EAGER: Cognitive modeling of strategies for dealing with errors in mobile touch interfaces

EAGER：处理移动触摸界面错误策略的认知建模

• 批准号: 1451172
• 负责人: Laurie Williams
• 金额: $ 28.11万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451172&HistoricalAwards=false
• 关键词: EAGER; Cognitive; modeling; strategies; dealing

Touch interfaces on mobile phones and tablets are notoriously error prone. One plausible reason for slow progress in improving the usability of touchscreen devices is that research and design efforts in human-computer interaction take a relatively narrow focus on identifying, understanding, and eliminating human error, focusing primarily on the specific individual touch interactions rather than the broader task knowledge that a person needs to use a device to do a real-world task. This project takes a different perspective, suggesting that usage errors represent breakdowns in a person's efforts to adapt to the complexity of the tasks and devices, and that many of these problems can be traced to interface designers not inadequately considering the cognitive task knowledge that a user will need to do a task. Many touchscreen design guidelines are arbitrary and based on common practice and established user expectations. This project aims to change the shape of research and practice for touch interaction on mobile devices by introducing sound scientific principles of cognitive task analysis and error analysis to the design and analysis of such interfaces. This project could benefit society by dramatically improving the user interface design of touchscreen interfaces.The project will develop cognitive models, which are computer programs that behave in some way like humans. The project will specifically develop cognitive models of touch interaction that could be used by designers when designing new touchscreen interfaces, to identify where users are likely to encounter problems with the new interface. The project includes three stages. The first stage will develop a small set of instrumented interfaces on mobile platforms to collect human data for error-prone tasks. The experiments will examine the variation of the probability of error at specific points during a sequence, the visual feedback provided for specific actions, and the user knowledge of error recovery and avoidance actions. The second stage will follow other successful efforts in cognitive modeling to analyze user data from our exploratory experiment with the goal of developing a representation of the strategies we have observed. An automated process will explore a search space of plausible cognitive strategies to identify models that explain the performance data collected during the first stage. The third stage will validate the hypotheses pertaining to the cognitive strategies that contribute to the successful and unsuccessful use of touchscreen devices that are established during the first two stages. The results will have much wider implications than current models of touch accuracy (which produce little more than target size and spacing guidelines) by focusing on the bigger picture of expertise and failure. The project takes a large step into the cognitive modeling of pervasive but not-yet-unexplored interface problems.

移动的手机和平板电脑上的触摸界面是出了名的容易出错。在改善触摸屏设备的可用性方面进展缓慢的一个合理原因是，人机交互的研究和设计工作相对狭隘地关注于识别，理解和消除人为错误，主要关注特定的个人触摸交互，而不是更广泛的任务知识，即一个人需要使用设备来完成现实世界的任务。这个项目采取了不同的角度来看，使用错误代表故障在一个人的努力，以适应任务和设备的复杂性，许多这些问题可以追溯到界面设计师没有充分考虑认知任务的知识，用户将需要做一个任务。许多触摸屏设计指南是任意的，并且基于常见的实践和既定的用户期望。本项目旨在通过将认知任务分析和错误分析的合理科学原理引入此类界面的设计和分析，改变移动的设备上触摸交互的研究和实践的形态。该项目将开发认知模型，即在某种程度上表现得像人类的计算机程序。该项目将专门开发触摸交互的认知模型，可供设计师在设计新的触摸屏界面时使用，以确定用户在新界面中可能遇到的问题。该项目包括三个阶段。第一阶段将在移动的平台上开发一套小型仪表化界面，以收集易出错任务的人类数据。实验将检查在序列中的特定点的错误的概率的变化，为特定的动作提供的视觉反馈，以及错误恢复和避免动作的用户知识。第二阶段将遵循认知建模的其他成功努力，分析我们探索性实验中的用户数据，目的是开发我们观察到的策略的代表性。一个自动化的过程将探索一个合理的认知策略的搜索空间，以确定解释在第一阶段收集的性能数据的模型。第三阶段将验证与认知策略有关的假设，这些策略有助于在前两个阶段建立的成功和不成功的触摸屏设备的使用。通过关注专业知识和失败的更大图景，这些结果将比当前的触摸准确性模型（只产生目标大小和间距指南）具有更广泛的影响。该项目向普遍但尚未探索的界面问题的认知建模迈出了一大步。