Levantine cranium from Manot Cave (Israel) foreshadows the first European modern humans

A key event in human evolution is the expansion of modern humans of African origin across Eurasia between 60 and 40 thousand years (kyr) before present (BP), replacing all other forms of hominins1. Owing to the scarcity of human fossils from this period, these ancestors of all present-day non-African modern populations remain largely enigmatic. Here we describe a partial calvaria, recently discovered at Manot Cave (Western Galilee, Israel) and dated to 54.7 ± 5.5 kyr BP(arithmetic mean ± 2 standard deviations) by uranium–thorium dating, that sheds light on this crucial event. The overall shape and discrete morphological features of the Manot 1 calvaria demonstrate that this partial skull is unequivocally modern. It is similar in shape to recent African skulls as well as to European skulls from the Upper Palaeolithic period, but different from most other early anatomically modern humans in the Levant. This suggests that the Manot people could be closely related to the first modern humans who later successfully colonized Europe. Thus, the anatomical features used to support the ‘assimilation model’ in Europe might not have been inherited from European Neanderthals, but rather from earlier Levantine populations. Moreover, at present, Manot 1 is the only modern human specimen to provide evidence that during the Middle to Upper Palaeolithic interface, both modern humans and Neanderthals contemporaneously inhabited the southern Levant, close in time to the likely interbreeding event with Neanderthals2,3

Optimality and modularity in human movement: from optimal control to muscle synergies

International audienceIn this chapter, we review recent work related to the optimal and modular control hypotheses for human movement. Optimal control theory is often thought to imply that the brain continuously computes global optima for each motor task it faces. Modular control theory typically assumes that the brain explicitly stores genuine synergies in specific neural circuits whose combined recruitment yields task-effective motor inputs to muscles. Put this way, these two influential motor control theories are pushed to extreme positions. A more nuanced view, framed within Marr’s tri-level taxonomy of a computational theory of movement neuroscience, is discussed here. We argue that optimal control is best viewed as helping to understand “why” certain movements are preferred over others but does not say much about how the brain would practically trigger optimal strategies. We also argue that dimensionality reduction found in muscle activities may be a by-product of optimality and cannot be attributed to neurally hardwired synergies stricto sensu, in particular when the synergies are extracted from simple factorization algorithms applied to electromyographic data; their putative nature is indeed strongly dictated by the methodology itself. Hence, more modeling work is required to critically test the modularity hypothesis and assess its potential neural origins. We propose that an adequate mathematical formulation of hierarchical motor control could help to bridge the gap between optimality and modularity, thereby accounting for the most appealing aspects of the human motor controller that robotic controllers would like to mimic: rapidity, efficiency, and robustness