Ballard D H, Hayhoe M M, Pook P K, Rao R P
Computer Science Department, University of Rochester, NY 14627, USA.
Behav Brain Sci. 1997 Dec;20(4):723-42; discussion 743-67. doi: 10.1017/s0140525x97001611.
To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level--more abstract than that used to capture natural phenomena but less abstract than what is traditionally used to study high-level cognitive processes such as reasoning. At this "embodiment level," the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can be matched to the natural computational economies of sequential decision systems through a system of implicit reference called deictic in which pointing movements are used to bind objects in the world to cognitive programs. This target article focuses on how deictic binding make it possible to perform natural tasks. Deictic computation provides a mechanism for representing the essential features that link external sensory data with internal cognitive programs and motor actions. One of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements.
为了描述在不同时间尺度上发生的现象,大脑的计算模型必须纳入不同层次的抽象。在大约三分之一秒的时间尺度上,身体的定向运动在认知中起着至关重要的作用,并形成了一个有用的计算层次——比用于捕捉自然现象的层次更抽象,但比传统上用于研究诸如推理等高阶认知过程的层次抽象程度更低。在这个“具身层次”上,物理系统的约束决定了认知操作的性质。关键的协同作用在于,在大约三分之一秒的时间尺度上,身体运动的自然顺序性可以通过一种称为指示的隐式参照系统,与顺序决策系统的自然计算经济性相匹配,在该系统中,指向动作被用于将世界中的物体与认知程序绑定。这篇目标文章聚焦于指示绑定如何使执行自然任务成为可能。指示计算提供了一种机制,用于表征将外部感官数据与内部认知程序及运动动作联系起来的基本特征。认知的核心特征之一,即工作记忆,可以与诸如眼球运动和手部运动等身体特征的瞬间状态相关联。