Modes of Dynamic Attending

这项提议考察了一种新的方法来构建出席的基本结构，该方法基于出席是一种固有的时间活动的假设：人们通过依赖能够夹带的内部出席节奏来对环境中的动态变化做出反应。提出了两种动态出席模式：分析型出席和面向未来出席，前者发生在相对较小的事件时间跨度上，后者发生在较长的事件时间段上。该理论框架假设环境事件的结构为每种参与方式提供了注意控制(即夹带)的基础。特别令人感兴趣的是面向未来的出席背后的一个特定的振荡器模型。提出了一些检验振子模型预测的显式假设。这些都与节奏因素的作用有关，这些因素被预测为促进或延缓注意力的牵连。这项研究之所以重要，是因为目前还没有一种注意力心理学模型来解释人们是如何关注环境的动态方面的，也就是说，人们是如何关注语言、视觉动作模式、音乐等事件的。相反，目前的大多数理论都关注静态视觉对象(例如，二维图片或字母序列)。但我们日常生活中的许多事情都是由不断变化的、动态的事件组成的。因为我们的环境主要是动态的，而且当人们与动态事件交互时，许多注意力障碍(如失语症)很明显，因此开发和测试能够识别这些事实的模型是很重要的。

This proposal examines a new approach to the fundamental construct of attending which rests on the assumption that attending is an inherently temporal activity: people respond to dynamic changes in their environment by relying on internal attending rhythms that are capable of entrainments. Two modes of dynamic attending are proposed: Analytic attending, which takes place over relatively small time spans of events, and Future-Oriented attending, which takes place over longer event time periods. The theoretical framework advanced presumes that the structure of environmental events offers a basis of attentional control (viz entrainment) for each attending mode. Of special interest is a specific oscillator model underlying Future-Oriented attending. A number of explicit hypotheses that test predictions of the oscillator model are presented. These pertain to the role of rhythmic factors that are predicted to facilitate or retard attentional entrainments. This research is important because no current psychological model of attention explains how people attend to dynamic aspects of the environment, that is, to events such as speech, visual action patterns, music etc.. Instead, most current theories address attending to static visual objects (e.g., two-dimensional pictures or letter sequences). But much of our everyday world consists of changing, dynamic, events. Because our environment is primarily dynamic and because many attentional dysfunctions (e.g. aphasia) are evident largely when people interact with dynamic events, it is important to develop and test models that recognize these facts.