Simulation, Situations, and Embodiment in Conceptual Processing

在国家科学基金会的支持下，Lawrence Barsalou博士将进行为期三年的基础研究。该资助项目将研究典型的高级认知活动（如人类话语、推理或感知）背后的知识。指导这项研究的假设是相当新的。关键的假设是高层次的、抽象的认知活动实际上是建立在身体的日常情境运作基础上的——因此被称为情境认知或具身认知。例如，我们对汽车的了解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、触摸和闻到一辆真正的汽车；或者感受对汽车的情感反应。这一观点与心理学的传统观点形成了对比，传统观点认为，我们对世界的认识是完全抽象和分离的——就像计算机程序可以编写的那种“集中的”一种知识结构适用于所有方法。这项受资助的研究将检验从这种新选择中得出的预测。人类参与者将执行经典的“知识任务”，回答诸如“汽车的属性是什么？”或“轮胎是汽车的属性吗？”之类的问题。围绕这些问题和任务设计了精心控制的实验室实验，以评估是否使用了情境和具体形式的知识来执行这些问题和任务。这项研究具有广泛的意义。首先，对其工作假设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的人工智能形式。使用围绕其外围设备形成的定位知识的智能机器，比传统的集中式智能机器更强大。可能的新机器类似于火星探测中使用的机器人，例如，第二代机器人可以更好地在环境中定位自己。

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.