Reckoning the Moment of Reckoning in Spontaneous Voluntary Movement

One question that naturally arises is: When, if at all, along the time course of the RP does the brain make the final commitment to initiate movement? Is there a point of no return after which the sequence of action potentials becomes “ballistic” and movement, although not yet happening, can no longer be aborted? This is the question that Schultze-Kraft et al. (9) ask through a clever experiment involving a direct brain–computer interface (BCI). On-line detection of the RP allowed them to present a stop signal when the probability of an impending movement was high. This process afforded the authors a unique perspective on the inhibition of voluntary, uncued actions

Why we may not find intentions in the brain

Intentions are commonly conceived of as discrete mental states that are the direct cause of actions. In the last several decades, neuroscientists have taken up the project of finding the neural implementation of intentions, and a number of areas have been posited as implementing these states. We argue, however, that the processes underlying action initiation and control are considerably more dynamic and context sensitive than the concept of intention can allow for. Therefore, adopting the notion of ‘intention’ in neuroscientific explanations can easily lead to misinterpretation of the data, and can negatively influence investigation into the neural correlates of intentional action.We suggest reinterpreting the mechanisms underlying intentional action, and we will discuss the elements that such a reinterpretation needs to account for

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Nowhere and Everywhere: The Causal Origin of Voluntary Action

The idea that intentions make the difference between voluntary and non-voluntary behaviors is simple and intuitive. At the same time, we lack an understanding of how voluntary actions actually come about, and the unquestioned appeal to intentions as discrete causes of actions offers little if anything in the way of an answer. We cite evidence suggesting that the origin of actions varies depending on context and effector, and argue that actions emerge from a causal web in the brain, rather than a central origin of intentional action. We argue that this causal web need not be confined to the central nervous system, and that proprioceptive feedback might play a counterintuitive role in the decision process. Finally we argue that the complex and dynamic origins of voluntary action and their interplay with the brain's propensity to predict the immediate future are better studied using a dynamical systems approach

Single-pulse transcranial magnetic stimulation reveals contribution of premotor cortex to object shape recognition

Action Contro

Hierarchies in action and motor control

Contains fulltext : 102587.pdf (publisher's version ) (Open Access)In analyses of the motor system, two hierarchies are often posited: The first—the action hierarchy—is a decomposition of an action into subactions and sub-subactions. The second—the control hierarchy—is a postulated hierarchy in the neural control processes that are supposed to bring about the action. A general assumption in cognitive neuroscience is that these two hierarchies are internally consistent and provide complementary descriptions of neuronal control processes. In this article, we suggest that neither offers a complete explanation and that they cannot be reconciled in a logical or conceptually coherent way. Furthermore, neither pays proper attention to the dynamics and temporal aspects of neural control processes. We will explore an alternative hierarchical organization in which causality is inherent in the dynamics over time. Specifically, high levels of the hierarchy encode more stable (goal-related) representations, whereas lower levels represent more transient (actions and motor acts) kinematics. If employed properly, a hierarchy based on this latter principle of temporal extension is not subject to the problems that plague the traditional accounts.10 p