Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian & Albert-László Barabási
Nature Communications volume 14, Article number: 2312 (2023)

Despite the accumulating evidence that increased consumption of ultra-processed food has adverse health implications, it remains difficult to decide what constitutes processed food. Indeed, the current processing-based classification of food has limited coverage and does not differentiate between degrees of processing, hindering consumer choices and slowing research on the health implications of processed food. Here we introduce a machine learning algorithm that accurately predicts the degree of processing for any food, indicating that over 73% of the US food supply is ultra-processed. We show that the increased reliance of an individual’s diet on ultra-processed food correlates with higher risk of metabolic syndrome, diabetes, angina, elevated blood pressure and biological age, and reduces the bio-availability of vitamins. Finally, we find that replacing foods with less processed alternatives can significantly reduce the health implications of ultra-processed food, suggesting that access to information on the degree of processing, currently unavailable to consumers, could improve population health.

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What was once outright denial has morphed into a subtler dismissal.

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Adam Safron, Dalton A. R. Sakthivadivel, Zahra Sheikhbahaee, Magnus Bein, Adeel Razi and Michael Levin

Symmetry is a motif featuring in almost all areas of science. Symmetries appear throughout the natural world, making them particularly important in our quest to understand the structure of the world around us. Symmetries and invariances are often first principles pointing to some lawful description of an observation, with explanations being understood as both ‘satisfying’ and potentially useful in their regularity. The sense of aesthetic beauty accompanying such explanations is reminiscent of our understanding of intelligence in terms of the ability to efficiently predict (or compress) data; indeed, identifying and building on symmetry can offer a particularly elegant description of a physical situation. The study of symmetries is so fundamental to mathematics and physics that one might ask where else it proves useful. This theme issue poses the question: what does the study of symmetry, and symmetry breaking, have to offer for the study of life and the mind?

Interface Focus Volume 13 Issue 3

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Vincent J Straub, Milena Tsvetkova, and Taha Yasseri

Collective Intelligence

Humans and other intelligent agents often rely on collective decision making based on an intuition that groups outperform individuals. However, at present, we lack a complete theoretical understanding of when groups perform better. Here, we examine performance in collective decision making in the context of a real-world citizen science task environment in which individuals with manipulated differences in task-relevant training collaborated. We find 1) dyads gradually improve in performance but do not experience a collective benefit compared to individuals in most situations; 2) the cost of coordination to efficiency and speed that results when switching to a dyadic context after training individually is consistently larger than the leverage of having a partner, even if they are expertly trained in that task; and 3) on the most complex tasks having an additional expert in the dyad who is adequately trained improves accuracy. These findings highlight that the extent of training received by an individual, the complexity of the task at hand, and the desired performance indicator are all critical factors that need to be accounted for when weighing up the benefits of collective decision making.

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