Above Real Time Training for Team Invasion Sport Skills

Lorains, Megan; MacMahon, Clare; Ball, Kevin; Mahoney, Josh

doi:10.1260/1747-9541.6.4.537

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…Another possibility for explaining the asymmetric impact timing is that the faster video may benefit the experienced batters. Previous researches have shown that sports experts identify faster video as more gamelike than normal or slower videos (Lorains et al, 2011). In addition, this also promotes the automaticity of their decision-making process and improves accuracy of decision (Lorains et al, 2013).…”

Section: Discussionmentioning

confidence: 93%

Effect of Manipulating Advanced Kinematic Information on Hitting Movement Prediction, Perception, and Action

Takamido

Yokoyama

Yamamoto

2020

Research Quarterly for Exercise and Sport

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Purpose: This study examined the effect of manipulating advanced kinematic information about opponents' pitching movement on ball speed prediction, ball speed perception, and impact timing errors under strict temporal constraints (i.e., a softball game). Method: Three experiments were conducted using visual stimuli consisting of varied kinematic information-different pitching movements with the same ball trajectory. In Experiment 1, participants observed the pitching movement of the visual stimuli and predicted pitched ball speed as a two-interval forced choice discrimination task (2IFC). In Experiment 2, participants observed both the pitching movement and ball trajectory, and evaluated the pitched ball speed as a 2IFC. In Experiments 3a and 3b, they tried to swing against the pitched ball presented on the screen as accurately as possible with regard to timing. Results: Batters tended to predict the ball was moving faster when the pitching movement was faster (Experiment 1). Incorrectly predicting the ball speed due to the difference in advanced kinematic information also biased batters' perception of the speed (Experiment 2), and this biased prediction yielded congruent impact timing (Experiment 3a). The impact timing error of naive participants also was affected by kinematic information (Experiment 3b). Conclusion: Limitations of this study (representative task design, sample size, and experimental procedures) notwithstanding, results indicate that, under strict temporal constraints, batters' perceptions and actions are sensitive to advanced kinematic information, which could lead to biased perceptions and actions.

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Section: Discussionmentioning

confidence: 93%

Effect of Manipulating Advanced Kinematic Information on Hitting Movement Prediction, Perception, and Action

Takamido

Yokoyama

Yamamoto

2020

Research Quarterly for Exercise and Sport

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“…There are many potential ways to quantify physical load in practice, as evidenced by the great diversity of metrics used in high-performance sport such as high velocity running measurements, algorithmically determined load (such as those calculated by many commercial GPS units), sessional ratings of perceived exertion and others [ 35 , 43 , 44 ]. Similarly, there are several options for defining physical fidelity, such as matching either physical output [ 45 ] or the frequency of constraint combinations [ 12 ], or even subjective measurement of how game-like a scenario looks and feels to an athlete [ 46 ]. The variety of possible measurement highlights the potential flexibility that this total workload approach provides.…”

Section: New Considerations For Measuring the Demands Of Representati...mentioning

confidence: 99%

Many Pieces to the Puzzle: A New Holistic Workload Approach to Designing Practice in Sports

2023

Self Cite

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Representative learning design (RLD) in sport is a well-established concept in both theory and practice. The goal of RLD is to faithfully replicate competition environments in training settings to benefit improvement in athletic performance. There is currently little research that considers how representative an activity needs to be to facilitate learning transfer, and how that level of representativeness might fluctuate between activities or sessions, and across competitive cycles. Similarly, there is no existing research that specifically considers the elevated workload (in cognitive and physical load) of highly representative training, and the potential impacts of chronic overuse of these highly demanding activities. This paper addresses these limitations, making a case for the application of RLD that considers the level of representativeness (fidelity) and the demands placed on athletes (load) from both a cognitive and physical perspective. This paper also suggests several categorisations of training activities that are based on their relative representativeness, level of imposed demands, and the intended outcomes of the activity with reference to the perception–action cycle. The two core concepts of fidelity and load are combined for a new approach to representative training that allows practitioners to balance the benefits of representative training with the risks of imposing excessive load on athletes.

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“…During the advanced stages, time pressure can make memory retrieval faster, more precise and more resistant to the mentioned stressors. It has been shown that simulations that use above real‐time training (ARTT) can better emulate the tasks that have a demanding nature in reality, such as military training or high‐performance sports (Donderi et al, 2012; Lorains et al, 2011). ARTT leads to a better transfer and retention while reducing the training time (Guckenberger et al, 1993; Lorains et al, 2013).…”

Section: Key Attributes Of Automaticity Acquisitionmentioning

confidence: 99%

A three‐step model for the gamification of training and automaticity acquisition

Jamshidifarsani

Tamayo-Serrano

Garbaya

et al. 2021

Computer Assisted Learning

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Training design for automatic skills has a vast domain of application, such as education, physical and cognitive rehabilitation, as well as sports, arts and professional training. Gamification concept used in technology‐assisted training has the potential to increase motivation, engagement and adherence to the training programme. Currently, the general gamification models of learning, did not take into account the temporal specificity of the game elements for automaticity acquisition training. In order to address this problem, an extensive overview of the key training attributes that impact automaticity acquisition was carried out. Then, based on this review, the three steps of a proposed model were presented. The first step of this model, named Task Analytics, helps with task‐specific training decisions. The second step provides descriptive and prescriptive approaches for the three phases of automaticity acquisition (fast learning, slow learning and automatization). The descriptive part characterizes each phase using psychological and performance‐related qualities, while the prescriptive part recommends the appropriate training elements for each phase. Based on the prescriptive part, a game‐design model is proposed in the third step, which classifies the game mechanics and maps them onto each phase of automaticity acquisition. Finally, to validate this approach, a mobile game was designed based on the proposed gamification model, and it was compared to control design. The two approaches are tested with 49 participants. The results showed that the experimental group had a significantly better engagement and higher performance. Furthermore, the experimental group showed significantly better performance in a multitasking challenge designed to evaluate the automaticity. The main contribution of this article is the proposed game design model that takes into account the temporal specificity of game elements during the acquisition of automaticity.

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Above Real Time Training for Team Invasion Sport Skills

Cited by 14 publications

References 21 publications

Effect of Manipulating Advanced Kinematic Information on Hitting Movement Prediction, Perception, and Action

Effect of Manipulating Advanced Kinematic Information on Hitting Movement Prediction, Perception, and Action

Many Pieces to the Puzzle: A New Holistic Workload Approach to Designing Practice in Sports

A three‐step model for the gamification of training and automaticity acquisition

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