Study title

Dataset and associated material used for the machine learning analysis of food choices. The dataset was obtained from an experiment with 154 participants who made 30 choices of the healthiest food within a choice array of 6 options, given nutritional label data. This is a secondary data analysis and the original data collection was not funded by the grant. The dataset contains 4260 observations (response trials) and 51 variables.Proper nutrition and healthy diets are a key aspect of health, which mandatory food labelling in the UK tries to address by empowering people with the information to help them make healthier choices. The format of this information (e.g., verbal quantifiers like 'low fat' or numerical quantifiers like '5% fat') affects whether people can easily understand and use food labels. Examining how people's judgements and decisions with respect to food differ depending on food label format therefore has wide-reaching impact for health policy decisions, consumer behaviour, and food industry practice. This project will use computational methods to identify different strategies people use to decide what foods are healthiest (e.g., less fat, or less sugar, etc.) I will evaluate which strategies produce the healthiest choices, use these insights to inform policy and conduct knowledge exchange with my industry partner. The project will consolidate my PhD, which investigated differences in people's decision-making strategies when using verbal and numerical quantifiers on food labels. Using a mixture of behavioural tasks, surveys, and eye-tracking methodology, I identified that different ways of presenting quantities can lead to people relying on different pieces of information to judge food. I intend to extend this research and maximise its impact in four ways. First, I will apply new and advanced statistical modelling to my research. To classify and predict food choice strategies in my data, I will learn two modelling techniques: multinomial...