Using consumption and reward simulations to create desire for plant-based foods

The production of animal-based foods has a significant, negative impact on the environment. Compared to plant-based protein sources, such as beans and lentils, the production of beef and other red meat requires 20 times more land and emits 20 times more greenhouse gas emissions per unit of edible protein. To curb climate change and sustain the planet, consumers urgently need to shift towards more plant-based diets.

Most food choices, however, are driven by expectations of taste, rather than sustainability, and plant-based foods are perceived as less tasty than meat-based foods. How can these perceptions be changed to promote sustainable, plant-based food choices?

This project proposes that desire for plant-based foods can be enhanced by food descriptions that emphasise sensory features, eating context, and hedonic consequences. Such descriptions will trigger spontaneous simulations (i.e., re-experiences) of eating and enjoying a food, and therefore create rewarding expectancies and desire. Supporting this account, previous work has shown that people describe attractive foods in terms of sensory experiences, eating context, and hedonic enjoyment.

Such descriptions, in turn, are associated with increased desire for a food. This suggests that people spontaneously simulate (re-experience) eating attractive food, and that this contributes to the desire to eat. In addition, our pilot work has shown that adding such simulation features to labels for plant-based foods increases attractiveness of these food.

Here, we will build on these preliminary findings and develop and test a systematic strategy for labeling plant-based foods such that they trigger desire.

In Work Package 1, we will systematically establish the simulation features that induce desire for a large number of existing dishes. In a task adapted from cognitive psychology, participants will list features of a large number of foods. We will ask different groups of participants to focus on specific kinds of features (e.g., sensory features, eating context, etc).

We will assemble these features and their links with attractiveness ratings in a comprehensive data-base that we will make openly available and disseminate among stakeholders in the food industry.

In Work Package 2, we will use this simulation feature database to create desire-inducing labels for plant-based foods. Based on ratings by plant-based food consumers and mainstream consumers, we will select features that fit a number of relatively novel plant-based foods. These foods will be selected in collaboration with our partner, the Better Buying Lab, who have a strong network of industry partners actively engaged in the shift toward plant-based diets.

Then, we will use these features to create eating simulation food labels, and conduct experiments to test whether such labels increase desire by assessing eating simulations, desire, salivation, and purchase intentions, compared to control labels that don't include eating simulation features.

Finally, Work Package 3 will examine whether eating simulation labels affect actual liking, and if they increase purchase intentions measured after consumption only if the product lives up to the expectations created by the label. Here, consumers will taste a plant-based food that is presented with an eating simulation label or a control label, and will report liking and purchase intentions.

Half of consumers receive a tasty version of the product, and half of consumers receive an adapted, bland version. Assessing desire before and after tasting the food will allow us to examine the interplay between simulations and experiences, which will be informative for the psychology of desire more generally.

Together, these studies will establish a detailed understanding of the role of simulations in desire for food, and show how simulation-inducing language can be used to boost desire. This will help to make the urgent the dietary shift needed to sustain the planet.