Abstract
Food-related studies often categorize foods using criteria such as fat and sugar content (e.g., high-fat, high-sugar foods; low-fat, low-sugar foods), and use these categorizations for further analyses. While these criteria are relevant to nutritional health, it is unclear whether they agree with the ways in which we typically group foods. Do these objective categories correspond to our subjective sense? To address this question, we recruited a group of 487 online participants to perform a triplet comparison task involving implicit object similarity judgements on images of 36 foods, which varied in their levels of fat and sugar. We also acquired subjective ratings of other food properties from another set of 369 online participants. Data from the online triplet task was used to generate a similarity matrix of these 36 foods. Principal Components Analysis (PCA) of this matrix identified that the strongest determinant of food similarity (the first PC) was most highly related to participants' judgements of how processed the foods were, while the second component was most related to estimates of sugar and fat content. K-means clustering analysis revealed five emergent food groupings along these PC axes: sweets, fats, starches, fruits, and vegetables. Our results suggest that naturalistic categorizations of food are driven primarily by knowledge of the origin of foods (i.e., grown or manufactured), rather than by their sensory or macronutrient properties. These differences should be considered and explored when developing methods for scientific food studies.