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.
@cite_44 examined the relationship between goal-directed valuations of food images by both lean and overweight people in an MRI scanner and food consumption at a subsequent all-you-can-eat buffet. They observed that both lean and overweight participants showed similar patterns of value-based neural responses to health and taste attributes of foods. This suggests that a shift in obesity may lie in how the presence of food overcomes prior value-based decision-making.
@cite_44
2305315319
To develop more ecologically valid models of the neurobiology of obesity, it is critical to determine how the neural processes involved in food-related decision-making translate into real-world eating behaviors. We examined the relationship between goal-directed valuations of food images in the MRI scanner and food consumption at a subsequent ad libitum buffet meal. We observed that 23 lean and 40 overweight human participants showed similar patterns of value-based neural responses to health and taste attributes of foods. In both groups, these value-based responses in the ventromedial PFC were predictive of subsequent consumption at the buffet. However, overweight participants consumed a greater proportion of unhealthy foods. This was not predicted by in-scanner choices or neural response. Moreover, in overweight participants alone, impulsivity scores predicted greater consumption of unhealthy foods. Overall, our findings suggest that, while the hypothetical valuation of the health of foods is predictive of eating behavior in both lean and overweight people, it is only the real-world food choices that clearly distinguish them.