Year: 2023
Type: dissertation
Abstract: One of the main challenges for global public health in the modern world is the rising prevalence of obesity. Obtaining a better understanding of the dysregulated feeding behaviour that leads to obesity, by investigating the decision making and learning processes underlying it, could advance our capabilities in battling the obesity epidemic. Consequently, our aim in this study is to design an experiment that could evaluate these processes. We examined ten healthy participants using a modified version of the "probabilistic selection task". We used gustatory stimuli as a replacement for monetary rewards, to assess the effect of nutritional rewards on the learning behaviour. We subsequently analysed the behavioural results with computational modelling and combined this with imaging data simultaneously acquired with a functional magnetic resonance imaging (fMRI) multiband sequence. All participants in this study succeeded in interpreting and interacting with the gustatory stimuli appropriately. Performance on the task was affected by the subjective valuation of the reward. Participants whose motivation to drink the reward and liking of its taste decreased during the task presented difficulties correctly choosing the more rewarding cues. Computational modelling of the behaviour found that the so-called asymmetric learning model, in which positive and negative reinforcement are differently weighted, best explained the group. The acquired fMRI data was suboptimal and we did not detect the neurological activity we expected in the reward system, which is central to our scientific question. Thus, our study shows it is possible to implement the PST with gustatory stimuli. However, to evaluate the corresponding neurological activity, our fMRI configuration requires improvement. An optimised system could be used in further studies to improve our understanding of the neurobiological mechanisms of learning that lead to obesity and elucidate the role of food as a distinctive reinforcer.
Author: Reiner, Omer
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6981072032