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A Second-Order Adaptive Social-Behavioural Model for Individual and Duo Motor Learning
Abstract
For a video presentation, see the Self-Modeling Networks channel at https://www.youtube.com/watch?v=oRPUKaDizzo. This paper addresses computational analysis by psychological knowledge in motor learning of how people with certain personalities, alone and in pairs, are being influenced by several factors during their motor learning processes. To this end a second order adaptive network model was designed for the social and behavioural processes involved. Example simulations show how the model fits to different situations. Mathematical analysis was performed for verification and parameter tuning for validation .
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Athletes of the highest level in many sports (athletics, football, baseball, tennis, golf, rugby, skiing, gymnastics, swimming, basketball etc.) talk about the importance of visualising technical execution both during the training period and in competition (during warm-ups, during breaks in official games, before throws, jumps, free kicks etc.).Visualisation techniques can improve motor skills, grow muscle strength, increase self-confidence, attention concentration and decrease anxiety. Through the use of imagery, pain management, endurance, performance motivation and physical performance can also be enhanced in athletes. To achieve the best results, visualisation techniques should include the five major senses (touch, hearing, sight, smell and taste) and should consider key aspects such as perspective, emotion, environment, task and timing. Mental rehearsal (or visualisation) is powerful because the subconscious processes the experience as a real one (by firing those neurons that are responsible for skill acquisition), makes the person calmer and more adapted to stressful situations, and can speed up the learning process in athletes and not only. One hour of mental training a day in 6-10 sequences has a special benefit that cannot be obtained by any other means. By applying both guided imagery techniques and practice, athletes design their mental road maps for success.
Videos of lectures on several chapters of this book can be found at: https://www.youtube.com/playlist?list=PLtJH8O7BvdydRVu9RXuhdtAo2S2wMPtgp. For more applications, see the Self-Modeling Networks channel at https://www.youtube.com/@self-modelingnetworks4255. This book addresses the challenging topic of modeling (multi-order) adaptive dynamical systems, which often have inherently complex behaviour. This is addressed by using their network representations. Networks by themselves usually can be modeled using a neat, declarative and conceptually transparent Network-Oriented Modeling approach. For adaptive networks changing the network’s structure, it is different; often separate procedural specifications are added for the adaptation process. This leaves you with a less transparent, hybrid specification, part of which often is more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach by which designing adaptive network models becomes much easier, as also the adaptation processes are modeled in a neat, declarative and conceptually transparent network-oriented manner, like the base network itself. Due to this dedicated overall Network-Oriented Modeling approach, no procedural, algorithmic or programming skills are needed to design complex adaptive network models.
A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, as adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive too, can be modeled just as easily; for example, this can be applied to model metaplasticity from Cognitive Neuroscience. The usefulness of this approach is illustrated in the book by many examples of complex (higher-order) adaptive network models for a wide variety of biological, mental and social processes.
The book has been written with multidisciplinary Master and Ph.D. students in mind without assuming much prior knowledge, although also some elementary mathematical analysis is not completely avoided. The detailed presentation makes that it can be used as an introduction in Network-Oriented Modelling for adaptive networks. Sometimes overlap between chapters can be found in order to make it easier to read each chapter separately. In each of the chapters, in the Discussion section, specific publications and authors are indicated that relate to the material presented in the chapter. The specific mathematical details concerning difference and differential equations have been concentrated in Chapters 10 to 15 in Part IV and Part V, which easily can be skipped if desired. For a modeler who just wants to use this modeling approach, Chapters 1 to 9 provide a good introduction.
The material in this book is being used in teaching undergraduate and graduate students with a multidisciplinary background or interest. Lecturers can contact me for additional material such as slides, assignments, and software. Videos of lectures for many of the chapters can be found at https://www.youtube.com/watch?v=8Nqp_dEIipU&list=PLF-Ldc28P1zUjk49iRnXYk4R-Jm4lkv2b.
Skilled performers such as athletes or musicians can improve their performance by imagining the actions or sensory outcomes associated with their skill. Performers vary widely in their auditory and motor imagery abilities, and these individual differences influence sensorimotor learning. It is unknown whether imagery abilities influence both memory encoding and retrieval. We examined how auditory and motor imagery abilities influence musicians' encoding (during Learning, as they practiced novel melodies), and retrieval (during Recall of those melodies). Pianists learned melodies by listening without performing (auditory learning) or performing without sound (motor learning); following Learning, pianists performed the melodies from memory with auditory feedback (Recall). During either Learning (Experiment 1) or Recall (Experiment 2), pianists experienced either auditory interference, motor interference, or no interference. Pitch accuracy (percentage of correct pitches produced) and temporal regularity (variability of quarter-note interonset intervals) were measured at Recall. Independent tests measured auditory and motor imagery skills. Pianists' pitch accuracy was higher following auditory learning than following motor learning and lower in motor interference conditions (Experiments 1 and 2). Both auditory and motor imagery skills improved pitch accuracy overall. Auditory imagery skills modulated pitch accuracy encoding (Experiment 1): Higher auditory imagery skill corresponded to higher pitch accuracy following auditory learning with auditory or motor interference, and following motor learning with motor or no interference. These findings suggest that auditory imagery abilities decrease vulnerability to interference and compensate for missing auditory feedback at encoding. Auditory imagery skills also influenced temporal regularity at retrieval (Experiment 2): Higher auditory imagery skill predicted greater temporal regularity during Recall in the presence of auditory interference. Motor imagery aided pitch accuracy overall when interference conditions were manipulated at encoding (Experiment 1) but not at retrieval (Experiment 2). Thus, skilled performers' imagery abilities had distinct influences on encoding and retrieval of musical sequences.
While there is ample evidence that motor imagery (MI) may improve motor performance, the models that have been proposed are mainly focused on some of the key components required to ascertain effectiveness, but do not give an extended overview of MI functions. This article reviews the models of greatest conceptual viability, and aims at describing the fields in which MI may play a crucial role, by integrating these functions in a unique model within motor performance and recovery. The detailed description of the imagery-based interventions considers distinct outcomes: (i) motor learning and performance, (ii) motivation, self-confidence and anxiety, (iii) strategies and problem-solving, and (iv) injury rehabilitation. The Motor Imagery Integrative Model of Imagery in Sport (MIIMS) may be used as a global guiding framework in the field of MI studies to develop more effective imagery interventions by covering the major key components of MI training related to the outcome achieved by athletes.
Introduction:
In recent decades there has been a special interest in theories that could explain the regulation of motor control, and their applications. These theories are often based on models of brain function, philosophically reflecting different criteria on how movement is controlled by the brain, each being emphasised in different neural components of the movement. The concept of motor learning, regarded as the set of internal processes associated with practice and experience that produce relatively permanent changes in the ability to produce motor activities through a specific skill, is also relevant in the context of neuroscience. Thus, both motor control and learning are seen as key fields of study for health professionals in the field of neuro-rehabilitation.
Development:
The major theories of motor control are described, which include, motor programming theory, systems theory, the theory of dynamic action, and the theory of parallel distributed processing, as well as the factors that influence motor learning and its applications in neuro-rehabilitation.
Conclusions:
At present there is no consensus on which theory or model defines the regulations to explain motor control. Theories of motor learning should be the basis for motor rehabilitation. The new research should apply the knowledge generated in the fields of control and motor learning in neuro-rehabilitation.
The anterior cingulate cortex (ACC) is a critical component of the human mediofrontal neural circuit that monitors ongoing
processing in the cognitive system for signs of erroneous outcomes. Here, we show that the consumption of alcohol in moderate
doses induces a significant deterioration of the ability to detect the activation of erroneous responses as reflected in the
amplitude of brain electrical activity associated with the ACC. This impairment was accompanied by failures to instigate performance
adjustments after these errors. These findings offer insights into how the effects of alcohol on mediofrontal brain function
may result in compromised performance.
A central tenet of economics is that individuals respond to incentives. For psychologists and sociologists, in contrast, rewards
and punishments are often counterproductive, because they undermine “intrinsic motivation”. We reconcile these two views,
showing how performance incentives offered by an informed principal (manager, teacher, parent) can adversely impact an agent's
(worker, child) perception of the task, or of his own abilities. Incentives are then only weak reinforcers in the short run,
and negative reinforcers in the long run. We also study the effects of empowerment, help and excuses on motivation, as well
as situations of ego bashing reflecting a battle for dominance within a relationship.
Motivation has characteristics of both states and trait. Much of motivation represents a specific state relative to a particular goal objective or value. Motivation represents a state of readiness characterized, when in its trait form, as the highly automated and practiced approach behaviors associated with a class of environmental stimuli. Behavior looks motivated if it is aggressively pursuing a particular outcome. A person is either acting in a motivated fashion or else has the potential to demonstrate those actions if the circumstances are right. These conditions represent two distinct neural networks and may be best thought of as separate processes.
The traditional baseball instruction strategies were mainly conducted by the instructors with oral explanation and exemplification while students had to improve their performance in athletic activities through continuous practice. During the learning process of athletic skills, students oftentimes posed less confidence due to unskilled body movement resulting in lower achievement sense. Finally, they started to reject the engagement in relevant athletic activities and even never practice anymore. Therefore, this research aimed to explore the influence on the learning motivation and the performance of athletic skills made by students in the conventionally instructive mode by introducing the Computer-Aided Design (CAD) instruction strategies of the kinect baseball learning system. Research results indicated: (1) after the kinect baseball learning system was introduced into instruction, it positively affected the learning motivation of students; (2) after the kinect baseball learning system was introduced into instruction, it positively affected the performance of athletic skills of students.
Discusses neural activity and stimulation crucial in fetal brain development and the formation of the mind. Focuses on activity-dependent remodeling related to development of the visual system and retinal activity. (MCO)
In this paper, we review experimental evidence for a novel form of persistent synaptic plasticity we call metaplasticity. Metaplasticity is induced by synaptic or cellular activity, but it is not necessarily expressed as a change in the efficacy of normal synaptic transmission. Instead, it is manifest as a change in the ability to induce subsequent synaptic plasticity, such as long-term potentiation or depression. Thus, metaplasticity is a higher-order form of synaptic plasticity. Metaplasticity might involve alterations in NMDA-receptor function in some cases, but there are many other candidate mechanisms. The induction of metaplasticity complicates the interpretation of many commonly studied aspects of synaptic plasticity, such as saturation and biochemical correlates.
Much of neurorehabilitation rests on the assumption that patients can improve with practice. This review will focus on arm movements and address the following questions: (i) What is motor learning? (ii) Do patients with hemiparesis have a learning deficit? (iii) Is recovery after injury a form of motor learning? (iv) Are approaches based on motor learning principles useful for rehabilitation?
Motor learning can be broken into kinematic and dynamic components. Studies in healthy subjects suggest that retention of motor learning is best accomplished with variable training schedules. Animal models and functional imaging in humans show that the mature brain can undergo plastic changes during both learning and recovery. Quantitative motor control approaches allow differentiation between compensation and true recovery, although both improve with practice. Several promising new rehabilitation approaches are based on theories of motor learning. These include impairment oriented-training (IOT), constraint-induced movement therapy (CIMT), electromyogram (EMG)-triggered neuromuscular stimulation, robotic interactive therapy and virtual reality (VR).
Motor learning mechanisms are operative during spontaneous stroke recovery and interact with rehabilitative training. For optimal results, rehabilitation techniques should be geared towards patients' specific motor deficits and possibly combined, for example, CIMT with VR. Two critical questions that should always be asked of a rehabilitation technique are whether gains persist for a significant period after training and whether they generalize to untrained tasks.
Motor learning and auditory information: Is movement sonification efficient
- A O Effenberg
- A Weber
- K Mattes
- U Fehse
- H Mechling
Effenberg, A.O., Weber, A., Mattes, K., Fehse, U., Mechling, H.: Motor learning and auditory
information: Is movement sonification efficient?. J. Sport Exerc. Psychol. 29 (2007)
Why conceptualizations of talent matter: implications for skill acquisition and talent identification and development. In Routledge Handbook of Talent Identification and Development in Sport
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