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 | |
| 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 |
Other
| Other | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 7/11/17 → 7/15/17 |
Fingerprint
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
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 proceeding › Conference contribution
}
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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85032205650&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032205650&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8037536
DO - 10.1109/EMBC.2017.8037536
M3 - Conference contribution
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.
ER -