A New Model of Graph and Visualization Usage
A New Model of Graph and Visualization Usage
复制标题
图形和可视化使用的新模型
DOI：
10.21236/ada479688
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
S. Trickett
中科院分区：
文献类型：
--
作者：
J. Trafton;S. Trickett
Gregory Trafton海军研究实验室trafton@itd.nrl.navy.mil Susan B. Trickett George Mason大学stricket@gmu.edu摘要我们认为，目前的图形理解模型并没有充分捕捉到人们如何使用图形和复杂的可视化。为了调查这一假设，我们用体内方法检查了3届科学家。我们发现，为了从他们的图表中获取信息，科学家们不仅直接从他们的可视化中读取信息（正如目前的理论所预测的那样），而且他们还使用了大量的心理意象（我们称之为空间转换）。我们提出了对当前可视化理解和使用模型的扩展，以解释这些数据。如果一个人看一个标准的股票市场图表，或者一个气象学家正在检查一个复杂的气象可视化，如何从这些图表中提取信息？对图形和可视化理解最有影响的研究是Bertin（1983）的任务分析，他提出了图形和可视化理解的三个主要过程：1。编码显示的视觉元素：例如，识别线和轴。这一阶段受到前注意过程的影响，并受到形状辨别能力的影响。2. 将元素转换为模式：例如，注意一个杆比另一个杆高，或者注意一条线的斜率。这个阶段受知觉扭曲和工作记忆限制的影响。3. 将模式映射到标签上，以解释图形所传达的特定关系。例如，确定条形图的值。大多数关于图理解的研究都考察了图的编码、感知和表示。例如，克利夫兰和麦吉尔研究了图形感知的心理物理方面（克利夫兰和麦吉尔，1984年，1986年）。同样，Pinker的图理解理论虽然相当广泛，但侧重于图的编码和理解（Pinker, 1990）。科斯林的作品强调了使图表或多或少难以阅读的认知过程。科斯林的句法和语义（以及较小程度的语用）分析侧重于图形的编码、感知和表示（科斯林，1989）。Carpenter和Shah（1998）最近的研究表明，为了理解可视化，人们会在看图表和看坐标轴之间切换。当图形包含用户需要的所有信息时（即，当信息以一种或另一种形式显式表示时），这种方案似乎工作得非常好。因此，当本科生被要求从条形图中提取特定信息时，上述过程似乎成立。然而，在实验室之外，图形的使用可能不仅仅是一系列的信息提取。例如，当查看股票市场图表时，目标可能不仅仅是确定股票当前或过去的价格，而是确定股票未来某个时候的价格。天气预报员在查看气象可视化时，经常试图预测未来的天气情况，以及当前可视化显示的情况（Trafton, Kirschenbaum, Tsui, Miyamoto, Ballas, & Raymond, 2000）。一位科学家在检查最近的实验结果时，并不总是能以一种完美地显示她的假设的答案的方式显示可用的信息。当图形或可视化不包含所需的确切信息时，当前的图形理解理论如何站得住脚？不幸的是，这些理论并没有说明这种情况。事实上，在任何图形理解理论中都没有关于如何从没有以某种形式表示的可视化信息中提取信息的规范。如果一个图不包含用户所需的信息，那么这个图通常被标记为“坏的”或“无用的”（Kosslyn, 1989; Pinker, 1990）。当前的图理解理论并没有说明当图由于各种原因没有明确显示所需信息时该怎么办。主要原因可能是大多数图理解研究都使用了相当简单的图，不需要特定的领域知识（例如，Carter, 1947; Lohse, 1993; Pinker, 1990）。然而，在现实世界的情况下，人们使用复杂的可视化，这需要大量的领域知识，所有需要的信息可能不会显式地表示在图中。因此，本研究将试图回答关于图理解的两个问题。可视化的专家用户是否需要他们正在使用的特定图表之外的信息？如果是，他们如何从图中提取信息？
A New Model of Graph and Visualization Usage J. Gregory Trafton Naval Research Laboratory trafton@itd.nrl.navy.mil Susan B. Trickett George Mason University stricket@gmu.edu Abstract We propose that current models of graph comprehension do not adequately capture how people use graphs and complex visualizations. To investigate this hypothesis, we examined 3 sessions of scientists using an in vivo method- ology. We found that in order to obtain information from their graphs, scientists not only read off information di- rectly from their visualizations (as current theories pre- dict), but they also used a great deal of mental imagery (which we call spatial transformations). We propose an extension to the current model of visualization compre- hension and usage to account for this data. Introduction If a person looks at a standard stock market graph or a meteorologist is examining a complex meteorological visualization, how is information extracted from these graphs? The most influential research on graph and visu- alization comprehension is Bertin’s (1983) task analysis that suggests three main processes in graph and visual- ization comprehension: 1. Encode visual elements of the display: For exam- ple, identify lines and axes. This stage is influenced by pre-attentive processes and is affected by the discrim- inability of shapes. 2. Translate the elements into patterns: For example, notice that one bar is taller than another or the slope of a line. This stage is affected by distortions of perception and limitations of working memory. 3. Map the patterns to the labels to interpret the spe- cific relationships communicated by the graph. For ex- ample, determine the value of a bar graph. Most of the work done on graph comprehension has examined the encoding, perception, and representation of graphs. Cleveland and McGill, for example, have examined the psychophysical aspects of graphical per- ception (Cleveland & McGill, 1984, 1986). Similarly, Pinker’s theory of graph comprehension, while quite broad, focuses on the encoding and understanding of graphs (Pinker, 1990). Kosslyn’s work emphasizes the cognitive processes that make a graph more or less diffi- cult to read. Kosslyn’s syntactic and semantic (and to a lesser degree pragmatic) level of analysis focuses on en- coding, perception, and representation of graphs (Koss- lyn, 1989). Recent work by Carpenter and Shah (1998) shows that people switch between looking at the graph and the axes in order to comprehend the visualization. This scheme seems to work very well when the graph contains all the information the user needs (i.e., when the information is explicitly represented in one form or an- other). Thus, when an undergraduate is asked to extract specific information from a bar-graph, the above process seems to hold. However, graph usage outside the labo- ratory is probably not simply a series of information ex- tractions. For example, when looking at a stock market graph, the goal may not be just to determine the current or past price of the stock, but perhaps to determine what the price of the stock will be sometime in the future. A weather forecaster looking at a meteorological visualiza- tion is frequently trying to predict what the weather will be in the future, as well as what the current visualization shows (Trafton, Kirschenbaum, Tsui, Miyamoto, Ballas, & Raymond, 2000). A scientist examining results from a recent experiment can not always display the available information in a way that perfectly shows the answer to her hypotheses. How do current theories of graph comprehension hold up when a graph or visualization does not contain the exact information needed? Unfortunately, the theories do not say anything about this situation. In fact, there are no specifications in any theory of graph comprehension about how information could or would be extracted from a visualization where that information is not represented in some form. If a graph does not contain the information needed by the user, the graph is often labeled “bad” or “useless” (Kosslyn, 1989; Pinker, 1990). Current graph comprehension theories do not have a great deal to say about what to do when a graph does not explicitly show the needed information for a variety of reasons. The main reason is probably that most graph comprehension studies have used fairly simple graphs for which no particular domain knowledge is required (e.g., Carter, 1947; Lohse, 1993; Pinker, 1990). However, in real-world situations, people use complex visualizations that require a great deal of domain knowledge, and all the needed information would probably not be explic- itly represented in the graph. This study will thus try to answer two questions about graph comprehension. Do expert users of visualizations ever need information that is not on a specific graph they are using? If so, how do they extract that information from the graph?