Interpersonal Pattern Dynamics and Adaptive Behavior in Multiagent Neurobiological Systems: Conceptual Model and Data

Passos, Pedro; Araújo, Duarte; Davids, Keith; Gouveia, Luís F.; Serpa, Sidónio; Milho, João; Fonseca, Sofia

doi:10.3200/35-08-061

Cited by 101 publications

(108 citation statements)

References 39 publications

(61 reference statements)

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“…In the current state-of-the-art, only a few papers have analysed the variability of displacement of football players [13][14][15]. All these studies focused on the variability within each sub-phase (e.g., 1 vs. 1 player, 2 vs. 2 players) without considering the full match dynamics (e.g., 11-a-side game).…”

Section: Variability Analysis In Sportmentioning

confidence: 99%

Dynamical Stability and Predictability of Football Players: The Study of One Match

Couceiro

Clemente

Martins

et al. 2014

Entropy

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Abstract:The game of football demands new computational approaches to measure individual and collective performance. Understanding the phenomena involved in the game may foster the identification of strengths and weaknesses, not only of each player, but also of the whole team. The development of assertive quantitative methodologies constitutes a key element in sports training. In football, the predictability and stability inherent in the motion of a given player may be seen as one of the most important concepts to fully characterise the variability of the whole team. This paper characterises the predictability and stability levels of players during an official football match. A Fractional Calculus (FC) approach to define a player's trajectory. By applying FC, one can benefit from newly OPEN ACCESSEntropy 2014, 16 646 considered modeling perspectives, such as the fractional coefficient, to estimate a player's predictability and stability. This paper also formulates the concept of attraction domain, related to the tactical region of each player, inspired by stability theory principles. To compare the variability inherent in the player's process variables (e.g., distance covered) and to assess his predictability and stability, entropy measures are considered. Experimental results suggest that the most predictable player is the goalkeeper while, conversely, the most unpredictable players are the midfielders. We also conclude that, despite his predictability, the goalkeeper is the most unstable player, while lateral defenders are the most stable during the match.

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Section: Variability Analysis In Sportmentioning

confidence: 99%

Dynamical Stability and Predictability of Football Players: The Study of One Match

Couceiro

Clemente

Martins

et al. 2014

Entropy

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“…It necessarily must omit progress in a number of significant areas. These include investigations of (a) movements that embody the cognitive coordination required for successful interpersonal communication, 88,89 (b) decision-making in team sports, 33,90 and (c) insight during problem-solving tasks. 39,[91][92][93] Despite the significance of (a)-(c), a more relevant focus for present purposes is research that addresses the generic issue of whether certain empirical signatures of cognitive dynamics are reliably identifiable in cognitive performance.…”

Section: Contemporary Cognitive Dynamicsmentioning

confidence: 99%

Dynamics of cognition

Riley

Holden

2012

WIRES Cognitive Science

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The application of dynamical systems methods and concepts to cognitive phenomena has broadened the range of testable hypotheses and theoretical narratives available to cognitive scientists. Most research in cognitive dynamics tests the degree to which observed cognitive performance is consistent with one or another core phenomena associated with complex dynamical systems, such as tests for phase transitions, coupling among processes, or scaling laws. Early applications of dynamical systems theory to perceptual-motor performance and developmental psychology paved the way for more recent applications of dynamical systems analyses, models, and theoretical concepts in areas such as learning, memory, speech perception, decision making, problem solving, and reading, among others. Reviews of the empirical results of both foundational and contemporary cognitive dynamics are provided. © 2012 John Wiley & Sons, Ltd. How to cite this article:WIREs Cogn Sci 2012, 3:593-606. doi: 10.1002/wcs.1200 INTRODUCTION Adynamical system is simply one that changes over time (see Box 1). It is apparent that many of the processes cognitive scientists study-development, learning, the spread of activation in a semantic network, changing patterns of cortical activity, motor behavior, and so on-are intrinsically dynamical. Dynamical systems theory supplies new tools and concepts for understanding these and other cognitive phenomena. The theory offers a full complement of analytical methods (e.g., nonlinear time series analyses) and modeling strategies (e.g., differential equations). Most importantly, it motivates novel theoretical and empirical questions: One may explore the relative stability of a cognitive activity, test for meta-or multi-stability, or test for empirical patterns consistent with scaling behavior, emergence, and self-organization.These kinds of questions complement those posed by mainstream cognitive science-which traditionally has emphasized static properties of mind such as symbolic representations or structural mechanisms of information processing-by focusing explicitly on change 1-9 (see, e.g., Elman 10 for a dynamical interpretation of connectionist models). Much work in cognitive dynamics was inspired by parallel * Correspondence to: michael.riley@uc.edu Center for Cognition, Action, & Perception, Department of Psychology, University of Cincinnati, Cincinnati, OH, USA efforts to understand the dynamics of perceptionaction. [11][12][13][14][15] Early and significant progress emerged in the domain of motor coordination, where the behavioral phenomena were more obviously dynamical and high-resolution measurement technologies were more readily available. 16 The dynamical account of interlimb rhythmic coordination 12,14 is an example of an early success.Research in cognitive dynamics is often allied with some version of embodiment. [17][18][19][20][21] Dynamical systems were sometimes framed as replacements for computational-representational accounts, 8,19 as distinct but complementary, 22 or even as consistent with inform...

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“…Due to inconsistent time series length, we used a normalized measure of ApEn, as suggested by Fonseca, Milho, Passos, Araújo, and Davids (submitted for publication) with input parameter m ¼ 2 and r ¼ .2 (i.e., number of windows compared, tolerance factor, respectively) (Stergiou et al, 2004). For similar procedures see Passos, Araújo, & Davids, et al (2009).…”

Section: Data Processingmentioning

confidence: 99%

Territorial gain dynamics regulates success in attacking sub-phases of team sports

Correia¹,

Araújo²,

Davids³

et al. 2011

Psychology of Sport and Exercise

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a b s t r a c tBackground and objective: Field invasion games, such as rugby union, can be conceptualised as dynamic social systems in which the agents continuously interact to contest ball possession and territorial gain. Accordingly, this study aimed to identify the collective system dynamics of rugby union phases-of-play near the try line by investigating whether ball displacement trajectory on the playing field provides insights on successful team performance. Methods: Five rugby union matches were videotaped involving teams at a national league performance level. From these matches, 22 second phases-of-play were selected and digitized for analysis. The variable "distance gained" was investigated as a potential coordination variable describing functional coordination between players and teams. This variable concerned the distance between ball initial position and ball current position over time and was used to define the degree of territory gained by an attacking team. Results: Analysis of distance gained dynamics in attacking sub-phases demonstrated the intermittent character of rugby union performers displacement trajectories on the playing field. Amplitude of ball movements was revealed as a distinguishing feature related to attacking effectiveness. Successful attacking phases displayed lower distances of positional retreat, with the maximum retreat distance achieved sooner in successful compared to unsuccessful phases-of-play. Autocorrelation and ApEn analyses suggested low system variability within time series data concerning both performance outcomes. However, evidence of less regularity and more complexity was found in unsuccessful phases-of-play. Conclusion: Results suggested that distance gained dynamics manifests a characteristic collective behaviour pattern that captures the macroscopic functional order of multi-player attackedefence systems in team sports like rugby union.

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Interpersonal Pattern Dynamics and Adaptive Behavior in Multiagent Neurobiological Systems: Conceptual Model and Data

Cited by 101 publications

References 39 publications

Dynamical Stability and Predictability of Football Players: The Study of One Match

Dynamical Stability and Predictability of Football Players: The Study of One Match

Dynamics of cognition

Territorial gain dynamics regulates success in attacking sub-phases of team sports

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