Brain regions concerned with perceptual skills in tennis: An fMRI study

Sporting performance makes special demands on perceptual skills, but the neural mechanisms underlying such performance are little understood. We address this issue, making use of fMRI to identify the brain areas activated in viewing and responding to video sequences of tennis players, filmed from the opponent’s perspective. In a block-design, fMRI study, 9 novice tennis players watched video clips of tennis play. The main stimulus conditions were (1) serve sequences, (2) non-serve behaviour (ball bouncing) and (3) static control sequences. A button response was required indicating the direction of serve (left or right for serve sequences, middle button for non-serve and static sequences). By comparing responses to the three stimulus conditions, it was possible to identify two groups of brain regions responsive to different components of the task. Areas MT/MST and STS in the posterior part of the temporal lobe responded either to serve and to non-serve stimuli, relative to static controls. Serve sequences produced additional regions of activation in parietal lobe (bilateral IPL, right SPL) and in right frontal cortex (IFGd, IFGv), and these areas were not activated by non-serve sequences. These regions of parietal and frontal cortex have been implicated in a “mirror neuron” network in the human brain. It is concluded that the task of judgement of serve direction produces two different patterns of response: activations in MT/MST and STS concerned with primarily with the analysis of motion and body actions, and activations in parietal and frontal cortex associated specifically with the task of identification of direction of serve

The influence of expertise on brain activation of the action observation network during anticipation of tennis and volleyball serves

In many daily activities, and especially in sport, it is necessary to predict the effects of others' actions in order to initiate appropriate responses. Recently, researchers have suggested that the action-observation network (AON) including the cerebellum plays an essential role during such anticipation, particularly in sport expert performers. In the present study, we examined the influence of task-specific expertise on the AON by investigating differences between two expert groups trained in different sports while anticipating action effects. Altogether, 15 tennis and 16 volleyball experts anticipated the direction of observed tennis and volleyball serves while undergoing functional magnetic resonance imaging (fMRI). The expert group in each sport acted as novice controls in the other sport with which they had only little experience. When contrasting anticipation in both expertise conditions with the corresponding untrained sport, a stronger activation of AON areas (SPL, SMA), and particularly of cerebellar structures, was observed. Furthermore, the neural activation within the cerebellum and the SPL was linearly correlated with participant's anticipation performance, irrespective of the specific expertise. For the SPL, this relationship also holds when an expert performs a domain-specific anticipation task. Notably, the stronger activation of the cerebellum as well as of the SMA and the SPL in the expertise conditions suggests that experts rely on their more fine-tuned perceptual-motor representations that have improved during years of training when anticipating the effects of others' actions in their preferred sport. The association of activation within the SPL and the cerebellum with the task achievement suggests that these areas are the predominant brain sites involved in fast motor predictions. The SPL reflects the processing of domain-specific contextual information and the cerebellum the usage of a predictive internal model to solve the anticipation task. © 2014 Balser, Lorey, Pilgramm, Naumann, Kindermann, Stark, Zentgraf, Williams and Munzert

Cortical fMRI activation to opponents' body kinematics in sport-related anticipation: Expert-novice differences with normal and point-light video

This is the post-print version of the final paper published in Neuroscience Letters. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Badminton players of varying skill levels viewed normal and point-light video clips of opponents striking the shuttle towards the viewer; their task was to predict in which quadrant of the court the shuttle would land. In a whole-brain fMRI analysis we identified bilateral cortical networks sensitive to the anticipation task relative to control stimuli. This network is more extensive and localised than previously reported. Voxel clusters responding more strongly in experts than novices were associated with all task-sensitive areas, whereas voxels responding more strongly in novices were found outside these areas. Task-sensitive areas for normal and point-light video were very similar, whereas early visual areas responded differentially, indicating the primacy of kinematic information for sport-related anticipation.Research Grants Council of the Hong Kong Special Administrative Region, Chin

Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018, Accepte

Neuronal bases of structural coherence in contemporary dance observation

The neuronal processes underlying dance observation have been the focus of an increasing number of brain imaging studies over the past decade. However, the existing literature mainly dealt with effects of motor and visual expertise, whereas the neural and cognitive mechanisms that underlie the interpretation of dance choreographies remained unexplored. Hence, much attention has been given to the Action Observation Network (AON) whereas the role of other potentially relevant neuro-cognitive mechanisms such as mentalizing (theory of mind) or language (narrative comprehension) in dance understanding is yet to be elucidated. We report the results of an fMRI study where the structural coherence of short contemporary dance choreographies was manipulated parametrically using the same taped movement material. Our participants were all trained dancers. The whole-brain analysis argues that the interpretation of structurally coherent dance phrases involves a subpart (Superior Parietal) of the AON as well as mentalizing regions in the dorsomedial Prefrontal Cortex. An ROI analysis based on a similar study using linguistic materials (Pallier et al. 2011) suggests that structural processing in language and dance might share certain neural mechanisms