Steering Away from Cognitive Overload (part 1)

(based on my article for ATD’s Science of Learning blog; Part 1 of 2)

Trying to learn important information through multimedia can feel like driving through a strange city for a big job interview.

CC-licensed image by Joshua McKenty

As the learning designer, you’re not the one heading to the interview, but you do select the car and choose the route. You not only give directions, but also mark the lanes, erect street signs, and string up the traffic signals. Whatever roads and vehicles the driver encounters, you put there. And if you make mistakes, the driver won’t arrive on time or do well in the interview.

With that happy thought, let’s discuss how to manage cognitive load. In other words, how can you reduce demands on working memory and maximize a learner’s chances of success?”

Research suggests—with a very loud “ahem!”—that multimedia razzle-dazzle can actually work against effective learning. Even background music can interfere with success, the way sound from the car radio makes it harder for you to navigate through a work zone. In “Nine Ways to Reduce Cognitive Load in Multimedia Learning,” Richard E. Mayer and Roxana Moreno explain what they mean by cognitive load and offer a three-part theory for how to make information meaningful.

Meaningful learning: how it happens, how it doesn’t

Multimedia learning, according to Mayer and Moreno, involves delivering information through words (printed or spoken) and images (drawings, photos, animations, videos). By “meaningful learning,” they mean you’re able to apply that information to a new situation.

How that happens, they say, is affected by three factors:

  • The dual-channel assumption says that we handle incoming information through two channels: one for words and one for images.
  • CC-licensed photo by Mr.TinDC

    The active processing assumption says that we need to do significant mental work in order to learn. We decide what to pay more attention to. And, for things that make the cut, we go on to figure out what they mean and how they interact. This processing is how we create a mental construct for what we’re learning, and connect that construct to existing knowledge.

  • The limited capacity assumption says that we can only work with so much at a time in a cognitive channel. We can only handle so many words or so many images at a time.

Assuming you’ve done some active processing with those three points, you can already see the implications. Learning is challenging enough; the way we present information through words and images can help or hinder.

Mayer and Moreno also identify five ways overload can happen, and they present strategies to overload. I’ll discuss three here, and two more in the next post.

Overload in a single channel

Imagine that a section of your multimedia lesson has most of its information in a single channel–say, a large block of text. Let’s say it’s all necessary information. In fact, because it’s necessary, you decide to include a voiceover to reinforce the print message.

You’re asking the learner to read and listen at the same time. The two streams of verbal information —  printed text and spoken words — compete for working-memory resources and can overwhelm the verbal channel.

Assuming all the information truly is relevant, Mayer and Moreno suggest off-loading some content: move some from verbal to visual. Use images to anchor key concepts, reduce the printed text, and let the audio channel carry the message. “Students understand a multimedia explanation better when the words are presented as narration rather than as on screen text,” write Mayer and Moreno.

Remember our interview candidate? She navigates traffic more smoothly with a GPS that combines spoken directions with a graphic map—far more so than if she had highly detailed, text-only directions.

What happened in the researchers’ experiments? One way to express the strength of an outcome is through “effect size.” Using one common measure, Cohen’s d, an effect size of 0.1 – 0.3 would be small, 0.3 – 0.5 would be moderate, and greater than 0.5 would be significant.*  In six experiments involving offloading, Mayer and Moreno report an effect size of 1.17.

*  In Cohen’s terminology, a small effect size is one in which there is a real effect — i.e., something is really happening in the world — but which you can only see through careful study. A ‘large’ effect size is an effect which is big enough, and/or consistent enough, that you may be able to see it ‘with the naked eye’.

From Statistics for Psychology

Overload in both channels

What if both channels, verbal and visual, have too much essential information? No matter how much you need to cover or how elegant the presentation, too much is too much. When the learner can’t process everything, she can’t organize the input into a useful mental model, let alone integrate it with what she already knows.

CC-licensed image by Gilad Lotan

Again, our driver trying to make the interview can’t easily cope simultaneously with a nagging GPS, unfamiliar street signs, shifting traffic, and a message board displaying cryptic data about a detour—even though it’s all important.

Mayer and Moreno offer two solutions. One is to segment content; break material into smaller pieces, and allow the learner to decide when to move on. An experiment broke a three-minute segment into 16 segments, linked by CONTINUE buttons. Compared with a control group, students who could choose when to continue, thus taking the time they wanted with the current segment, performed substantially better.

When segmenting won’t work, a second solution is to offer pre-training, which means providing some information ahead of time, such as the names or functions of major parts. In order to build a mental model of what you’re learning, you need a component model (how each major part works), and a causal model (how the parts affect each other). Pre-training gets you to the component model faster so it’s easier to construct your causal model.

Suppose our interview candidate has traveled to Washington, D.C. Before she gets her car, she might learn the different names for the most important freeway (I-495, I-95, the Beltway) and the meaning of “Inner Loop” and “Outer Loop.” That could help her negotiate the trip from Dulles airport to Bethesda.

(End of Part 1; Part 2 here)

Steering Away from Cognitive Overload (part 2)

This is the second part of my summary of Nine Ways to Reduce Cognitive Load in Multimedia Learning, by Richard E. Mayer and Roxana Moreno, originally published in ATD’s Science of Learning blog. 

Part 1 (here) deals with how we process information through two channels (one for words, one for images), and how overload can occur in one channel or in both.

Overload from extraneous information

(Spoiler alert: “Nice to know” doesn’t mean “good to include.”)

CC0-licenced mage from Pixabay by Broesis

Mayer and Moreno point out that “interesting but extraneous material” takes up cognitive capacity. The learner has to pay some attention—for instance, it’s hard to not listen to background music. Effort goes into deciding whether anything deserves further attention. The more this happens, the less capacity remains for learning what actually does matter.

You probably can guess what the researchers recommend: weeding. Remove the extraneous. What’s the bare minimum that people need to know in order to accomplish the skill or apply the knowledge? Force everything else to justify its inclusion.

In an animated sales-call lesson, for example, I don’t need to see the customer driving in. I don’t need an animated phone, virtual pens, and virtual paper clips. I do need a customer statement to respond to. I need time to analyze it. I need clear examples of responses and how effective they are in a situation like the one I’m seeing.

CC-licensed photo by Steve Kennedy

To me, the weeding of nonessential material is the difference between the rich but irrelevant detail of a war story and the crisp relevance of a pertinent example. Our interview candidate probably doesn’t need to know that there’s a library two blocks before she gets to Midcounty Highway; she does need to know when she gets there, the two right lanes are right-turn-only.

Granted, sometimes you can’t edit details out. Suppose you’re explaining how to operate packaging machinery in a pharmaceutical plant. Your learner will confront lots of equipment and lots of steps, along with potentially overwhelming detail in the video close-ups.

When weeding is not an option, Mayer and Moreno recommend is signaling—providing cues to the learner about how to organize the material. So, the lesson might start by breaking packaging into four stages: product into plastic blisters, blisters into cardboard wallets, wallets into carton packs, cartons into cases. In subsequent lessons, arrows or similar highlighting emphasize key components of each stage.

Overload from poor presentation

Sometimes overload results from the confusing presentation of essential information. Imagine an animation in one part of a screen and related text in another. The learner has to shift focus between the two areas, as well as figure out which parts are related to which.

From a Slideshare presentation by Lynnylu

Mayer and Moreno recommend closer alignment of words and pictures. Placing text inside a graphic, rather than alongside as a caption, aligns the explanation more closely with the visual for what’s being explained.

In a related situation, information arrives as animation, onscreen text, and audio narration. The simultaneous presentation of text and narration, which the researchers call redundant presentation, requires the learner to work at reconciling the two verbal forms while also dealing with the visual form. It’s as if our interview candidate were watching an animation of the route to follow and reading directional text while the person next to her recited those directions.

Mayer and Moreno cite studies with a significant shift as a result of reducing redundancy, such as dropping onscreen text and using only narration. An interesting twist they add is that if there’s no animation, students learn better from concurrent narration and on-screen text than from narration alone. The interpretation is that the on-screen text by itself doesn’t overload the visual channel the way it would with the animation there as well.

Overload from “Hold that thought!”

The final type of cognitive overload involves both essential processing and “representational holding.” Mayer and Moreno explain that as having to retain information in working memory. For example, if you read about the thermoforming process for drug packaging, and then watch a video showing the process, you have to keep elements of that text in memory during the video, which reduces your ability to select, organize, and integrate.

One way to avoid this overload is to synchronize—interweave text or audio with the video. Words about the sealing step should arrive as the visual does; a description of the check-weigher should come while the learner sees that device in action.

CC-licensed photo by Jamillah Knowles

Researchers cite robust evidence that “students understand a multimedia presentation better when animation and narration are presented simultaneously rather than successively.” Meanwhile, Mayer and Moreno point out that if the non-synched elements are brief—a few seconds of narration followed by a few seconds of animation—there’s less overload, mostly likely because the learner has less representational holding to do: fewer things to keep in mind from the verbal information.

But what if you can’t synchronize? Then, the recommendation is individualization, or ensuring that you have learners skilled at holding things in memory. If for your work you’re able to match “high-quality multimedia design with high-spatial learners,” you’re all set. Personally, I’m rarely able to manage that.

Final thoughts

I started by comparing the multimedia learner to someone who has to drive through a strange city to make an interview. Mayer and Moreno highlight ways that your design decisions can make that trip far more difficult than necessary. Pick up some learning principles and lessons from this research—and take off a little cognitive load.

Bilingualism and the brain

“What do you call someone who speaks three languages?”


“What do you call someone who speaks two?”


“And what do you call someone who speaks only one?”


It’s an old joke — and I once hear it from someone who mocked her own countrymen by changing the punch line to “French.” It’s here because I’ve been wondering about how many Americans are able to speak more than one language.

A 2001 Gallup poll said that about 1 American in 4 can hold a conversation in a second language.  Looking at the topic from a different angle, a 2007 report from the Bureau of the Census said that “of 281.0 million people aged 5 and over, 55.4 million people (20 percent of this population) spoke a language other than English at home.”

Of those 55.4 million, about 31 million claimed to speak English “very well”, and another 11 million said “well.”

It’s something of a moving target, then, depending on how you define bilingual. I focused on it after seeing an article by science writer Catherine de Lange. The version I first saw appeared in the Washington Post, based on a longer piece de Lange wrote in New Scientist (paywall).  De Lange’s mother, who was French, spoke French to her from infancy, and the articles have to do with the effects of bilingualism on the brain.

One study she mentions discussed “a profound difference [in brain imaging] between babies brought up speaking one language and those who spoke two.”  In essence, researcher Laura Ann Petitto says, the babies’ bilingualism seems to “wedge open” the window for learning language, making it easier for them to acquire new languages through life.

And there’s this (from de Lange’s Washington Post article):

Ellen Bialystok, a psychologist at York University in Toronto, first stumbled upon one of these advantages while asking children to spot whether various sentences were grammatically correct. Both monolinguals and bilinguals could see the mistake in phrases such as “apples growed on trees,” but differences arose when they considered nonsensical sentences such as “apples grow on noses.” The monolinguals, flummoxed by the silliness of the phrase, incorrectly reported a grammar error, whereas the bilinguals did not.

One explanation (based on work by Viorica Marian and her colleagues) is that the two languages “are constantly competing for attention in the back of the [bilingual] mind.” As a result, the brain is constantly getting “the kind of cognitive workout…common in many commercial brain-training programs.”  (Those programs require you to ignore distracting information.)

What about the long-term effect of this competition?  De Lange reports that Bialystock and colleagues found that bilinguals were slower than their monolinguals peers to show signs of Alzheimer’s — by four to five years, even after taking in factors like occupation and education.

So possibly all that activation strengthens the brain in a way that helps it resist the disease.  Not that you should try learning another language as a form of medication–though if that’s the way you look at it, enjoy.

More speculative, but just as interesting were de Lange’s comments on how a bilingual person can express himself — can behave, so to speak — differently in the two languages.  There’s a hint that the person may have the mental equivalent of two channels, one for each language.

Which probably bodes well for the bilingual Karen Matheson, who sings Canan nan Gaidheal (The Language of the Gaels).  (The song tells of the Western Isles — the Outer Hebrides — the stronghold of Scottish Gaelic.)


The psychology of swindling

It's only an illusionIn the current issue of Smithsonian magazine, Teller (of the professional duo, Penn & Teller) reveals some secrets of his art.

First he talks about the world of neuroscience and perception, into which he’s often invited as a speaker.  And he makes the point that when it comes to experimenting with human perception, neuroscientists are amateurs compared with magicians.

I recall his partner Penn Gillette saying once that they were not magicians.  They were tricksters, swindlers.  His point was that nothing in their act was magical.  They’re not exempt from the laws of physics. Instead, as magicians have done for thousands of years, they rely on trickery, on quirks of perceptions.

It’s well worth reading the original (link in the first paragraph, above) to enjoy Teller’s style and to take in the details he provides for points like these:

  • Exploit pattern recognition.  Our brains constantly seek patterns, especially when there isn’t one.  That’s why the night sky has constellations, but an evenly spaced series of dots seems to have no pattern at all.
  • Distract with laughter. What Teller’s really talking about here is a kind of cognitive overload–if you’re watching the performance and laughing at the comedy, you’re likelier to miss some small detail.  I think the same thing applies when a training exercise is sufficiently engrossing–people don’t care as much about elegant presentation and high-end graphics if the exercises feels like interesting, useful work.
  • Nothing fools you better than the lie you tell yourself.  Here, he’s talking about allowing the audience (or the learner) to reach their own conclusions, make their own judgments, even if as the “designer” he knows these will be erroneous.  For a magic act, that means the audience is all the more mystified by the effect–thus, success.  When it comes to learning, the learner is comparing a conclusion she arrived at with new data that conflicts with that conclusion.  That, gentle reader, is where the learning starts.

He goes on; you don’t need me to repeat it here.  I found the article engaging enough that I wanted to see more, and came across a 2008 article in Nature Reviews – Neuroscience.   In Attention and awareness in stage magic: turning tricks into research, Teller and several coauthors study magic tricks so that “neuroscientists can learn powerful methods to manipulate attention and awareness in the lab.”

If you’re doubtful, take a look at this demonstration by one of the coauthors, pickpocket Apollo Robbins.

I think it’s worth the 16 minutes.  Watch carefully during the first two-thirds, when (I’m not giving away much here) Robbins actually picks the pockets of a volunteer who’s pretty sure that’s what’s going to happen.  You’ll find the subsequent explanation all the more compelling.

“If I’m here (standing alongside the mark), and I want to split his attention… I’ll bring my chin up into his personal space. His head will whip up to my face, and he won’t focus on that movement (of my hands).”

Worm-brain wiring: not as simple as you’d think

Sometimes, it’s worth the whole week’s subscription to The New York Times just to get the Tuesday Science section.  (It’s certainly not worth it if you’re only going to count how often in a week the Times uses the word “famously”).

Science this week included Nicholas Wade’s article In Tiny Worm, Unlocking Secrets of the Brain, which centers on the work of Cornelia Bargmann.

I’m going to summarize the parts of the article that most intrigued me, in part because both the grunt work conducted on a 1-millimeter worm, and the complexity that work has revealed, are probably good to… well, have in mind when you read some breathless “finding about the brain” that means you should never use magenta as a font color.

Connecting the dots
(Click to see NYTimes diagram)

Bargmann has spent 24 years studying Caenorhabditis elegans.  Many neuroscientists do, in part because C. elegans has only 302 neurons.  (You, by way of contrast, have 100 billion or so.)  John G. White spent more than 10 years mapping the 8,000 connections between those neurons.

At that point, science had a neurological map for the worm, but didn’t know which connections made what happen.  It was like having the wiring diagram for an apartment building. As is, just the wires: not knowing what was connected to any outlet or socket.

Worming the information out
Cell bodies of the ILR, VL, and 2-neurons (ILR is about 2 microns wide)

Bargmann eventually tried the equivalent of flipping circuit breakers to see which lights went out.  She knew that C. elegans “can taste waterborne chemicals and move toward those it finds attractive.”  So she started killing one neuron at a time with a laser. The idea was to try to figure out what the neuron did from what the worm stopped doing.

Eventually, she did find the neuron that controlled taste.  She also discovered that C. Elegans has a sense of smell, as well.  Like rats, these worms can tell what to eat and what to avoid by scent.  Bargmann learned that neurons, and not odor receptors, controlled the move-toward-good, move-from-bad behavior.

This is tough learning.  In addition to the 302 neurons and their 8,000 connections, there’s another system of “gap junctions” involving chemical connection between neurons.

And there are neuropeptides (250 different ones) that neurons release to affect other neurons.  Which means the pattern of neural connections changes on the fly.

Cell-body image of C. elegans neurons by Thomas Boulin for WormAtlas.