NDLI: Characterization of human use of ethanol based on video games with ethanol rewards: Model, system identification and statistical performance

Content Provider	IEEE Xplore Digital Library
Author	Ozil, I. Plawecki, M.H. Doerschuk, P.C. O'Connor, S.J.
Copyright Year	2011
Description	Author affiliation: Biomedical Engineering and Electrical and Computer Engineering, Cornell University, USA (Doerschuk, P.C.) \|\| Electrical and Computer Engineering, Cornell University, USA (Ozil, I.) \|\| Department of Psychiatry, Indiana University School of Medicine, USA (Plawecki, M.H.; O'Connor, S.J.)
Abstract	Recent research on alcoholism explores the influence of family history and genetics on the risk of developing abuse or dependence. Endophenotypes and behavioral paradigms have been used to help detect genetic contributions to this disease. Electronic tasks, which can be considered video games, that provide alcohol as a reward in controlled environments have been developed to explore some of the behaviors and characteristics of individuals with or at risk for alcohol substance use disorders. One such game involves a progressive work paradigm where subjects receive larger or smaller alcohol rewards for completing the task. A generative model for this game is described along with the signal processing needed to characterize the subjects' behavior by system identification. Statistical performance of the algorithm is described and evaluated for the human data. Potential meanings of the different parameter values and the performance results are described.
Starting Page	1107
Ending Page	1111
File Size	115518
Page Count	5
File Format	PDF
ISBN	9781467303217
ISSN	10586393
e-ISBN	9781467303231
DOI	10.1109/ACSSC.2011.6190185
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Games Maximum likelihood estimation Humans Mathematical model Approximation methods Presses Educational institutions
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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