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

动物性食品的生产对环境有重大的负面影响。与豆类和扁豆等以植物为基础的蛋白质来源相比，牛肉和其他红肉的生产需要20倍的土地，每单位可食用蛋白质排放的温室气体也是植物的20倍。为了遏制气候变化和维持地球的生存，消费者迫切需要转向更多以植物为基础的饮食。然而，大多数食物的选择是由对味道的期望驱动的，而不是可持续性，植物性食物被认为不如肉类食物美味。如何改变这些观念，以促进可持续的植物性食物选择？该项目提出，可以通过强调感官特征、进食环境和享乐后果的食品描述来增强人们对植物性食品的渴望。这样的描述将触发对饮食和享受食物的自发模拟(即重新体验)，从而创造出令人满意的期望和欲望。支持这一说法的是，之前的研究表明，人们从感官体验、饮食环境和享乐享受三个方面描述有吸引力的食物。反过来，这样的描述又与对食物的渴望增加有关。这表明，人们自发地模拟(重新体验)吃有吸引力的食物，这有助于产生进食的欲望。此外，我们的试点工作表明，在植物性食品的标签上添加这种模拟特征会增加这些食品的吸引力。在这里，我们将在这些初步发现的基础上，开发和测试一种系统的策略来标记植物性食品，使它们能够激发欲望。在工作包1中，我们将系统地建立诱导对大量现有菜肴的渴望的模拟功能。在一项改编自认知心理学的任务中，参与者将列出大量食物的特征。我们将要求不同的参与者将重点放在特定类型的特征上(例如，感觉特征、进食环境等)。我们将把这些特征及其与吸引力评级的联系汇集在一个全面的数据库中，我们将公开提供该数据库，并在食品行业的利益相关者中传播。在Work Package 2中，我们将使用这个模拟特征数据库为植物性食品创建诱人的标签。根据植物性食品消费者和主流消费者的评级，我们将选择适合一些相对新颖的植物性食品的特征。这些食品将与我们的合作伙伴Better Buyer Lab合作选择，后者拥有一个强大的行业合作伙伴网络，积极参与向植物性饮食的转变。然后，我们将使用这些功能来创建进食模拟食品标签，并进行实验，以测试与不包括进食模拟特征的对照标签相比，这些标签是否通过评估进食模拟、欲望、唾液和购买意图来增加欲望。最后，工作包3将检查进食模拟标签是否影响实际喜好，以及只有当产品达到标签创建的期望时，它们才会增加消费后测量的购买意图。在这里，消费者将品尝带有食用模拟标签或控制标签的植物性食品，并将报告喜好和购买意图。一半的消费者收到的是美味版本的产品，一半的消费者收到的是经过改编的平淡无奇的版本。在品尝食物之前和之后评估欲望将使我们能够检验模拟和体验之间的相互作用，这将对更广泛的欲望心理学提供信息。总而言之，这些研究将建立对模拟在食物欲望中的作用的详细理解，并展示如何使用模拟诱导语言来刺激欲望。这将有助于使这一紧迫的饮食转变成为维持地球所需的。

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.