People often want to eat more healthily, but often make spur-of-the-moment decisions to eat foods that don't fit within their ideal health goals. Some research has shown that inhibitory training-- that is, practicing stopping your behaviour when you think of a cue-- can help people eat less unhealthy foods. However, other research shows that cognitive training, or practicing what you think about foods, is also effective. This research is the first to directly compare and contrast the effectiveness of both behavioural and cognitive training, and also identify who it's more effective for, and how long these effects last.

While we can imagine the crisp sweetness of an apple, or the painful heat of an open fire, it's not always clear what, and how, we choose to remember when we make decisions. Often, with risky choices, we find we want to get as much as possible, but if we don't prioritize the right information, we may make ill-advised choices. This research combines drift-diffusion modeling, a computational model of choice, and electroencephalography (EEG), a neuroimaging technique, to understand how we might prioritize more useful information in the scant seconds we need to make real-world decisions.

Despite making us feel angry and disgusted, morally charged news tends to go viral. Why do people seemingly seek out and spread moral outrage even though it makes us feel bad? Is there a benefit? Are there certain kinds of people who actively seek moral outrage? This research project uses qualitative data to characterize the kinds of reasons people seek, or avoid, morally outraging news, as well as quantitative data to consider which factors are most important to determine why people choose to read morally outraging headlines.

We tend to first focus on the information most relevant to us first. Sometimes, we bring our biases before we even meet someone or try a new kind of food (e.g. "I think musicians are flaky."). Other times, we're motivated to find the good or bad of a situation (e.g. "I find them annoying, but my friend is dating them, so I should try to play nice"). While this difference is subtle, computational models of choice can tease apart these mechanisms-- biases happen before you see what you're working with, while motivated reasoning happens after. However, I demonstrate how researchers should be careful about correctly specifying these computational models, as ignoring one of these influences in model parameters, or misspecifying these models, can lead to misleading conclusions about whether we're biased or motivated during decision-making.

Despite research showing that processed and ready-made foods is associated with negative health consequences, surveys suggest that up to 70% of the food that North American households consume consists of convenient, fast, and ready-made meals rather than healthy foods. Laboratory research has overlooked the importance of convenience for the tastiness and healthiness of foods, since foods are pre-prepared in the lab, rendering effort irrelevant. However, my work shows that convenience, even when irrelevant, is remembered and predictive of dietary decisions, above and beyond taste and health.