| Paper: | PS-2B.28 |
| Session: | Poster Session 2B |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Computational mechanisms of human state-action-reward contingency learning under perceptual uncertainty |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1078-0 |
| Authors: |
Dirk Ostwald, Rasmus Bruckner, Hauke Heekeren, Freie Universität Berlin, Germany |
| Abstract: |
To successfully interact with an everchanging world imbued with uncertainties, humans often have to learn probabilistic state-action-reward contingencies. Reinforcement learning algorithms have been able to provide a mechanistic picture of the neurocomputational principles that govern such learning and decision processes. However, standard reinforcement learning algorithms assume that the environmental state is fully observable. Humans, on the other hand, often have to learn the expected reward of choice options under considerable perceptual uncertainty. In this project we investigate the computational principles that govern probabilistic state-action-reward 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 artificial agents which describe a range of cognitive-computational strategies. These strategies range from Bayes-optimal exploitative decision making that takes perceptual uncertainty parametrically into account to fully random choice policies. Our behavioural modelling initiative favoured an agent model that suggests that human participants integrate their subjective perceptual uncertainty when learning probabilistic state-action-reward contingencies. They tend, however, to underestimate the degree they should do so from a normative Bayes-optimal perspective. |
