%0 Conference Paper %1 chetitah2023gamification %A Chetitah, Mounsif %A Müller, Julian %A Deserno, Lorenz %A Walttman, Maria %A von Mammen, Sebastian %D 2023 %K %T Gamification Framework for Reinforcement Learning-based Neuropsychology Experiments %X Reinforcement learning (RL) is an adaptive process where an agent relies on its experience to improve the outcome of its performance. It learns by taking actions to maximize its rewards, and by minimizing the gap between predicted and received rewards. In experimental neuropsychology, RL algorithms are used as a conceptual basis to account for several aspects of human motivation and cognition. A number of neuropsychological experiments, such as reversal learning, sequential ecision-making, and go-no-go tasks, are required to validate the decisive RL algorithms. The experiments are conducted in digital environments and are comprised of numerous trials that lead to participants’ frustration and fatigue. This paper presents a gamification framework for reinforcement-based neuropsychology experiments that aims to increase participant engagement and provide them with appropriate testing environments.

@inproceedings{chetitah2023gamification, abstract = {Reinforcement learning (RL) is an adaptive process where an agent relies on its experience to improve the outcome of its performance. It learns by taking actions to maximize its rewards, and by minimizing the gap between predicted and received rewards. In experimental neuropsychology, RL algorithms are used as a conceptual basis to account for several aspects of human motivation and cognition. A number of neuropsychological experiments, such as reversal learning, sequential ecision-making, and go-no-go tasks, are required to validate the decisive RL algorithms. The experiments are conducted in digital environments and are comprised of numerous trials that lead to participants’ frustration and fatigue. This paper presents a gamification framework for reinforcement-based neuropsychology experiments that aims to increase participant engagement and provide them with appropriate testing environments.}, added-at = {2023-05-31T17:29:00.000+0200}, author = {Chetitah, Mounsif and Müller, Julian and Deserno, Lorenz and Walttman, Maria and von Mammen, Sebastian}, biburl = {https://www.bibsonomy.org/bibtex/2804f903135cda9b8c5a5c1698e985618/m.chetitah}, interhash = {080927e6da5b9d930ef458d48b96ac24}, intrahash = {804f903135cda9b8c5a5c1698e985618}, keywords = {}, timestamp = {2023-05-31T17:29:00.000+0200}, title = {Gamification Framework for Reinforcement Learning-based Neuropsychology Experiments}, year = 2023 }