Simulation, Situations, and Embodiment in Conceptual Processing

在国家科学基金会的支持下，劳伦斯·巴萨卢博士将进行为期三年的基础研究。这项由基金资助的项目将考察人类话语、推理或感知等典型高级认知活动背后的知识。指导这项研究的假设是相当新的。关键的假设是，高水平的、抽象的、认知活动实际上植根于身体的情景日常工作--因此术语情景认知或具身认知。例如，我们对汽车的知识反映了我们如何与汽车互动，实际驾驶汽车是什么感觉；看到、听到、触摸和闻到真正的汽车；或者感受到对汽车的情感反应。这一观点与心理学的传统形成了鲜明对比，在心理学中，我们对世界的知识被认为是完全抽象和超然的--就像一种适用于所有计算机程序的“集中式”知识结构。这项受资助的研究将测试从新的替代方案中得出的预测。人类参与者将执行经典的“知识任务”，回答诸如“汽车的特性是什么”之类的问题。或者“轮胎是汽车的财产吗？”围绕这样的问题和任务设计了精心控制的实验室实验，以评估是否使用情境和具身形式的知识来执行它们。这项研究具有广泛的意义。首先，对其工作假说的支持将促使人类知识的基本科学理论发生重大变化；这项工作可能有助于从根本上改变我们对自己的看法。其次，这项研究的结果可能会对教育(即如何最好地教授知识领域)或认知工程(即如何设计机器以最好地与人类互动)产生广泛的应用影响。最后，这项工作可能会提出新形式的人工智能。智能机器使用围绕其外围设备塑造的情境知识，比传统的集中式智能机器更强大。可能的新机器可能类似于火星探测中使用的机器人，例如，第二代机器人，它们能更好地适应自己的环境。

With National Science Foundation support, Dr. Lawrence Barsalou will conduct three years of basic research. The funded project will examine the knowledge that underlies typical high-level cognitive activities such as human discourse, reasoning, or perception. The hypotheses that guide this research are quite new. The key assumption is that high level, abstract, cognitive activities are actually grounded in the situated everyday workings of the body-thus the terms situated cognition or embodied cognition. For example, our knowledge of cars reflects how we interact with cars, what it is like to actually drive a car; to see, hear, touch, and smell a real car; or to feel an emotional response to a car. This view contrasts with a tradition in psychology, whereby our knowledge of the world is assumed to be fully abstract and detached-something like the "centralized" one-kind-of-knowledge-structure-fits-all way in which a computer program can be written. The funded research will test predictions derived from the new alternative. Human participants will perform classic "knowledge tasks," responding to questions such as "What are the properties of a car?" or "Is a tire a property of a car?". Carefully-controlled laboratory experiments have been designed around such questions and tasks to assess whether situated and embodied forms of knowledge are used to perform them.This research has broad implications. First, support for its working hypotheses would motivate big changes in basic scientific theories of human knowledge; this work could contribute to a fundamental shift in how we think about ourselves. Second, the outcomes of this research could have broad applied impact in education (i.e., how best to teach a knowledge domain) or cognitive engineering (i.e., how machines should be designed to best interact with human beings). Finally, this work may suggest new forms of artificial intelligence. Intelligent machines that use situated knowledge, shaped around their peripheral devices, are more robust than traditional centralized intelligent machines. Possible new machines could resemble the robots used in exploration of Mars, for example, a second-generation of robots that better situate themselves in their environments.