Representative learning design and functionality of research and practice in sport

Egon Brunswik proposed the concept of “representative design” for psychological experimentation, which has historically been overlooked or confused with another of Brunswik’s terms, ecological validity. In this article, we reiterate the distinc­tion between these two important concepts and highlight the relevance of the term representative design for sports psychology, practice, and experimental design. We draw links with ideas on learning design in the constraints-led approach to motor learning and nonlinear pedagogy. We propose the adoption of a new term, repre­sentative learning design, to help sport scientists, experimental psychologists, and pedagogues recognize the potential application of Brunswik’s original concepts, and to ensure functionality and action fidelity in training and learning environments.

Workflow patterns put into context

In his paper “Approaches to Modeling Business Processes. A Critical Analysis of BPMN, Workflow Patterns and YAWL”, Egon Börger criticizes the work of the Workflow Patterns Initiative in a rather provocative manner. Although the workflow patterns and YAWL are well established and frequently used, Börger seems to misunderstand the goals and contributions of the Workflow Patterns Initiative. Therefore, we put the workflow patterns and YAWL in their historic context. Moreover, we address some of the criticism of Börger by pointing out the real purpose of the workflow patterns and their relationship to formal languages (Petri nets) and real-life WFM/BPM systems.

Separable spatiotemporal priors for convex reconstruction of time-varying 3D point clouds

Reconstructing 3D motion data is highly under-constrained due to several common sources of data loss during measurement, such as projection, occlusion, or miscorrespondence. We present a statistical model of 3D motion data, based on the Kronecker structure of the spatiotemporal covariance of natural motion, as a prior on 3D motion. This prior is expressed as a matrix normal distribution, composed of separable and compact row and column covariances. We relate the marginals of the distribution to the shape, trajectory, and shape-trajectory models of prior art. When the marginal shape distribution is not available from training data, we show how placing a hierarchical prior over shapes results in a convex MAP solution in terms of the trace-norm. The matrix normal distribution, fit to a single sequence, outperforms state-of-the-art methods at reconstructing 3D motion data in the presence of significant data loss, while providing covariance estimates of the imputed points.