Physiological modulation of learning and decision-making

Thesis

Many of the decisions we make in our day-to-day lives are influenced by internal physiological states and external environmental factors. In my thesis, I employed an interdisciplinary approach to investigate how internal physiological states, such as hunger, affect various aspects of decision-making. Neurobiological research has shown that food deprivation enhances dopaminergic signalling and increases the incentive salience of food. However, classical reinforcement learning theory only maps learning of external factors onto dopaminergic signalling. I extended the reinforcement learning theory in a biologically relevant manner to incorporate mechanisms by which internal physiological signals interact with the dopaminergic system to influence learning and action selection (chapter 3). This new theory predicts that food deprivation enhances learning, promotes approach behaviour, and reduces avoidance behaviour by enhancing the dopaminergic activation and teaching signals. I tested these theoretical predictions for decision-making in human volunteers. This study showed that hunger increased the valuation of food, but not of monetary rewards or nonfood items (chapter 4). Hunger reduced the biases for approach and avoidance behaviour that sated people exhibit by modulating the trade-offbetween positive and negative consequences of actions (chapter 5). Hunger also interacted with economic choice, but only when outcomes of choices had to be learned, not when they were explicitly described (chapter 6). Lastly, this study showed that hunger enhanced the reliance of "gut-instinct", without affecting cognitive control (chapter 7). In summary, this thesis characterises mechanisms by which motivational drives, such as hunger, could affect valuation, learning, and action selection in a biological manner. It provides empirical evidence that hunger also affects decision-making for monetary rewards, which could have significant implications for both real-world economic transactions and for aberrant decision-making in eating disorders and obesity.

Authors: Van Swieten, MMH

• DOI: 10.5287/ora-2n2m0kenr
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: decision-making; learning; physiology; reinforcement learning; risk-taking; hunger; computational modelling; dopamine
• Subjects: Computational neuroscience; Cognitive neuroscience; Behavioural neuroscience