Complex dynamical systems in social and personality psychology: theory, modeling, and analysis
Complex dynamical systems in social and personality psychology: theory, modeling, and analysis
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社会和人格心理学中的复杂动力系统：理论、建模和分析
DOI：
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
K. L. Marsh
中科院分区：
文献类型：
--
作者：
Michael J. Richardson;R. Dale;K. L. Marsh
所有的社会过程都涉及到时间的变化：判断在几毫秒或几秒钟内迅速实现，对话在几分钟内进行，关系在更长的时间尺度上发展。简而言之，社会系统是动态系统。“动态”这个词简单地意味着时间演化，因此动态系统只是一个行为随时间演化或变化的系统。提出社会过程是动态的并不新鲜，在社会心理学中有着悠久的历史（例如，Asch，1952; Lewin，1936; Mead，1934）。此外，大多数社会人格心理学的研究人员都同意，社会过程和行为是动态的，并随着时间的推移而变化。然而，传统上，社会人格心理学，就像一般的实验心理学一样，专注于汇总统计，这些统计随着时间的推移而汇总，例如以量值的形式（例如，行为频率、情绪强度等）。这种传统的方法植根于Fisher和其他人开发的线性统计方法（Meehl，1978），目的是检测治疗或操作总体上是否会影响某些测量的行为状态或变量的结果。因此，行为变化被概念化为静态测量之间的差异，并通过对这些测量的协变响应来建模。不幸的是，这种传统的方法仅仅描述了行为变化;它没有捕捉到真实的时间演化，因此对于理解行为变化发生的过程并不总是最佳的。因此，为了在我们对心理变化和过程的理解方面取得进展，研究人员需要考虑采用新的工具和方法论概念，即动力系统。动力系统的科学研究关注的是理解，建模和预测系统行为随时间变化的方式。作为一种形式化的方法，它在应用数学和物理学中有着悠久的历史，并已被广泛用于理解和建模许多不同类型的物理系统的行为，如行星的运动（位置和速度），质量弹簧系统，摆动陀螺和自持振荡器。然而，在过去的几十年里，越来越多的研究人员开始使用动力系统的概念和工具来研究和理解更复杂的生物、认知和社会系统的动力学行为。术语“复杂性”是指大多数生物、认知和社会系统通常表现出非线性的行为，涉及大量相互作用的元素或组件。从历史上看，复杂动力系统的非线性在很大程度上阻碍了对此类系统的研究，因为使人们能够揭示非线性和复杂动力系统的动力学的数值技术涉及大量的计算过程，如果没有现代计算机，这些计算过程是不可能执行的。这对于抽象的非线性动力学模型（在本章第二节中讨论）和行为数据分析（在第三节中讨论）都是如此。当然，如今这些计算上的困难已经不复存在，研究人员可以很容易地用公式表示和分析许多非线性和复杂的动力系统。事实上，非线性动力学和复杂系统的领域，以及我们对这些系统的理论理解，已经与
All social processes fundamentally involve change in time: Judgments materialize quickly over milliseconds or seconds, conversations flow over minutes, and relationships evolve across even longer time scales. Put simply, social systems are dynamical systems. The word “dynamical” simply means time-evolving and thus a dynamical system is simply a system whose behavior evolves or changes over time. Proposing that social processes are dynamical is not new and has a long history in social psychology (e.g., Asch, 1952; Lewin, 1936; Mead, 1934). Moreover, most researchers in social-personality psychology would agree that social processes and behavior are dynamical and change over time. Traditionally, however, socialpersonality psychology, like experimental psychology in general, has focused on summary statistics, which aggregate over time, such as in the form of magnitudes (e.g., behavioral frequencies, emotional intensities, and so on). This traditional approach is rooted in the linear statistical methods developed by Fisher and others (Meehl, 1978), and is aimed at detecting whether treatments or manipulations, on the whole, affect the outcome of some measured behavioral state or variable. Behavioral change is therefore conceptualized as the difference between static measures and is modeled by covarying responses on such measures. Unfortunately, this traditional approach merely describes behavioral change; it does not capture true time evolution and so is not always optimal for understanding the process by which behavioral change occurs. To make progress in our understanding of psychological change and process, therefore, researchers need to consider adopting new tools and methodological concepts, namely those of dynamical systems. The scientific study of dynamical systems is concerned with understanding, modeling, and predicting the ways in which the behavior of a system changes over time. As a formal approach, it has a long history in applied mathematics and physics, and has been used extensively to understand and model the behavior of many different types of physical systems, such as the motion (position and velocity) of planets, mass-spring systems, swinging pendulums, and selfsustained oscillators. In the last few decades, however, an increasing number of researchers have begun to investigate and understand the dynamic behavior of more complex biological, cognitive, and social systems, using the concepts and tools of dynamical systems. The term “complexity” refers to the fact that most biological, cognitive, and social systems typically exhibit behavior that is nonlinear and involves a large number of interacting elements or components. Historically, it is the nonlinearity of complex dynamical systems that has largely hindered research on such systems, in that the numerical techniques that enable one to uncover the dynamics of nonlinear and complex dynamical systems involve an extensive number of computational processes that are impossible to perform without modern computers. This is true for both abstract nonlinear dynamical models (covered in the second section of this chapter) and for the analysis of behavioral data (discussed in the third section). These days, of course, these difficulties of computation no longer exist, and researchers can formulate and analyze many nonlinear and complex dynamical systems quite easily. Indeed, the fields of nonlinear dynamics and complex systems, as well as our theoretical understanding of such systems, have grown in parallel with
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DOI：
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发表时间：
2007
期刊：
--
影响因子：
--
作者：
E. Tognoli;J. Lagarde;G. Deguzman;J. Kelso
通讯作者：
E. Tognoli;J. Lagarde;G. Deguzman;J. Kelso
语音分类的非线性动力学。
DOI：
10.1037//0096-1523.20.1.3
发表时间：
1994
期刊：
Journal of experimental psychology. Human perception and performance
影响因子：
--
作者：
Tuller,B;Case,P;Ding,M;Kelso,JA
通讯作者：
Kelso,JA
影响因子：
7.6
作者：
Larsen,RJ;Kasimatis,M
通讯作者：
Kasimatis,M
影响因子：
7.6
作者：
Eliot R. Smith
通讯作者：
Eliot R. Smith
影响因子：
5.4
作者：
Gilden, DL
通讯作者：
Gilden, DL