We know a lot about attention, which has been studied for well over a century. William James, the father of psychology, in his Principles of Psychology (1890) gave a detailed account of attention, and Pillbury, in his book Attention (1908), outlines many of the ideas that were later confirmed by research. Behaviourism dampened interest in what was seen as too subjective a subject for a long time but Broadbent’s Filter theory had impact in the 60s and attentional capacity was the adopted model in the 1970s. With Daniel Kahneman, best known for his work on bias, attention theory took a different turn. He saw ‘effort’ as important element in attention, something that could be encouraged and trained and it was cognitive psychology and recent attention on attention in UI and UX design that have led to intense scrutiny.
Attention is to selectively focus your mind and effort on that which has to be learned. Attention matters as it is what manages new data that has to be processed in working memory. Focused attention narrows down input from sensory memory and your recall knowledge from long-term memory. At any moment in time attention is what determines what is processed. We must always be aware that attention is the bottleneck through which everything must go in learning, first into working memory, then if you successfully learn, into long-term memory. The selective nature of attention is what regulates and limits cognitive overload.
Remarkably, although the brain takes in 11 million bits of information a second, we only process around 50 bits, even when reading or doing a complex task like playing the piano. Working memory is a complex and efficient filter with an actual attention span of around 20 seconds and we can only manipulate around 3-4 things at any one time. It is therefore time and quantity limited and highly selective. This is the essential issue that needs to be addressed in learning experience design – dealing with this filter and these limitations.
Attention is also proactive. Effects like The Cocktail Party Effect allow us to attend with laser-like focus on one person in conversation even in a busy room. Indeed, attention in learning, really only works in terms of meaning and deeper processing when it attends to one thing. Even the simplest of complications and overloads, such as reading out the words that appear on the screen in online learning can confuse and actually inhibit learning. One of the great mistakes in designing learning experiences is the failure to recognise that less is often more.
This attentive focus is highly selective and subject to selection, then interpretation on the way to consciousness. Many will have seen how actively selective attention can be when watching the famous ‘basketball’ video. You are cued to count the passes and miss the Gorilla walking through the group. Our selective bias is set by probability of events happening, expectations, perceived value, even recent context and memories. Mind readers and magicians work all of this to their advantage!
So, over a century of research has shown that attention is not one thing but a very complex phenomena. We select inputs, interpretations of those inputs, then select plans of action and actions themselves. Attention is tied up with motivation, interest, feelings and ultimately action. It is the basecamp for understanding how learning experiences should be designed.
It is time to introduce another word, that is often confused with psychological attention – arousal. Arousal is different from attention in that it refers to the rise or fall in perceptions or physical activity (some quite specific such as sexual arousal). Arousal occurs when we wake up and our mind rises into consciousness. Arousal is momentary and common, when, for example, driving a car or riding a bike. It can also fall due to fatigue. A great deal of work was done on this during the war, when operators had to pay attention for long periods listening or looking for sounds or signals. Fatigue sets in and arousal falls, leading to errors. The opposite happens with over-arousal, where one also fails in tasks. It is thought that stress may be a form of over-arousal. It is thought, therefore, that arousal is an upside down U shape, with optimal operation at the top of the bend.
What can we learn from these ruminations on arousal for learning? Learning needs a modicum of arousal to reach an optimal level and a reasonable level of arousal appears to be good for retention in long-term memory. So designing learning experiences that raise through arousal, then settle at optimal attention seem sensible. However, and this is important, there is also evidence that over-arousal can inhibit learning, Sherwood (1965). Virtual Reality and Gaming, for example, can induce over-arousal and actually inhibit learning. The lesson here is that arousal to a level of optimal attention is advisable but not beyond that optimal level, as performance may fall.
Should you play music while learning
When thinking practically about attention it is useful to take a concrete example and a common one. Should you listen to music when learning? These are two separate, attentional processes both take up cognitive bandwidth and load. Remember that multitasking is largely a myth.
There are those who extoll the Mozart Effect, extolling the virtues of playing Mozart to learners while they learn.This was sparked off by a paper in Nature by Rauscher, Shaw and Ky (1993), which showed a small improvement in spatial reasoning score (very specific), the effect lasted no longer than 15 minutes, then disappeared. The theory also disappeared, as several follow up studies could not replicate the effect. Rauscher herself, disclaimed the idea, saying that they had made no claim linking the playing of Mozart to intelligence. Steele in a 1999 put the nail in the coffin by showing that such effects are merely the result of short-term and temporary ‘enjoyment arousal'.
But education can never resist a fad and there are always people who can't let a good bandwagon pass, in this case Don Campbell, who published The Mozart Effect (1997) and The Mozart Effect for Children (2000). These books are, quite simply, bogus. His claims bear no resemblance to the actual research and, if you have this idea floating around in your brain, it’s largely down to him trade-marking the effect, then publishing these books, that were then taken up by ill-informed journalists. This is how it ended up in the minds of so many parents and teachers. It was even funded and applied in some states in the US, notably Georgia and Florida.
On the general proposition, that listening to music helps one learn, we also have to be very careful. There is a large and complex literature on this subject, testing the effect of music on various cognitive phenomena and there is some evidence that it improves mood, even motivation, but one must be careful when it comes to actual learning.
Moreno and Mayer (2000) tried online learning with the following groups:
Learning with music
Learning with sounds
When retention and transfer were tested the groups with ‘music’ performed worse than those without music. This is a well known phenomenon where cognitive overload inhibits learning. It's to do with the overloading of working memory, especially with spoken words. One quick experiment you can do with your kids, or students, is to take a random page from a book on a subject they are unfamiliar with. Now tell them to read it in silence. Now choose another page and ask them to read it while repeating the word ‘boing-boing’ over and over. They will be unable to meaningfully learn from the text. The reason is the overloading of working memory, the phonological loop to be exact. Music takes up valuable bandwidth, therefore inhibits learning.
In this interesting study, silence is used as a control, along with the two major components in popular music - music and lyrics. Perham and Currie (2014) created four groups:
Music without lyrics
Music with lyrics they liked
Music with lyrics they disliked
Revising in silence was significantly better than revising while listening to music with lyrics (liked or disliked). Revising to 'music without lyrics' produced better scores than revising to 'music with lyrics'. In the end, revising in 'silence' predicted learning outcomes better than the other groups. Reaves et al., (2015) found that adults performed significantly worse on the task when exposed to any kind of music.
There is lots of bad advice around study techniques that focus on superficial, low retention study methods and ignore attention, effort, retrieval and deliberate practice. No doubt some wag will tell us that music is good for those with an auditory learning style... that's also bullshit.
Design for limited attention
Users spend a lot of time online and that time is increasing. This means you are up against a lot of competition for attention, from some of the largest companies in the world who know about attention and spend significant sums fine tuning their service so that you don’t tune out.
The design process is full of trade-offs. You want their attention but not too much as too much arousal will hinder learning. There’s an optimal balance between getting and maintaining attention, which is limited, while not overloading the brain.
First the context and environment. Post-prandial dips in attention are well known, not too hot room temperatures and morning sessions all help, for physiological reasons. But motivation really matters a lot. Motivation is what largely leads to attention, not the other way round, as is sometimes assumed. What kills attention is cognitive overload, where too much is presented, dissonance or confusion sets in or the mind is over-stimulated. Screens with too much media – video, animation, text, sound and effects can easily lead to cognitive overload problems. Media rich does not mean mind rich. Even simple combinations such as asking learners to read text while hearing it spoken can be a problem as dividing attention is bad news. Less is almost always more.
Cues guide attention. Highlights, colour, arrows, bold, italics, underlining, font style, font size and ordering (generally top left to bottom right), layout, framing, movement and so on, are just some of many design techniques used to cue components. This matters more in learning than other tasks, as novices, those who don’t know much in any domain, really do need cued guidance to keep them attentive and learning.
The formatting of text, labels and graphics, so that they are intimately related, helps enormously with attention. Mayer and Moreno (2003) provide research supporting Nine ways to reduce cognitive load in multimedia learning. As we have separate systems for processing image and verbal material, dual-channels, each channel has limits to the amount they can process. Meaningful learning needs to build connections between images and verbal representations. However, cognitive overload commonly results in inhibiting learning. Mayer has published over 500 studies in online learning and has many solutions these problems. He is a must read for learning experience designers.
Mousavi, Low and Sweller (1995), found that explanatory audio can also relieve cognitive load. We use audio only introductions to online learning courses in WildFire, which gains attention, calm the learner and diminishes any anxiety they may feel about the coming learning experience. This is used, for example, by the Khan Academy in teaching mathematics and other subjects. There is no talking head, only the maths and audio. What really matters is the coordination of the elements, so that they are coherent and easily assimilated.
Organizers, an attention-focusing tool particular to learning, that summarise, often in an infographic, what is to come or what has just been covered, also helps cue and focus attention (Asubel; 1960). This gives the learner a high-level, bird’s-eye view of the content and helps with processing to and from long-term memory. It gives you a top-level schema into which you as a learner can fit the details. This leads into building schema in long-term memory to build expertise which takes time and effort by the learner. Schema are built by diversity of problems and effort. Worked examples (Paas & van Merrienboer, 1994), multiple perspectives and varied contexts and analogies all help, especially varying the surface features to embed the deeper principle.
Attention and technology
There has been a Renaissance of interest, dare I say attention, paid to attention in recent times, as tech companies vie for your attention online. UI and UX design on the web has become a marketable skill. Labs measure eye movements in relation to screen real estate and events, all in an attempt to first gain then hold your attention. This can increase the efficacy of online learning but the mistake is to think that it measures actual learning, retention, ability to recall or transfer. The danger is that such things are taken as proxies for learning.
Beyond this, the work being done in AI and mind-machine perception has also created a renewed interest in what it is to be attentive, as the automation of surveillance and data interpretation has become an essential part of robotics and object recognition. Object recognition is a remarkably attentive success in AI. The algorithms used to improve recognition are increasingly attentive and selective in terms of the goals they have to achieve. Exciting times.
Beyond this we have a renewed effort to use technology, in invasive and non-invasive forms. It has already led to clear health benefits for those with physical disabilities. The hope is that they will also help us identify how the brain learns, even act as aids to learning, optimising brain states, such as attention, even, in the far future interpreting and implanting memories.
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