Why We Remember Some Visualizations and Forget Others

By Tiffany Trader

November 1, 2013

Researchers from Harvard and MIT have teamed up to address an important question: what makes a data visualization memorable? The conventional opinion is that it’s easy to identify “bad” data visualization: tacky renderings with too much text, excessive ornamentation, distracting colors, and kitschy clip art.

Top twelve most memorable visualizations from the experiment (Image courtesy of Michelle Borkin, Harvard SEAS.)
Design expert Edward Tufte refers to these pieces as “chart junk” classifying them as redundant at best, and useless at worst. The visualization community, however, is divided. Some say these seemingly extraneous elements actually serve a purpose by creating a lasting impression in the viewer’s mind.

The debate over “chart junk” became the impetus for a scientific study, which was then documented in a research paper by computer scientists at Harvard and cognitive scientists at MIT. These experts of design call into question the usefulness of a perfectly-executed graphic that hardly anyone remembers. They conclude that the very design elements that attract so much criticism can also make a visualization more memorable.

The authors write that “knowing what makes a visualization memorable is a step towards answering higher level questions like ‘What makes a visualization engaging’ or ‘What makes a visualization effective?'”

Results of this study were presented on October 15 at the IEEE Information Visualization (InfoVis) conference in Atlanta, Georgia. The work was also highlighted on Harvard’s School of Engineering and Applied Sciences website.

For lead author Michelle Borkin, a doctoral student at the Harvard School of Engineering and Applied Sciences (SEAS), memorability is a key metric. “I spend a lot of my time reading these scientific papers, so I have to wonder, when I walk away from my desk, what am I going to remember?” she says. “Which of the figures and visualizations in these publications are going to stick with me?”

Borkin and her team performed the largest-scale visualization study of its kind, collecting 5,693 visualizations, categorized by visualization type (e.g., bar chart, line graph, etc.), from news media sites, government reports, scientific journals, and infographic sources. After eliminating multiple images (i.e., ones that were grouped rather than stand-alone) the initial pool was winnowed to 2,070 single-panel visualizations. A further subset of 410 images were selected as “target” visualizations. Each of these was annotated with additional attributes, including ratings for data-ink ratios and visual densities.

The experiment was set up as a game on Amazon’s Mechanical Turk, which compensates participants, called workers, for performing HITs (“Human Intelligence Task”). Workers were presented with a sequence of images and asked to press a key if they saw an image for the second time in the sequence. At the end of the testing, each image was given a memorability score. What the researchers discovered was that observers are consistent in which visualizations are most memorable and which are most forgettable.

Out of the 410 target images, 145 contained either photographs or cartoons, humanly recognizable objects, which the scientists refer to as pictograms. The study showed that visualizations that used pictograms had on average higher memorability scores.

Borkin’s adviser, Hanspeter Pfister, a Wang Professor of Computer Science at Harvard SEAS, adds this commentary: “A visualization will be instantly and overwhelmingly more memorable if it incorporates an image of a human-recognizable object – if it includes a photograph, people, cartoons, logos – any component that is not just an abstract data visualization,” she says. “We learned that any time you have a graphic with one of those components, that’s the most dominant thing that affects the memorability.”

Visualizations that were more dense or used more color also had higher memorability scores, but other results proved a bit more surprising:

“You’d think the types of charts you’d remember best are the ones you learned in school – the bar charts, pie charts, scatter plots, and so on,” Borkin says. “But it was the opposite.” Charts with more unusual shapes – tree diagrams, network diagrams, grid matrices and such – were actually more memorable.

Audra Oliva, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Lab, has been studying visual memory for about six years now. Research performed by her team demonstrates that human memory responds better to human-centric images rather than landscapes.

Without this similarity across human responses, asking what makes an image or visualization more memorable than another would be pointless.

“All of us are sensitive to the same kinds of images, and we forget the same kind as well,” Oliva says. “We like to believe our memories are unique, that they’re like the soul of a person, but in certain situations it’s as if we have the same algorithm in our heads that is going to be sensitive to a particular type of image. So when you find a result like this in photographs, you want to know: is it generalizable to many types of materials – words, sound, images, graphs?”

The scientists who performed the study are excited about the potential for advancing the science of visualization, but they are also quick to point out that memorability is just one parameter. Accuracy is always the highest priority with the best visualizations also being easy to comprehend, engaging and aesthetically-pleasing. But there’s no reason they can’t also be memorable.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break “Speed Limit”

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

HPE Extreme Performance Solutions

HFT Firms Turn to Co-Location to Gain Competitive Advantage

High-frequency trading (HFT) is a high-speed, high-stakes world where every millisecond matters. Finding ways to execute trades faster than the competition translates directly to greater revenue for firms, brokerages, and exchanges. Read more…

Intel Ships Drives Based on 3-D XPoint Non-volatile Memory

March 20, 2017

Intel Corp. has begun shipping new storage drives based on its 3-D XPoint non-volatile memory technology as it targets data-driven workloads. Read more…

By George Leopold

Researchers Recreate ‘El Reno’ Tornado on Blue Waters Supercomputer

March 16, 2017

The United States experiences more tornadoes than any other country. About 1,200 tornadoes touch down each each year in the U.S. Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

AMD Expands Exascale Vision at IEEE HPC Symposium

March 7, 2017

With the race towards exascale heating up – for example, the Exascale Computing Program PathForward awards are expected soon – AMD delivered more details of its exascale vision at last month’s 23rd IEEE Symposium on High Performance Computer Architecture. The chipmaker presented an “Exascale Node Architecture (ENA) as the primary building block for exascale machine” including descriptions of component, interconnect, and packaging strategy along with simulation benchmarks to bolster its case. Read more…

By John Russell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Leading Solution Providers

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This