Continuous evaluation of cost-to-go for flexible reaching control and online decisions

Emerging perspectives for the study of the neural basis of motor behaviour

The 33rd Annual Meeting of the Society for the Neural Control of Movement (NCM) brought together over 500 experts to discuss recent advancements in motor control. This article highlights key topics from the conference, including the foundational mechanisms of motor control, the ongoing debate over the context-dependency of feedforward and feedback processes, and the interplay between motor and cognitive functions in learning, memory, and decision-making. It also presents innovative methods for studying movement in complex, real-world environments.

Task-dependent coarticulation of movement sequences

Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to the coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here, we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co-articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.

Biomechanical costs influence decisions made during ongoing actions

Accurate interaction with the environment relies on the integration of external information about the spatial layout of potential actions and knowledge of their costs and benefits. Previous studies have shown that when given a choice between voluntary reaching movements, humans tend to prefer actions with lower biomechanical costs. However, these studies primarily focused on decisions made before the onset of movement ("decide-then-act" scenarios), and it is not known to what extent their conclusions generalize to many real-life situations, in which decisions occur during ongoing actions ("decide-while-acting"). For example, one recent study found that biomechanical costs did not influence decisions to switch from a continuous manual tracking movement to a point-to-point movement, suggesting that biomechanical costs may be disregarded in decide-while-acting scenarios. To better understand this surprising result, we designed an experiment in which participants were faced with the decision between continuing to track a target moving along a straight path or changing paths to track a new target that gradually moved along a direction that deviated from the initial one. We manipulated tracking direction, angular deviation rate, and side of deviation, allowing us to compare scenarios where biomechanical costs favored either continuing or changing the path. Crucially, here the choice was always between two continuous tracking actions. Our results show that in this situation decisions clearly took biomechanical costs into account. Thus we conclude that biomechanics are not disregarded during decide-while-acting scenarios but rather that cost comparisons can only be made between similar types of actions.NEW & NOTEWORTHY In this study, we aim to shed light on how biomechanical factors influence decisions made during ongoing actions. Previous work suggested that decisions made during actions disregard biomechanical costs, in contrast to decisions made before movement. Our results challenge that proposal and suggest instead that the effect of biomechanical factors is dependent on the types of actions being compared (e.g., continuous tracking vs. point-to-point reaching). These findings contribute to our understanding of the dynamic interplay between biomechanical considerations and action choices during ongoing interactions with the environment.

Toward a neuroscience of natural behavior

One of the most exciting new developments in systems neuroscience is the progress being made toward neurophysiological experiments that move beyond simplified laboratory settings and address the richness of natural behavior. This is enabled by technological advances such as wireless recording in freely moving animals, automated quantification of behavior, and new methods for analyzing large data sets. Beyond new empirical methods and data, however, there is also a need for new theories and concepts to interpret that data. Such theories need to address the particular challenges of natural behavior, which often differ significantly from the scenarios studied in traditional laboratory settings. Here, we discuss some strategies for developing such novel theories and concepts and some example hypotheses being proposed.

Human reaching control in dynamic environments

Closed-loop models of movement control have attracted growing interest in how the nervous system transforms sensory information into motor commands, and several brain structures have been identified as neural substrates for these computational operations. Recently, several studies have focused on how these models need to be updated when environmental parameters change. Current evidence suggests that when the task changes, rapid control updates enable flexible modifications of current actions and online decisions. At the same time, when movement dynamics change, humans use different strategies based on a combination of adaptation and modulation of controller sensitivity to exogenous perturbations (robust control). This review proposes a unified framework to capture these results based on online estimation of model parameters with dynamic updates in control. The reviewed studies also identify the time scales of associated behavioral mechanisms to guide future research on the neural basis of movement control.