A Componential Model of Human Interaction with Graphs. H. Effects of the Distances among Graphical Elements
A Componential Model of Human Interaction with Graphs. H. Effects of the Distances among Graphical Elements
复制标题
人类与图交互的组件模型。
DOI：
10.1177/154193129203600422
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发表时间：
1992
期刊：
影响因子：
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通讯作者：
Michael O. Neary
中科院分区：
文献类型：
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作者：
D. Gillan;Michael O. Neary
Gillan和Lewis（1992）基于对人们使用图形的任务分析，开发了一个描述人们如何与图形交互的模型。该模型提出，对于简单的任务（如比较和减法）和常见的图形（如线形图、散点图和条形图），图形用户采用五个组成过程的组合：搜索指标、对指标的值进行编码、对值进行算术运算、对指标进行空间比较、响应。该模型进一步表明，用户应用的组件的组合和顺序取决于用户的任务和图的类型。本研究考察了图中空间关系模型的两个预测：(1)回答比较问题的反应时间对两个指标之间的距离变化敏感，而对指标与坐标轴的距离变化不敏感；(2)回答差异问题的反应时间对指标与y轴的距离敏感，而对指标之间的距离不敏感。在实验中，受试者使用线形图和柱状图来回答比较和差异问题，其中适当的距离系统地变化。研究结果支持了这两种预测，从而为模型提供了实证验证。此外，数据的某些方面是模型没有预料到的，这表明需要增强组件模型。
Based on task analyses of people using graphs, Gillan and Lewis (1992) have developed a model that describes how people interact with graphs. The model proposes that for simple tasks (e.g., comparisons and subtraction) and common graphs (e.g., line, scatter, and bar graphs), graph users apply combinations of five component processes — Searching for indicators, Encoding the value of indicators, performing Arithmetic Operations on the values, making Spatial Comparisons among the indicators, and Responding. The model further suggests that the combination and order of the components that the user applies depends on a user's task and the type of graph. The present research investigated two predictions from the model concerning spatial relations in a graph: (1) that response times to answer comparison questions should be sensitive to varying the distance between two indicators, but not to varying the indicator-to-axis distance, and (2) that response times to answer difference questions should be sensitive to the distance between the indicator and the y-axis, but not to the distance between the indicators. In the experiment, subjects used line and bar graphs to answer comparison and difference questions in which the appropriate distances varied systematically. The results of the research supported both predictions, thereby providing empirical validation of the model. In addition, some aspects of the data were not anticipated by the model, suggesting the need to enhance the componential model.