HCC: Small: A Computational Theory of Perceptual Integration in Multimodal Multitasking
HCC: Small: A Computational Theory of Perceptual Integration in Multimodal Multitasking
批准号:
1017593
负责人:
Anthony Hornof
金额:
$49.96万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-07-01 至 2016-09-30
中文摘要
许多当代计算都是在多任务环境中完成的,在这种环境中,多个视觉和听觉显示器会争夺一个人。知觉、认知(决策)和运动(运动)加工。在一些任务领域,如空中交通管制、紧急车辆调度和车内导航,多任务是无法避免的,人们需要将导航等次要任务与驾驶等重要生命任务穿插在一起。为了最大限度地提高人的整体效率,为生命关键型和时间关键型复杂多任务设计的用户界面需要考虑到人。S并行监视和响应多个信息源的能力。然而,很少或根本没有实用的科学理论来解释人类多模态(听觉和视觉)多任务处理的能力、局限性和策略。该项目开发了人类能力预测建模所需的科学基础,以跨多种模式集成以并行完成多项任务。该项目将发展多模态多任务中的感知整合理论,对精心收集的人类数据进行严格、详细、高保真的计算认知建模,包括详细的眼动数据,用于定位在实验室高分辨率任务和数据与现实世界之间的任务,以确保实际应用。该模型将强调中央执行认知决策在管理知觉处理、移动眼睛和协调交叉任务需求的运动反应方面的作用。该项目通过研究和揭示人类多模态多任务性能的核心局限性,并为任务控制中心、地铁调度中心、急诊室和车辆计算机系统的设计提供可付诸实践的理论,从而造福社会。这一理论将导致实际的设计决策,从而提高地铁、核电站、高速公路、医院和车辆的安全性。
英文摘要
Much contemporary computing is done in multitasking environments in which multiple visual and auditory displays compete for a person?s perceptual, cognitive (decision), and motor (movement) processing. In some task domains, such as air-traffic control, emergency vehicle dispatching, and in-car navigation, multitasking cannot be avoided, and people need to interleave secondary tasks such as navigation with primary life-critical tasks such as driving. To maximize overall human effectiveness, user interfaces intended for life-critical and time-critical complex multitasking need to be designed to account for a person?s ability to monitor and respond to multiple information sources in parallel. And yet, there is little or no practical scientific theory to explain the human abilities, limitations, and strategies for multimodal (auditory and visual) multitasking.This project develops the science base needed for predictive modeling of human abilities to integrate across multiple modalities to accomplish multiple tasks in parallel. The project will develop a theory of Perceptual Integration in Multimodal Multitasking with rigorous, detailed, high-fidelity computational cognitive modeling of carefully collected human data, including detailed eye movement data, for tasks that are positioned between the lab for high resolution tasks and data and the real world to insure practical application. The modeling will emphasize the role of central executive cognitive decisions for managing perceptual processing, moving the eyes, and coordinating motor responses to interleaved task demands.The project benefits society by studying and revealing the limitations to human multimodal multitasking performance at the core, and by providing theory that can be put to practice in the design of mission control centers, subway dispatching centers, emergency rooms, and computer systems for vehicles. The theory will lead to practical design decisions that will improve the safety of subways, nuclear power plants, highways, hospitals, and vehicles.
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