The Role of Sensorimotor and Perceptual Features in Perceiving and Enacting Actions

提供虚拟训练已经成为用于训练在真实的世界（例如，军事行动）。一个长期存在的问题是，在模拟训练环境中学习的技能如何能够很好地转移到真实的世界实践中。模拟世界在许多方面不同于真实的世界。特别是，在感觉、运动和知觉特征方面存在显著差异。 具身认知科学的进展不断证明，人体和身体所处的环境如何共同形成一个产生心理活动的复杂系统。然而，迄今为止报道的大多数研究仅检查了体现与经典认知科学预测的认知过程中的微小增量差异。很少有研究调查增量差异是否会转化为学习新技能的实际结果。本研究项目的中心假设是，在模拟世界和真实的世界中，人们对风险和非风险行为的感知和行为是否存在差异。在这些研究中的主要困难之一是创建简单的并行任务环境，是服从控制实验，但可以扩大到真实的世界的应用。该研究项目通过选择可能具有内在风险的日常行为来实现这一点，并使用检查事件感知展开的时间过程的方法。为了感知动作，参与者将执行感知分割任务，记录他们的眼球运动，并回答有关动作记忆的问题。对于制定行动，参与者将说明他们将如何完成行动。本研究的长期实践目标是为模拟训练环境的开发者提供以下三个方面的经验基础：1）确定感知和感觉信息的适当规格水平; 2）确定适当的指令水平，以突出或补偿模拟世界中制定行动所导致的一些显著差异;以及3）确定哪些动作可以通过模拟学习，哪些动作应该通过真实的世界实践学习。该研究项目汇集了来自具体认知科学，人工智能，人机交互和机器人领域的理论和实证研究的跨学科整合。 直到最近，认知心理学才开始理解人类如何自发地将多模态信息的恒定流分割成离散事件，并对正在进行的世界施加认知控制。这个研究项目在即将到来的世界的多模态分割方面朝着概念化人类和复杂机器行为前进了一步。更好的模拟环境设计将来自于这种理解，并将使整个社会受益，在这个社会中，我们越来越多地与技术互动，并在技术驱动的学习环境中接受培训。

Providing virtual training has become an increasingly important method for training skills that are risky, expensive, or otherwise infeasible to carry out in the real world (e.g., military operations). One long standing question has been how well skills learned in a simulated training environment can be transferred to real world practice. The simulated world differs from the real world in a number of aspects. In particular, there are significant differences in sensory, motor, and perceptual features. Advances in embodied cognitive science have consistently demonstrated how the human body, and the environment which the body inhabits, together form a complex system in producing mental activities. However, most studies that have been reported thus far only examined small incremental differences in cognitive processes as predicted by the embodied versus classic cognitive science. There are few studies that have investigated whether the incremental differences will translate into tangible consequences in learning new skills. The central hypothesis being investigated in this research project is whether there are differences in how people perceive and enact risky versus non-risky actions in the simulated versus the real world. One of the major difficulties in such studies is to create simple parallel task environments that are amenable to controlled experimentation but can be scaled up for real world applications. This research project accomplishes this by selecting everyday actions that can be inherently risky and uses methods that examine the time course in which event perception unfolds. For perceiving actions, participants will perform perceptual segmentation tasks, have their eye movements recorded, and answer questions regarding the memory of actions. For enacting actions, participants will indicate how they would complete the actions. The long-term practical objective of this research is to provide an empirical basis for developers of simulated training environments in the following three areas: 1) determining the appropriate level of specifications for perceptual and sensory information; 2) determining the appropriate level of instructions to either highlight or compensate some of the significant differences that result from enacting actions in the simulated world; and, 3) determining what actions can be learned via simulations versus what actions ought to be learned via real world practices. This research project brings together an interdisciplinary integration of theory and empirical research from the fields of embodied cognitive science, artificial intelligence, human-computer interaction, and robotics. Cognitive psychology has not begun, until recently, to understand how humans spontaneously segment the constant flux of multimodal information into discrete events and exert cognitive control over the ongoing world. This research project goes a step forward towards conceptualizing human and complex machine behaviors in terms of the multimodal segmentations of the incoming world. The better design of simulated environments will result from this understanding and will benefit all of society, in which we are increasingly interacting with technology and being trained in technology driven learning environments.