Project description
A novel approach to understanding food-seeking behaviour
The selection of food is guided by sensory feedback, including post-ingestive information about energy content, and it has been studied across several species, paradigms and nutrients. The EU-funded CalorieRL project aims to use computational reinforcement learning (RL) models applied to instrumental conditioning, in addition to brain functional and molecular imaging and peripheral nerve stimulation, to study post-ingestive reinforcement of food-seeking behaviour. The study hypothesises that behaviour has a dopaminergic substrate and is associated with neural activity in brain reward circuits, resulting from sensory information transmitted through the vagus nerve. Furthermore, the project will address whether the RL and neural correlates of post-ingestive reinforcement are relevant in the context of obesity.
Objective
The drive to eat is one of the strongest in the modulation of behaviour. Selection of food is guided by sensory feedback, including post-ingestive information about energy content, that has been studied across several species, paradigms and nutrients. In rodents, post-oral administration of sugar is associated with striatal dopamine release and conditions robust preferences, in part as a result of viscerosensory information transmitted through the vagus nerve. Consistently, in humans there is evidence that pairing a flavour with a tasteless carbohydrate will enhance pleasantness of the pre-ingestive stimulus and increase its consumption.
However, available data in humans have provided limited mechanistic underpinnings for this process and, in our own experiments, performed with greater control of pre-ingestive stimulation, while we reproduced increased consumption of conditioned flavours, we found no evidence for post-ingestive mediated changes of pleasantness. Critically, this suggests that the impact of nutrient conditioning on behaviour and choice reflects the modulation of the value of actions, rather than that of associated stimuli. The application of computational reinforcement learning theory to human probabilistic decision-making tasks, which has allowed for renewed sophistication and progress to understand the mechanisms of appetitive learning, is yet to be applied to address this question.
In CalorieRL, we will use computational reinforcement models applied to instrumental conditioning, in addition to brain functional and molecular imaging and peripheral nerve stimulation, to study post-ingestive reinforcement of food-seeking behaviour. We hypothesize such behaviour has a dopaminergic substrate and is associated with neural activity in brain reward circuits, resulting from sensory information transmitted through the vagus nerve. Importantly, we will also address if post-ingestive reinforcement is relevant in the context of obesity.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesbiochemistrybiomoleculescarbohydrates
- medical and health scienceshealth sciencesnutritionobesity
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Funding Scheme
ERC-STG - Starting GrantHost institution
1400-038 Lisboa
Portugal