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 proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish (US)
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3194-3197
Number of pages4
ISBN (Electronic)9781509028092
DOIs
StatePublished - Sep 13 2017
Externally publishedYes
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: Jul 11 2017Jul 15 2017

Other

Other39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
CountryKorea, Republic of
CityJeju Island
Period7/11/177/15/17

Fingerprint

Variational techniques
Decision Making
Decision making
Learning
Primates
Research

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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). [8037536] 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; Dougherty, Darin D.; Eskandar, Emad N.; Widge, Alik S.; Eden, Uri T.

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.

Research output: Chapter in Book/Report/Conference proceedingConference 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, 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 https://doi.org/10.1109/EMBC.2017.8037536
Yousefi, Ali ; Kakooee, Reza ; Beheshti, Mohammad Th ; Dougherty, Darin D. ; Eskandar, Emad N. ; Widge, Alik S. ; Eden, Uri T. / 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
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