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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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. Abstract One of the brain’s key roles is to facilitate foraging and feeding. It is presumably no coincidence, then, that the mouth is situated close to the brain in most animal species. However, the environments in which our brains evolved were far less plentiful in terms of the availability of food resources (i.e., nutriments) than is the case for those of us living in the Western world today. The growing obesity crisis is but one of the signs that humankind is not doing such a great job in terms of optimizing the contemporary food landscape. While the blame here is often put at the doors of the global food companies – offering addictive foods, designed to hit ‘the bliss point’ in terms of the pleasurable ingredients (sugar, salt, fat, etc.), and the ease of access to calorie-rich foods – we wonder whether there aren’t other implicit cues in our environments that might be triggering hunger more often than is perhaps good for us. Here, we take a closer look at the potential role of vision; Specifically, we question the impact that our increasing exposure to images of desirable foods (what is often labelled ‘food porn’, or ‘gastroporn’) via digital interfaces might be having, and ask whether it might not inadvertently be exacerbating our desire for food (what we call ‘visual hunger’). We review the growing body of cognitive neuroscience research demonstrating the profound effect that viewing such images can have on neural activity, physiological and psychological responses, and visual attention, especially in the ‘hungry’ brain. Abstract Sixteen normal-weight subjects rated the perceived intensity of sweetness, fatness, and creaminess of 20 different mixtures of milk, cream, and sugar, and assigned an overall pleasantness (hedonic) rating to each sample. Intensity estimates increased as power functions of ingredient concentration and no significant mixture suppression effect was observed. In contrast, hedonic responses strongly depended on the relative proportions of sucrose and fat in the samples tasted. Hedonic preference ratings first rose and then declined with increasing sucrose concentration, but continued to rise with increasing dairy fat content. The addition of sucrose greatly enhanced hedonic ratings for high-fat stimuli. Changes in hedonic responsiveness were monitored using a mathematical modelling technique known as the Response Surface Method, which allows computerized simulation of the hedonic response surface as a function of perceived ingredient levels. Overnight fasting did not affect the perception or hedonics for sweet or “fatty” tastes. The observed preference for sweetened high-fat foods may have implications for the development of dietary-induced obesity in man. Food is an integral part of our lives, cultures, and well-being, and is of major interest to public health. The collection of daily nutritional data involves keeping detailed diaries or periodic surveys and is limited in scope and reach. Alternatively, social media is infamous for allowing its users to update the world on the minutiae of their daily lives, including their eating habits. In this work we examine the potential of Twitter to provide insight into US-wide dietary choices by linking the tweeted dining experiences of 210K users to their interests, demographics, and social networks. We validate our approach by relating the caloric values of the foods mentioned in the tweets to the state-wide obesity rates, achieving a Pearson correlation of 0.77 across the 50 US states and the District of Columbia. We then build a model to predict county-wide obesity and diabetes statistics based on a combination of demographic variables and food names mentioned on Twitter. Our results show significant improvement over previous CHI research (Culotta 2014). We further link this data to societ al and economic factors, such as education and income, illustrating that areas with higher education levels tweet about food that is significantly less caloric. Finally, we address the somewhat controversial issue of the social nature of obesity (Christakis & Fowler 2007) by inducing two social networks using mentions and reciprocal following relationships.