| Paper: | PS-2B.18 | ||
| 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: | Perceptual uncertainty modulates human reward-based learning | ||
| 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.2019.1042-0 | ||
| Authors: | Rasmus Bruckner, Hauke Heekeren, Dirk Ostwald, Freie Universität Berlin, Germany | ||
| Abstract: | To successfully interact with an everchanging world imbued with uncertainties, humans have to learn probabilistic state-action-reward contingencies. Here we investigate the computational principles that govern decision making and state-action-reward contingency learning under perceptual uncertainty. To this end, we designed an integrated perceptual and economic decision making learning task and acquired behavioural data from 52 human participants. To interpret the participants' choice data, we developed a set of seven artificial agent models that allow testing if humans consider or ignore perceptual uncertainty. Moreover, we apply these models to test if learning under perceptual uncertainty can be better described according to principles of Bayesian inference or a temporal-difference learning rule. Our results favor a Bayesian agent model that suggests that humans integrate their subjective perceptual uncertainty when learning probabilistic state-action-reward contingencies. Importantly, humans partly deviate from optimal Bayesian inference in that previous perceptual choices influence the regulation of learning at the cost of an underestimation of perceptual uncertainty. Together, this study provides a better understanding of the computational mechanisms of human state-action-reward contingency learning under perceptual uncertainty. | ||