Modes of Dynamic Attending

本提案探讨了一种新的方法来研究参与的基本结构，该结构基于参与是一种内在的时间活动的假设：人们通过依赖能够引起娱乐的内部参与节奏来响应环境的动态变化。本文提出了两种动态出席模式：分析出席和面向未来的出席，前者发生在相对较小的事件时间跨度内，后者发生在较长的事件时间段内。提出的理论框架假设，环境事件的结构为每一种参与模式提供了注意控制（即夹带）的基础。特别有趣的是一个特定的振荡器模型，其基础是面向未来的出席。提出了一些明确的假设来检验振子模型的预测。这些与节奏因素的作用有关，节奏因素被预测会促进或阻碍注意力的转移。这项研究很重要，因为目前还没有关于注意力的心理学模型来解释人们如何关注环境的动态方面，也就是说，关注诸如演讲、视觉动作模式、音乐等事件。相反，大多数当前的理论关注的是静态的视觉对象（例如，二维图片或字母序列）。但是我们的日常生活中有很多变化的、动态的事件。因为我们的环境主要是动态的，因为许多注意力障碍（如失语症）在人们与动态事件互动时很明显，所以开发和测试识别这些事实的模型很重要。

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.