Simulation, Situations, and Embodiment in Conceptual Processing

在美国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。 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）的基础知识。 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运作，因此，术语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、触摸和闻到真实的汽车是什么感觉;或者感受到对汽车的情感反应是什么感觉。 这种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分离的，就像计算机程序可以编写的“集中式”知识结构一样。 受资助的研究将测试来自新替代方案的预测。 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？或者“轮胎是汽车的财产吗？”". 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化和具体化的知识形式来完成这些任务。 首先，支持其工作假设将推动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我们如何看待自己的根本转变。 其次，这项研究的成果可能在教育方面产生广泛的应用影响（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）。 最后，这项工作可能会提出新形式的人工智能。 使用情境知识的智能机器，围绕其外围设备形成，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，第二代机器人可以更好地适应环境。

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.