| Paper: | PS-1B.4 |
| Session: | Poster Session 1B |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Predicting memory performance using a joint model of brain and behavior |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1194-0 |
| Authors: |
David Halpern, Shannon Tubridy, Lila Davachi, Todd Gureckis, New York University, United States |
| Abstract: |
Understanding the links between brain and behavior is a central goal of computational cognitive neuroscience. We present a framework for simultaneous modeling of behavioral and neuroimaging data in the context of human memory acquisition and forgetting. Using a Hidden Markov Model of memory that can account for both behavioral and functional magnetic resonance imaging (fMRI) observations, we show that we can predict memory performance in held-out data at a level well-above chance and that we can surpass the predictions made by fMRI data alone as well as those made by variants of established behavioral models. This work highlights a path for better understanding the relationship between neural data and latent cognitive processes and advances a model of memory whose predictive ability could enable model-augmented learning environments. |
