Giovanni Alfonso Borelli (1608-1679) and Giorgio Baglivi (1668-1707) were prominent figures in early modern physiology who contributed significantly to the evolving debate on the origin of the heartbeat. Borelli, a leading exponent of iatromechanics, acknowledged the autonomous continuation of the heartbeat but attributed its initial impulse to a facultas sensitiva capable of perceiving internal imbalances. Baglivi advanced the discussion by proposing a fiber-based model of the body and offering experimental evidence of the heart's intrinsic contractile capacity. He classified body fibers into membranous and carneous types, locating the active force of contraction within the structure of the muscle itself. Although neither author wholly excluded the role of nerves, their work marked a pivotal moment in the conceptual shift toward intrinsic explanations of cardiac rhythm and laid the groundwork for future developments in cardiovascular physiology.NEW & NOTEWORTHY Borelli and Baglivi were pioneers in cardiac physiology in the seventeenth and eighteenth centuries. They both contributed to the gradual shift toward intrinsic explanations of the heartbeat, proposing that the heart possesses an autonomous capacity for contraction, laying the foundations for later myogenic models. Their experimental and theoretical work marked a turning point in early modern physiology, paving the way for subsequent research in cardiac function and the development of modern cardiovascular physiology.

At the heart of language neuroscience lies a fundamental question: How does the brain process the rich variety of languages? Multilingual neural network models offer a way to answer this question by representing linguistic content across languages in a shared space. Leveraging these advances, we evaluated the similarity of linguistic representations in speakers of 21 languages. We combined existing (12 languages across 4 language families) and newly collected fMRI data (9 languages across 4 families) to test encoding models predicting brain activity in the language network using representations from multilingual models. Model representations reliably predicted brain responses within each language. Critically, encoding models can be transferred zero-shot across languages, so that a model trained to predict brain activity in a set of languages can account for responses in a held-out language. These results imply a shared cross-lingual component, which appears to be related to a shared meaning space.