Technical Program

Paper Detail

Paper: PS-1A.12
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: A Multi-Level Reinforcement-Learning Model of Wisconsin Card Sorting Test Performance
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Alexander Steinke, Hannover Medical School, Germany; Florian Lange, KU Leuven, Belgium; Bruno Kopp, Hannover Medical School, Germany
Abstract: The Wisconsin Card Sorting Test (WCST) is considered to be gold standard for the clinical assessment of executive functions. However, little is known about cognitive processes corresponding to WCST performance. Recent research suggests that multiple levels of control contribute to WCST performance. In this study, we introduce a reinforcement-learning (RL) model, which incorporates category and response learning. We test this multi-level RL model against single-level models, i.e., a category RL model and the state-of-the-art attentional updating model, by means of relative and absolute model performance. A sample of 375 participants completed a computerized version of the WCST (cWCST). Behavioral outcome measures were traditional perseveration and set-loss errors that we further stratified by response demands. The multi-level RL model outperformed both single-level models, with the state-of-the-art attentional updating model performing worst. Only the multi-level RL model was able to simulate all behavioral phenomena under consideration. In conclusion, results of model comparisons support the hypothesis that control processes at multiple levels contribute to cWCST performance. The multi-level RL model might offer a suitable framework for discerning latent cognitive processes contributing to WCST performance in general.