Tectonic evolution of the southern margin of the Amazonian craton in the late Mesoproterozoic based on field relationships and zircon U-Pb geochronology

New U-Pb zircon geochronological data integrated with field relationships and an airborne geophysical survey suggest that the Nova Brasilândia and Aguapeí belts are part of the same monocyclic, metaigneous and metasedimentary belt formed in the late Mesoproterozoic (1150 Ma-1110 Ma). This geological history is very similar to the within-plate origin of the Sunsás belt, in eastern Bolivia. Thus, we propose that the Nova Brasilândia, Aguapeí and Sunsás belts represent a unique geotectonic unit (here termed the Western Amazon belt) that became amalgamated at the end of the Mesoproterozoic and originated through the reactivation of a paleo-suture (Guaporé suture zone) in an intracontinental rift environment. Therefore, its geological history involves a short, complete Wilson cycle of ca. 40 Ma. Globally, this tectonic evolution may be related with the final breakup of the supercontinent Columbia. Mafic rocks and trondhjemites in the northernmost portion of the belt yielded U-Pb zircon ages ca. 1110 Ma, which dates the high-grade metamorphism and the closure of the rift. This indicates that the breakup of supercontinent Columbia was followed in short sequence by the assembly of supercontinent Rodinia at ca. 1.1-1.0 Ga and that the Western Amazon belt was formed during the accretion of the Arequipa-Antofalla basement to the Amazonian craton

Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task

A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task

Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes

Instrumental responses are hypothesized to be of two kinds: habitual and goal-directed, mediated by the sensorimotor and the associative cortico-basal ganglia circuits, respectively. The existence of the two heterogeneous associative learning mechanisms can be hypothesized to arise from the comparative advantages that they have at different stages of learning. In this paper, we assume that the goal-directed system is behaviourally flexible, but slow in choice selection. The habitual system, in contrast, is fast in responding, but inflexible in adapting its behavioural strategy to new conditions. Based on these assumptions and using the computational theory of reinforcement learning, we propose a normative model for arbitration between the two processes that makes an approximately optimal balance between search-time and accuracy in decision making. Behaviourally, the model can explain experimental evidence on behavioural sensitivity to outcome at the early stages of learning, but insensitivity at the later stages. It also explains that when two choices with equal incentive values are available concurrently, the behaviour remains outcome-sensitive, even after extensive training. Moreover, the model can explain choice reaction time variations during the course of learning, as well as the experimental observation that as the number of choices increases, the reaction time also increases. Neurobiologically, by assuming that phasic and tonic activities of midbrain dopamine neurons carry the reward prediction error and the average reward signals used by the model, respectively, the model predicts that whereas phasic dopamine indirectly affects behaviour through reinforcing stimulus-response associations, tonic dopamine can directly affect behaviour through manipulating the competition between the habitual and the goal-directed systems and thus, affect reaction time