Systematic Representative Design and Clinical Virtual Reality (bibtex)
by Mozgai, Sharon, Hartholt, Arno and Rizzo, Albert “Skip”
Abstract:
The authors of the article, “Causal Inference in Generalizable Environments: Systematic Representative Design”, boldly announce their core point in the opening line of the abstract stating that, “Causal inference and generalizability both matter.” While a surface glance might suggest this to be a simple notion, a closer examination reveals the complexity of what they are proposing. This complexity is apparent when one considers that the bulk of human experimental research has always been challenged in its inability to concurrently deliver on both of these aims. This is no slight on the tens of 1000’s of human researchers and behavioral scientists who have devoted long careers to highly controlled human psychological and social science laboratory research. Rather, it reflects the sheer enormity of the challenges for conducting human studies designed to specify human function with physics-informed lab methods, while at the same time producing results that lead to enhanced understanding and prediction of how people will operate in the complex and ever-changing contexts that make up everyday life. At the core of this issue is a methodological and philosophical challenge that is relevant to all areas of human subjects’ research, beyond the social science focus of the Miller et al. (this issue) article. It is our aim to discuss the central topics in their article through the lens of our own work using Virtual/Augmented Reality and Virtual Human simulation technologies for clinical and training applications
Reference:
Systematic Representative Design and Clinical Virtual Reality (Mozgai, Sharon, Hartholt, Arno and Rizzo, Albert “Skip”), In Psychological Inquiry, volume 30, 2019.
Bibtex Entry:
@article{mozgai_systematic_2019,
	title = {Systematic {Representative} {Design} and {Clinical} {Virtual} {Reality}},
	volume = {30},
	issn = {1047-840X, 1532-7965},
	url = {https://www.tandfonline.com/doi/full/10.1080/1047840X.2019.1693873},
	doi = {10.1080/1047840X.2019.1693873},
	abstract = {The authors of the article, “Causal Inference in Generalizable Environments: Systematic Representative Design”, boldly announce their core point in the opening line of the abstract stating that, “Causal inference and generalizability both matter.” While a surface glance might suggest this to be a simple notion, a closer examination reveals the complexity of what they are proposing. This complexity is apparent when one considers that the bulk of human experimental research has always been challenged in its inability to concurrently deliver on both of these aims. This is no slight on the tens of 1000’s of human researchers and behavioral scientists who have devoted long careers to highly controlled human psychological and social science laboratory research. Rather, it reflects the sheer enormity of the challenges for conducting human studies designed to specify human function with physics-informed lab methods, while at the same time producing results that lead to enhanced understanding and prediction of how people will operate in the complex and ever-changing contexts that make up everyday life. At the core of this issue is a methodological and philosophical challenge that is relevant to all areas of human subjects’ research, beyond the social science focus of the Miller et al. (this issue) article. It is our aim to discuss the central topics in their article through the lens of our own work using Virtual/Augmented Reality and Virtual Human simulation technologies for clinical and training applications},
	number = {4},
	journal = {Psychological Inquiry},
	author = {Mozgai, Sharon and Hartholt, Arno and Rizzo, Albert “Skip”},
	month = oct,
	year = {2019},
	keywords = {MedVR, UARC, Virtual Humans},
	pages = {231--245}
}
Powered by bibtexbrowser