Initiating technical refinements in high-level golfers: Evidence for contradictory procedures

When developing motor skills there are several outcomes available to an athlete depending on their skill status and needs. Whereas the skill acquisition and performance literature is abundant, an under-researched outcome relates to the refinement of already acquired and well-established skills. Contrary to current recommendations for athletes to employ an external focus of attention and a representative practice design, Carson and Collins’ (2011) [Refining and regaining skills in fixation/diversification stage performers: The Five-A Model. International Review of Sport and Exercise Psychology, 4, 146–167. doi:10.1080/1750984x.2011.613682] Five-A Model requires an initial narrowed internal focus on the technical aspect needing refinement: the implication being that environments which limit external sources of information would be beneficial to achieving this task. Therefore, the purpose of this paper was to (1) provide a literature-based explanation for why techniques counter to current recommendations may be (temporarily) appropriate within the skill refinement process and (2) provide empirical evidence for such efficacy. Kinematic data and self-perception reports are provided from high level golfers attempting to consciously initiate technical refinements while executing shots onto a driving range and into a close proximity net (i.e. with limited knowledge of results). It was hypothesised that greater control over intended refinements would occur when environmental stimuli were reduced in the most unrepresentative practice condition (i.e. hitting into a net). Results confirmed this, as evidenced by reduced intra-individual movement variability for all participants’ individual refinements, despite little or no difference in mental effort reported. This research offers coaches guidance when working with performers who may find conscious recall difficult during the skill refinement process

Continuity in cognition

Designing for continuous interaction requires designers to consider the way in which human users can perceive and evaluate an artefact’s observable behaviour, in order to make inferences about its state and plan, and execute their own continuous behaviour. Understanding the human point of view in continuous interaction requires an understanding of human causal reasoning, of the way in which humans perceive and structure the world, and of human cognition. We present a framework for representing human cognition, and show briefly how it relates to the analysis of structure in continuous interaction, and the ways in which it may be applied in design

VirtualHome: Simulating Household Activities via Programs

In this paper, we are interested in modeling complex activities that occur in a typical household. We propose to use programs, i.e., sequences of atomic actions and interactions, as a high level representation of complex tasks. Programs are interesting because they provide a non-ambiguous representation of a task, and allow agents to execute them. However, nowadays, there is no database providing this type of information. Towards this goal, we first crowd-source programs for a variety of activities that happen in people's homes, via a game-like interface used for teaching kids how to code. Using the collected dataset, we show how we can learn to extract programs directly from natural language descriptions or from videos. We then implement the most common atomic (inter)actions in the Unity3D game engine, and use our programs to "drive" an artificial agent to execute tasks in a simulated household environment. Our VirtualHome simulator allows us to create a large activity video dataset with rich ground-truth, enabling training and testing of video understanding models. We further showcase examples of our agent performing tasks in our VirtualHome based on language descriptions.Comment: CVPR 2018 (Oral

Model-based vision for space applications

This paper describes a method for tracking moving image features by combining spatial and temporal edge information with model based feature information. The algorithm updates the two-dimensional position of object features by correlating predicted model features with current image data. The results of the correlation process are used to compute an updated model. The algorithm makes use of a high temporal sampling rate with respect to spatial changes of the image features and operates in a real-time multiprocessing environment. Preliminary results demonstrate successful tracking for image feature velocities between 1.1 and 4.5 pixels every image frame. This work has applications for docking, assembly, retrieval of floating objects and a host of other space-related tasks

Implementation of intelligent manufacturing algorithms in agile architectures for production: world models for systems incorporating binary and continuous variables

Agile Manufacturing, Intelligent systems, AlgorithmsBuilding in previous works of the authors, the present paper focuses on the extension of the Algorithms described for the specific case study of binary systems (systems in which each variable can take one out of two values), to cases that also incorporate continuous variables (those which can take any continuous value within a range). This extension of the Algorithms makes possible the incorporation of new possibilities and functionalities for the treatment of the information received by the sensors of the manufacturing systems and in particular reduce the number of variables in which to monitor the states and costs of execution. The Construction of World Models based on this logical theory -that incorporates the knowledge derived from the results of a set of experiments conducted by the system utilizing a set of different algorithms- is applicable to a wide range of production systems topologies, which is also visited in the present work.Postprint (published version