, Michelle Morales, Stefan Scherer), In Journal of Medical Internet Research, volume 19, 2017.
Bibtex Entry:
@article{parra_multimodal_2017,
	title = {The {Multimodal} {Assessment} of {Adult} {Attachment} {Security}: {Developing} the {Biometric} {Attachment} {Test}},
	volume = {19},
	issn = {1438-8871},
	url = {http://www.jmir.org/2017/4/e100/},
	doi = {10.2196/jmir.6898},
	abstract = {Background: Attachment theory has been proven essential for mental health, including psychopathology, development, and interpersonal relationships. Validated psychometric instruments to measure attachment abound but suffer from shortcomings common to traditional psychometrics. Recent developments in multimodal fusion and machine learning pave the way for new automated and objective psychometric instruments for adult attachment that combine psychophysiological, linguistic, and behavioral analyses in the assessment of the construct.},
	number = {4},
	journal = {Journal of Medical Internet Research},
	author = {Parra, Federico and Miljkovitch, Raphaële and Persiaux, Gwenaelle and Morales, Michelle and Scherer, Stefan},
	month = mar,
	year = {2017},
	keywords = {UARC, Virtual Humans},
	pages = {e100}
}
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