Browsing by Subject "150"
Now showing 1 - 13 of 13
- Results Per Page
- Sort Options
Research Data Open Access A Bayesian Model for Chronic Pain.Eckert, Anna-Lena; Endres, Dominik; Pabst, KathrinResearch Data Open Access A collection of identities for variational inference with exponential-family modelsEndres, Dominik; Pabst, Kathrin; Eckert, Anna-Lena; Schween, RaphaelResearch Data Open Access Competition between salience and informational value for saccade adaptationWolf, Christian; Wagner, Ilja; Schütz, Alexander C.Research Data Open Access Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model- Springer2024(Philipps-Universität marburg, 07.05.2021) Endres, Dominik; Meibodi, Neda; Meibodi, NedaThis repository contains the files and data necessary to recreate the results from the paper Meibodi, N., Abbasi, H., Schubö, A. et al. Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model. Comput Brain Behav 7, 268–285 (2024). https://doi.org/10.1007/s42113-024-00197-6 Please go to Version 2 if you are interested to see the files related to the other pubplication N.Meibodi, H.Abbasi, A. Schuboe, D. Endres (2021) A Model of Selection History in Visual Attention, Proceedings of the 2021 Conference of the Society of Cognitive Science, Vienna, Austria.Research Data Open Access Electrophysiological correlates of attentional capture in joint actionAbbasi, Hossein; Schubö, Anna; Dötsch, DominikResearch Data Open Access Research Data Open Access Humans trust central vision more than peripheral vision even in the darkSchütz, Alexander C.; Gloriani, Alejandro H.Research Data Open Access Implausible Priors in Bayesian Models of Body Ownership(Moritz Schubert, 2021-05-28) Schubert, Moritz; Endres, DominikResearch Data Open Access Modeling aberrant volatility estimates in Autism Spectrum Disorder(AE Theoretische Kognitionswissenschaft) Niehaus, Haukem; Kamp-Becker, Inge; Endres, Dominik; Stroth, SannaResearch Data Open Access Modeling Reward Learning Under Placebo Expectancies: A Q-Learning Approach(2022-05-10) Augustat, Nick; Müller, Erik Malte; Endres, Dominik; Chuang, Li-Ching; Panitz, Christian; Stolz, ChristopherResearch Data Open Access Purchasing under threat: Changes in purchasing patterns during the COVID-19 pandemic(Sebastian Schmidt) Schmidt, Sebastian; Pané-Farré, Christiane; Benke, ChristophResearch Data Open Access Sensorimotor processes are not a source of much noise: sensorimotor and decision components of reaction timesMeibodi, Neda; Schubö, Anna; Endres, DominikResearch Data Open Access Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning(Philipps-Universität Marburg) Augustat, Nick; Endres, Dominik; Müller, Erik MalteThis repository includes code and data necessary for running the online survey and task, and the analyses of the manuscript "Augustat, N., Endres, D., Müller, E. M.: Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning". Abstract: The placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty, analogously to reward uncertainty, may affect reward learning after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward learning. In an online between-subjects experiment with N=141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.