| Paper: | PS-2B.39 |
| Session: | Poster Session 2B |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Modeling Visual Working Memory in Schizophrenia |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1076-0 |
| Authors: |
Yijie Zhao, Xuemei Ran, Li Zhang, East China Normal University, China; Ruyuan Zhang, University of Minnesota, United States; Yixuan Ku, East China Normal University, China |
| Abstract: |
It has been well documented that people with schizophrenia (PSZ) have deficits in visual working memory (VWM). One widely acknowledged explanation is that PSZ has decreased working memory capacity compared to healthy control subjects (HCS). Here, we leveraged the state-of-the-art computational framework – the variable precision (VP) framework to disentangle the contributions of different VWM components to the atypical behavior observed in PSZ. Using a classical delay-estimation VWM task, we found that neither the memory resources across different set size levels nor the variability at the choice stage were the differences between two groups (PSZ vs. HCS). Interestingly, PSZ exhibited abnormally larger variability in allocating memory resources across items and trials. Our findings challenged the classical “limited capacity” account in PSZ and showed that larger resource allocation variability was the major determinant of the VWM deficits in PSZ, which could only be detected by the VP framework. |
