Visual adaptation to goal-directed hand actions

Prolonged exposure to visual stimuli, or adaptation, often results in an adaptation “aftereffect” which can profoundly distort our perception of subsequent visual stimuli. This technique has been commonly used to investigate mechanisms underlying our perception of simple visual stimuli, and more recently, of static faces. We tested whether humans would adapt to movies of hands grasping and placing different weight objects. After adapting to hands grasping light or heavy objects, subsequently perceived objects appeared relatively heavier, or lighter, respectively. The aftereffects increased logarithmically with adaptation action repetition and decayed logarithmically with time. Adaptation aftereffects also indicated that perception of actions relies predominantly on view-dependent mechanisms. Adapting to one action significantly influenced the perception of the opposite action. These aftereffects can only be explained by adaptation of mechanisms that take into account the presence/absence of the object in the hand. We tested if evidence on action processing mechanisms obtained using visual adaptation techniques confirms underlying neural processing. We recorded monkey superior temporal sulcus (STS) single-cell responses to hand actions. Cells sensitive to grasping or placing typically responded well to the opposite action; cells also responded during different phases of the actions. Cell responses were sensitive to the view of the action and were dependent upon the presence of the object in the scene. We show here that action processing mechanisms established using visual adaptation parallel the neural mechanisms revealed during recording from monkey STS. Visual adaptation techniques can thus be usefully employed to investigate brain mechanisms underlying action perception.Publisher PDFPeer reviewe

Neural development and sensorimotor control

What is the relationship between development of the nervous system and the emergence of voluntary motor behavior? This is the central question of the nature-nurture discussion that has intrigued child psychologists and pediatric neurologists for decades. This paper attempts to revisit this issue. Recent empirical evidence on how infants acquire multi-joint coordination and how children learn to adapt to novel force environments will be discussed with reference to the underlying development of the nervous system. The claim will be made that the developing human nervous system by no means constitutes an ideal controller. However, its redundancy, its ability to integrate multi-modal sensory information and motor commands and its facility of time-critical neural plasticity are features that may prove to be useful for the design of adaptive robots

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

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

Brief Report: Visuo-spatial Guidance of Movement during Gesture Imitation and Mirror Drawing in Children with Autism Spectrum Disorders

Thirteen autistic and 14 typically developing children (controls) imitated hand/arm gestures and performed mirror drawing; both tasks assessed ability to reorganize the relationship between spatial goals and the motor commands needed to acquire them. During imitation, children with autism were less accurate than controls in replicating hand shape, hand orientation, and number of constituent limb movements. During shape tracing, children with autism performed accurately with direct visual feedback, but when viewing their hand in a mirror, some children with autism generated fewer errors than controls whereas others performed much worse. Large mirror drawing errors correlated with hand orientation and hand shape errors in imitation, suggesting that visuospatial information processing deficits may contribute importantly to functional motor coordination deficits in autism

A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics