Energetics of interlimb coordination

While the traditional dependent variables of motor skill learning are accuracy and consistency of movement outcome, there has been increasing interest in aspects of motor performance that are described as reflecting the ‘energetics’ of motor behaviour. One defining characteristic of skilled motor performance is the ability to complete the task with minimum energy expenditure (Sparrow & Newell, 1998). A further consideration is that movements also have costs in terms of cognitive ‘effort’ or ‘energy’. The present project extends previous work on energy expenditure and motor skill learning within a coordination dynamics framework. From the dynamic pattern perspective, a coordination pattern lowest on the 11KB model potential curve (Haken, Kelso & Bunz, 1985) is more stable and least energy is required to maintain pattern stability (Temprado, Zanone, Monno & Laurent, 1999). Two experiments investigated the learning of stable and unstable coordination patterns with high metabolic energy demand. An experimental task was devised by positioning two cycle ergometers side-by-side, placing one foot on each, with the pedals free to move independently at any metronome-paced relative phase, Experiment 1 investigated practice-related changes to oxygen consumption, heart rate, relative phase, reaction time and muscle activation (EMG) as participants practiced anti-phase, in-phase and 90°-phase cycling. Across six practice trials metabolic energy cost reduced and AE and VE of relative phase declined. The trend in the metabolic and reaction time data and percent co-contraction of muscles was for the in-phase cycling to demonstrate the highest values, anti-phase the lowest and 90°-phase cycling in-between. It was found that anti- and in-phase cycling were both kinematically stable but anti-phase coordination revealed significantly lower metabolic energy cost. It was, therefore, postulated that of two equally stable coordination patterns, that associated with lower metabolic energy expenditure would constitute a stronger attractor. Experiment 2 was designed to determine whether a lower or higher energy-demanding coordination pattern was a stronger attractor by scanning the attractor layout at thirty-degree intervals from 0° to 330°. The initial attractor layout revealed that in-phase was most stable and accurate, but the remaining coordination patterns were attracted to the low energy cost anti-phase cycling. In Experiment 2 only 90°- phase cycling was practiced with a post-test attractor layout scan revealing that 90°-phase and its symmetrical partner 270°-phase had become attractors of other coordination patterns. Consistent with Experiment 1, practicing 90°-phase cycling revealed a decline in AE and VE and a reduction in metabolic and cognitive cost. Practicing 90°-phase cycling did not, however, destabilise the in-phase or anti-phase coordination patterns either kinematically or energetically. In summary, the findings suggest that metabolic and mental energy can be considered different representations of a ‘global’ energy expenditure or ‘energetic’ phenomenon underlying human coordination. The hypothesis that preferred coordination patterns emerge as stable, low-energy solutions to the problem of inter-and intra-limb coordination is supported here in showing that the low-energy minimum of coordination dynamics is also an energetic minimum

Action and behavior: a free-energy formulation

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network

It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences. Although many existing robotics studies use a forward model framework, there are generalization issues with high degrees of freedom. The current study shows that the predictive coding (PC) and active inference (AIF) frameworks, which employ a generative model, can develop better generalization by learning a prior distribution in a low dimensional latent state space representing probabilistic structures extracted from well habituated sensory-motor trajectories. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound, while goal-directed planning is accomplished by inferring latent variables for maximizing the estimated lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data by setting an intermediate value for a regularization coefficient. Furthermore, comparative simulation results show that the proposed model outperforms a conventional forward model in goal-directed planning, due to the learned prior confining the search of motor plans within the range of habituated trajectories.Comment: 30 pages, 19 figure