Internal models in the cerebellum.

This review will focus on the possibility that the cerebellum contains an internal model or models of the motor apparatus. Inverse internal models can provide the neural command necessary to achieve some desired trajectory. First, we review the necessity of such a model and the evidence, based on the ocular following response, that inverse models are found within the cerebellar circuitry. Forward internal models predict the consequences of actions and can be used to overcome time delays associated with feedback control. Secondly, we review the evidence that the cerebellum generates predictions using such a forward model. Finally, we review a computational model that includes multiple paired forward and inverse models and show how such an arrangement can be advantageous for motor learning and control.

Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

Dialogue to address the roots of resource competition: Lessons for policy and practice

Conflict management is an intrinsic element of natural resource management, and becomes increasingly important amid growing pressure on natural resources from local uses, as well as from external drivers such as climate change and international investment. If policymakers and practitioners aim to truly improve livelihood resilience and reduce vulnerabilities of poor rural households, issues of resource competition and conflict management cannot be ignored. This synthesis report summarizes outcomes and lessons from three ecoregions: Lake Victoria, with a focus on Uganda; Lake Kariba, with a focus on Zambia; and Tonle Sap Lake in Cambodia. Partners used a common approach to stakeholder engagement and action research that we call “Collaborating for Resilience”. In each region, partners assisted local stakeholders in developing a shared understanding of risks and opportunities, weighing alternative actions, developing action plans, and evaluating and learning from the outcomes. These experiences demonstrate that investing in capacities for conflict management is practical and can contribute to broader improvements in resource governance.