| Paper: | PS-2B.59 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Adaptation to environmental statistics in an action control task | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1332-0 | ||
| Authors: | Nils Neupärtl, Constantin Rothkopf, Technical University Darmstadt, Germany | ||
| Abstract: | Although humans are prone to perceptual illusions and decision biases, they perform very well in every-day tasks with varying difficulties and complexities. It has been shown that humans learn to adopt to the statistical regularities of the environment. However, whether humans have correct physical intuitions about these ordinary processes and reflect related dynamics in an appropriate internal model has been disputed. Recent studies have shown that human behavior in diverse physical judgment tasks can indeed be explained with probabilistic models based on realistic, Newtonian functions while considering sensory uncertainties. Here, we examined whether humans use physical models of their environment in a control task, which involves non-linearities in the involved dynamics. Participants were asked to shoot a puck into a target area affected by realistic friction. By deploying Bayesian models we can show that humans are capable to adopt to these physical relationships and have appropriate internal beliefs about relevant quantities. | ||