TY - GEN
T1 - Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique
AU - Yousefi, Ali
AU - Kakooee, Reza
AU - Beheshti, Mohammad Th
AU - Dougherty, Darin D.
AU - Eskandar, Emad N.
AU - Widge, Alik S.
AU - Eden, Uri T.
N1 - Funding Information:
*This research was funded [in part] by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0045,issued bytheArmyResearch Officecontracting office in support of DARPA'S SUBNETS program. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. AY, and UTE are with the Department of Mathematics and Statistics of Boston University, Boston, MA AY, DDD, ENS, and ASW are with Harvard Medical School and Massachusetts General Hospital, Boston, MA RK, and MTHB are with the Department of Electrical and Computer Engineering, Tarbiat ModaresUniversity, Tehran, Iran AY andRKcontributedequallyto this research. UTE and ASW are the co-senior authors and contributed equally to the manuscript. Address for reprint requests and other correspondence: Ali Yousefi, Email: ayousefi@mgh.harvard.edu
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Multiple-Choice Decision-Making Tasks are widely used to analyze behavior and infer underlying cognitive states that shape the decision and learning processes. The behavioral signals recorded in these tasks are dynamic and often non-Gaussian -for instance, when learning a multiple choice association task. Previously developed estimation algorithms for latent behavioral variables do not address multiple-choice responses. In this research, we use a state-space modeling framework to predict a cognitive learning state related to multiple choice decisions, which are best described by a multinomial distribution. The proposed algorithm combines a multinomial filter/smoother and a variational Bayes technique to estimate the dynamics of a learning state vector. The algorithm is applied to decision response data recorded from non-human primates (NHPs) performing a Multiple-Choice Decision Task.
AB - Multiple-Choice Decision-Making Tasks are widely used to analyze behavior and infer underlying cognitive states that shape the decision and learning processes. The behavioral signals recorded in these tasks are dynamic and often non-Gaussian -for instance, when learning a multiple choice association task. Previously developed estimation algorithms for latent behavioral variables do not address multiple-choice responses. In this research, we use a state-space modeling framework to predict a cognitive learning state related to multiple choice decisions, which are best described by a multinomial distribution. The proposed algorithm combines a multinomial filter/smoother and a variational Bayes technique to estimate the dynamics of a learning state vector. The algorithm is applied to decision response data recorded from non-human primates (NHPs) performing a Multiple-Choice Decision Task.
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U2 - 10.1109/EMBC.2017.8037536
DO - 10.1109/EMBC.2017.8037536
M3 - Conference contribution
C2 - 29060577
AN - SCOPUS:85032205650
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3194
EP - 3197
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -