Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique

Ali Yousefi; Reza Kakooee; Mohammad Th Beheshti; Darin D. Dougherty; Emad N. Eskandar; Alik S. Widge; Uri T. Eden

doi:10.1109/EMBC.2017.8037536

Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique

Ali Yousefi, Reza Kakooee, Mohammad Th Beheshti, Darin D. Dougherty, Emad N. Eskandar, Alik S. Widge, Uri T. Eden

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3194-3197
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037536
State	Published - Sep 13 2017
Externally published	Yes
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: Jul 11 2017 → Jul 15 2017

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Other

Other	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	7/11/17 → 7/15/17

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2017.8037536

Cite this

Yousefi, A., Kakooee, R., Beheshti, M. T., Dougherty, D. D., Eskandar, E. N., Widge, A. S., & Eden, U. T. (2017). Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 3194-3197). Article 8037536 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8037536

Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique. / Yousefi, Ali; Kakooee, Reza; Beheshti, Mohammad Th et al.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3194-3197 8037536 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yousefi, A, Kakooee, R, Beheshti, MT, Dougherty, DD, Eskandar, EN, Widge, AS & Eden, UT 2017, Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8037536, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 3194-3197, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 7/11/17. https://doi.org/10.1109/EMBC.2017.8037536

Yousefi A, Kakooee R, Beheshti MT, Dougherty DD, Eskandar EN, Widge AS et al. Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3194-3197. 8037536. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2017.8037536

Yousefi, Ali ; Kakooee, Reza ; Beheshti, Mohammad Th et al. / Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3194-3197 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "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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Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique

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