Action Intentions, Predictive Processing, and Mind Reading: Turning Goalkeepers Into Penalty Killers

The key to action control is one’s ability to adequately predict the consequences of one’s actions. Predictive processing theories assume that forward models enable rapid “preplay” to assess the match between predicted and intended action effects. Here we propose the novel hypothesis that “reading” another’s action intentions requires a rich forward model of that agent’s action. Such a forward model can be obtained and enriched through learning by either practice or simulation. Based on this notion, we ran a series of studies on soccer goalkeepers and novices, who predicted the intended direction of penalties being kicked at them in a computerized penalty-reading task. In line with hypotheses, extensive practice in penalty kicking improved performance in penalty reading among goalkeepers who had extensive prior experience in penalty blocking but not in penalty kicking. A robust benefit in penalty reading did not result from practice in kinesthetic motor imagery of penalty kicking in novice participants. To test whether goalkeepers actually use such penalty-kicking imagery in penalty reading, we trained a machine-learning classifier on multivariate fMRI activity patterns to distinguish motor-imagery-related from attention-related strategies during a penalty-imagery training task. We then applied that classifier to fMRI data related to a separate penalty-reading task and showed that 2/3 of all correctly read penalty kicks were classified as engaging the motor-imagery circuit rather than merely the attention circuit. This study provides initial evidence that, in order to read our opponent’s action intention, it helps to observe their action kinematics, and use our own forward model to predict the sensory consequences of “our” penalty kick if we were to produce these action kinematics ourselves. In sum, it takes practice as a penalty kicker to become a penalty killer

The Asian Art Society in the Netherlands. A centennial celebration

The Asian Art Society in the Netherlands: A Centennial CelebrationTHE ASIAN Art Society in the Netherlands (VVAK) was founded on June 29th, 1918 by a handful of men, who were keen to stimulate interest in art from Asia and to bring together art lovers in that field.[i] Ten years later, the organisation decided to start its own museum, which opened in 1932. Now—100 years after its foundation—the objects assembled by the Asian Art Society form the mainstay of the Rijksmuseum’s Asian art collection, and its members form a large group of dedicated enthusiasts.[i] P. Lunsingh Scheurleer, “Asian Art in the Rijksmuseum”, in Asian Art (Rijksmuseum collection book), Amsterdam, Rijksmuseum, 2014, pp. 8-23; M. Draak, “Chronicle of the Vereniging van Vrienden der Aziatische Kunst”, in P. Lunsingh Scheurleer, ed., Asiatic Art in the Rijksmuseum, Amsterdam, 1985, pp. 9-27; M. Fitski, “Japanese Art in the Westendorp-Osieck Collection”, Arts of Asia, Vol. 38, no. 4, July-August 2008 issue, pp. 48-57.Modern and Contemporary Studie

Integrability of a conducting elastic rod in a magnetic field

We consider the equilibrium equations for a conducting elastic rod placed in a uniform magnetic field, motivated by the problem of electrodynamic space tethers. When expressed in body coordinates the equations are found to sit in a hierarchy of non-canonical Hamiltonian systems involving an increasing number of vector fields. These systems, which include the classical Euler and Kirchhoff rods, are shown to be completely integrable in the case of a transversely isotropic rod; they are in fact generated by a Lax pair. For the magnetic rod this gives a physical interpretation to a previously proposed abstract nine-dimensional integrable system. We use the conserved quantities to reduce the equations to a four-dimensional canonical Hamiltonian system, allowing the geometry of the phase space to be investigated through Poincar\'e sections. In the special case where the force in the rod is aligned with the magnetic field the system turns out to be superintegrable, meaning that the phase space breaks down completely into periodic orbits, corresponding to straight twisted rods.Comment: 19 pages, 1 figur

High-throughput synchrotron X-ray diffraction for combinatorial phase mapping

Discovery of new materials drives the deployment of new technologies. Complex technological requirements demand precisely tailored material functionalities, and materials scientists are driven to search for these new materials in compositionally complex and often non-equilibrium spaces containing three, four or more elements. The phase behavior of these high-order composition spaces is mostly unknown and unexplored. High-throughput methods can offer strategies for efficiently searching complex and multi-dimensional material genomes for these much needed new materials and can also suggest a processing pathway for synthesizing them. However, high-throughput structural characterization is still relatively under-developed for rapid material discovery. Here, a synchrotron X-ray diffraction and fluorescence experiment for rapid measurement of both X-ray powder patterns and compositions for an array of samples in a material library is presented. The experiment is capable of measuring more than 5000 samples per day, as demonstrated by the acquisition of high-quality powder patterns in a bismuth-vanadium-iron oxide composition library. A detailed discussion of the scattering geometry and its ability to be tailored for different material systems is provided, with specific attention given to the characterization of fiber textured thin films. The described prototype facility is capable of meeting the structural characterization needs for the first generation of high-throughput material genomic searches

The detection of age groups by dynamic gait outcomes using machine learning approaches

Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the present study was to classify healthy young-middle aged, older adults and geriatric patients based on dynamic gait outcomes. Classification performance of three supervised machine learning methods was compared. From trunk 3D-accelerations of 239 subjects obtained during walking, 23 dynamic gait outcomes were calculated. Kernel Principal Component Analysis (KPCA) was applied for dimensionality reduction of the data for Support Vector Machine (SVM) classification. Random Forest (RF) and Artificial Neural Network (ANN) were applied to the 23 gait outcomes without prior data reduction. Classification accuracy of SVM was 89%, RF accuracy was 73%, and ANN accuracy was 90%. Gait outcomes that significantly contributed to classification included: Root Mean Square (Anterior-Posterior, Vertical), Cross Entropy (Medio-Lateral, Vertical), Lyapunov Exponent (Vertical), step regularity (Vertical) and gait speed. ANN is preferable due to the automated data reduction and significant gait outcome identification. For clinicians, these gait outcomes could be used for diagnosing subjects with mobility disabilities, fall risk and to monitor interventions. (This work was supported by Keep Control project, funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 721577.