We see plenty of data visualization every day, on newspaper, billboard, smartphone, etc. Information about politics, culture, business is rapidly converted from numeric data to graphic images like bar charts, scatter plots, maps, networks etc. The goal of visualization is to aid our interpretation of data by leveraging the visual system of human beings to recognize patterns, trends, and identify outliers (Heer, 2010). When designing the visualization of data, what aspects should be concerned when it comes to human perception and cognition?

Working memory and chunking

The theory of magical number seven is quite universal today even though it is a result of misinterpreting Miller’s article in 1956. It is said our short-term memory capacity, or memory span is limited, usually seven plus or minus two, depends on the type of stimuli. Some other research argued that the number should be three to five. There is no point to discuss which number is correct, just keep in mind there is a limit of individuals’ working memory to remember new things. And we are supposed to avoid creating cognitive overload when designing data visualization.

Chunking is a process in brains’ short-term memory by which individual pieces of information are organized into a meaningful whole, to be accessible for easy recall. If items can be grouped into chunks in our memory, the capacity can be greatly increased.

On the one hand, we should avoid information overload. On the other hand, a certain amount of information is needed to make sense of the issue. Let us look at some examples. Wikum is an online tool that allows readers to refine out the main point of discussion. The tree structure gives us a snapshot of the entire discussion content. Their study compares Wikum and Google doc with and without the summary. The results show that users rate reading and exploring experience highest in Wikum. Wikum also got the highest rate of summary quality though not so significant. Generally, this study shows summaries are a useful way to get an overview of a discussion.

Wikum is not a purely a data visualization project, but we can get some insights from the tree structure and interactive system. The summary is a sort of chunk of information and nicely shown in the tree structure node. Colors indicate two types of content: orange nodes mean summarized content while the blue ones are summarized.

Attention resources

It has been proven that we have attention limits when we are doing several activities at the same time. In Daniel Kahneman’s capacity model, he describes attention as cognitive effort whose capacity varies depending on multiple factors such as environment, tasks, and individual conditions. Regardless of single or multiple resources pool, information is competing for our attention.

Cognitive tunneling

Cognitive tunneling is the mental state in which your brain hangs on to the thing that is closest to you or in front of you and does not see the rest of the environment, or other, relevant data.

Data visualization with animation looks pretty cool as the example shows.

But it is also hard to directly guide readers to the most salient content. Viewers may only focus on the fancy animation itself without trying to understand the content. The same as interactive data visualization.

Proceduralization

If we look at information processing model, we can see how we generate the long-term memory. In John Robert Anderson’s model, learning happens in stages. We first get declarative knowledge, when we need high working memory load but have frequent errors. Then with the knowledge compilation, the declarative knowledge convert into procedural knowledge.

Procedural knowledge is a collection of productions, or if-then statements that specify a cognitive condition and an action that will be performed if that condition is met.

When it comes to data visualization, a simple example is that we can rapidly recognize green as “good” and red as “warning”. Or red as hot and blue as cold. It is not a good idea to use a brand format that completely different or opposite from common sense, which will add readers’ cognitive load.

In the following example, the traditional pie chart is replaced by blocks in a round. And the number in the circle does not make sense either. It takes time for viewers to understand what this graphic means.

To summarize, in terms of human perceptions and cognitive load, we need to pay attention to:

• Working memory
• Attention resources
• Cognitive tunneling
• proceduralization

And some considerations for visualization:

• Taking advantage of proceduralization
• Minimizing resource consumption
• Resisting cognitive tunneling
• Helping viewers chunk information

Thanks for Jeff Rzeszotarski and his course: Sensemaking, theory and practice. 

Reference

Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological review, 89(4), 369.

Cowan, N. (1998). Visual and auditory working memory capacity. Trends in Cognitive Sciences, 2(3), 77–78.

Farrington, J. (2011). Seven plus or minus two. Performance Improvement Quarterly, 23(4), 113-116.

Hancock, P. A., & Kim, J. W. (2010). Cognitive Modeling of Performance Response Capacity Under Time Pressure (No. TR-2010-0907). UNIVERSITY OF CENTRAL FLORIDA ORLANDO DEPT OF PSYCHOLOGY.

Haroz, S., & Whitney, D. (2012). How capacity limits of attention influence information visualization effectiveness. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2402-2410.

Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Queue, 8(5), 20.

Zhang, A. X., Verou, L., & Karger, D. (2017). Wikum: Bridging discussion forums and wikis using recursive summarization.