A common mechanism underlying food choice and social decisions

People make numerous decisions every day including perceptual decisions such as walking through a crowd, decisions over primary rewards such as what to eat, and social decisions that require balancing own and others’ benefits. The unifying principles behind choices in various domains are, however, still not well understood. Mathematical models that describe choice behavior in specific contexts have provided important insights into the computations that may underlie decision making in the brain. However, a critical and largely unanswered question is whether these models generalize from one choice context to another. Here we show that a model adapted from the perceptual decision-making domain and estimated on choices over food rewards accurately predicts choices and reaction times in four independent sets of subjects making social decisions. The robustness of the model across domains provides behavioral evidence for a common decision-making process in perceptual, primary reward, and social decision making.

Novel insights into mechanisms of food allergy and allergic airway inflammation using experimental mouse models

Over the last decades, considerable efforts have been undertaken in the development of animal models mimicking the pathogenesis of allergic diseases occurring in humans. The mouse has rapidly emerged as the animal model of choice, due to considerations of handling and costs and, importantly, due to the availability of a large and increasing arsenal of genetically modified mouse strains and molecular tools facilitating the analysis of complex disease models. Here, we review latest developments in allergy research that have arisen from in vivo experimentation in the mouse, with a focus on models of food allergy and allergic asthma, which constitute major health problems with increasing incidence in industrialized countries. We highlight recent novel findings and controversies in the field, most of which were obtained through the use of gene-deficient or germ-free mice, and discuss new potential therapeutic approaches that have emerged from animal studies and that aim at attenuating allergic reactions in human patients.

Self-licensing effects on food choices

Recalling an egoistic act nudges people to choose healthy over unhealthy food options. Conversely, participants preferred unhealthy over healthy food options when they recalled an altruistic deed. Consistent with this choice pattern participants were willing to pay more for healthy than for unhealthy options. This experiment extends the self-licensing literature.

[Sausage or carrot--spoilt for choice].

As far as healthy food is concerned, it cannot be categorized simply as "good" or "bad" since its effect on health depends mainly on the amount and method consumed. Today's recommendations include a diversified diet, a diet which targets energy-balance and provides all nutrients necessary. Living in an affluent society aggravates healthy choices because of a constantly available, large assortment of food items. In general, the way food is prepared these days has changed a lot: mainly, the energy content has constantly increased, while the fiber and natural micronutrient concentrations decreased. Food items with a high energy yield, containing a lot of fat and sugar, affects our energy balance, which may lead to diseases of affluence such as type 2 diabetes, cardiovascular diseases, and some kinds of tumors.

Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.