Javascript must be enabled for the correct page display

Unexpected Uncertainty in decision making and how it influences self-generated thought

boersma, jussi (2019) Unexpected Uncertainty in decision making and how it influences self-generated thought. Bachelor's Thesis, Artificial Intelligence.

[img]
Preview
Text
AI_BA_2019_JUSSIBOERSMA.pdf

Download (325kB) | Preview
[img] Text
Toestemming.pdf
Restricted to Registered users only

Download (123kB)

Abstract

In this modern world we frequently have to make choices. At moments of rest you can find yourself spontaneously starting to ponder about these choices. This is part of a phenomenon that we call mind-wandering. It could be that the more uncertain we are about a choice the more likely we are to mind-wander about it. What we want to find out in this research is if the amount and context of mind-wandering is influenced by the amount of uncertainty in an environment. To do this we created an experiment consisting of two alternating tasks. A two armed bandit response task where participants had to choose between two selling platforms to sell imaginary items and maximize their total reward. One of the two platforms would always have a higher chance of selling the item, this chance was periodically switched to create uncertainty. This task was followed by a metronome response task (MRT) in which participants would be probed about the content of their thoughts. We also fitted a reinforcement learning model to the data of our participants to find out if parameters used in this model show any relation to the thought types of the participants. In our study we have found that variability in MRT responses is significantly higher when people reported to be mind-wandering. We were unable to find a significant relation between uncertainty and mind-wandering. We also found no significant relation between the model parameters and thought types.

Item Type: Thesis (Bachelor's Thesis)
Supervisor:
Supervisor nameSupervisor E mail
Vugt, M.K. vanM.K.van.Vugt@rug.nl
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 11 Jul 2019
Last Modified: 12 Jul 2019 07:05
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/20107

Actions (login required)

View Item View Item