Topic: sensorimotor behavior

Primate neurophysiology has provided numerous insights into the neural mechanisms of short and stereotypical movements, such as center-out reaching, which are mainly guided by feedforward control. However, to understand highly interactive and feedback-driven behaviors, experimental paradigms are needed that involve continuous interactions with the world. One example of such paradigms is stick-balancing which requires constant integration of feedback for successful control. Recently, a simplified virtual implementation of the stick-balancing task was developed as the Critical Stability Task (CS

Normative computational models of sensorimotor behavior based on optimal feedback control with signal-dependent noise (Todorov, 2005) have been able to account for many phenomena including online corrections, redundancy, synergies, and uncontrolled manifolds of movements. In past research, a cost function needed to be assumed for the respective task, and agreement between the predictions of optimal behavior by the model and empirical movement trajectories had to be assessed. However, not all behavioral goals may be known a priori and costs internal to the subject such as biomechanical or subje

Bayesian models have been successful in explaining sensorimotor behavior in humans and animals, yet it remains unclear whether they can also explain neural activity. Testing them using neural data is complicated by the fact that linking them to neural activity requires two key moving parts: (1) a hypothesis about which latent variable is represented by a neuron, or neural population (``generative model'') and (2) a hypothesis about how beliefs about a latent variable (``posterior'') are represented by neural responses. Here, we present two new methods that in conjunction allow us to, first, fa