Food is an integral part of our life and what and how much we eat crucially affects our health. Our food choices largely depend on how we perceive certain characteristics of food, such as whether it is healthy, delicious or if it qualifies as a salad. But these perceptions differ from person to person and one person's "single lettuce leaf" might be another person's "side salad". Studying how food is perceived in relation to what it actually is typically involves a laboratory setup. Here we propose to use recent advances in image recognition to tackle this problem. Concretely, we use data for 1.9 million images from Instagram from the US to look at systematic differences in how a machine would objectively label an image compared to how a human subjectively does. We show that this difference, which we call the "perception gap", relates to a number of health outcomes observed at the county level. To the best of our knowledge, this is the first time that image recognition is being used to study the "misalignment" of how people describe food images vs. what they actually depict.
Whereas the three studies discussed above studied the perception at the level of brain activity, our own work only looks at data from perception reported in the form of hashtags. This, indirectly, relates to a review by @cite_51 of studies on the link between the (self-declared) palatability, i.e., the positive sensory perception of foods, and the food intake. All of their reviewed studies showed that increased palatability leads to increased intake. In , we study a similar aspect by looking at regional differences in what is tagged as #delicious and how this relates to obesity rates and other health outcomes.
@cite_51
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Effect of sensory perception of foods on appetite and food intake: a review of studies on humans