There’s a good chance you’re multitasking right now. A second tab is open. A notification just flashed. You half-considered checking your phone in the time it took to read that sentence.
Most of us believe we can do this. That the modern brain has adapted to embrace distraction. That switching between tasks, monitoring multiple inputs, and splitting attention across screens is just how work happens now.
Employers list it as a desirable skill. Employees note it on their CVs. And L&D teams design training that competes for attention alongside everything else on their learners’ screens. Yet the research says every part of this is wrong.
What we call multitasking isn’t multitasking at all. It’s task switching. And it comes with a cognitive cost that most people dramatically underestimate. For anyone who designs learning experiences, the implications are significant.
So go on. Close the other tabs. This one needs your full attention.
What is Multitasking? (It’s Not What You Think)
The term “multitasking” was borrowed from computer science, where it describes a processor handling multiple operations simultaneously. The analogy flatters us. After all, human brains don’t work like processors.
When most people say they’re multitasking, they mean they’re performing two or more cognitive tasks simultaneously. The belief is that the brain can process multiple streams of information in parallel, giving adequate attention to each.
It can’t. When it comes to conscious, high-level tasks (like reading, writing, or learning), the brain processes information serially, not in parallel. This means it can only attend to one stream of information at a time.
What feels like doing two things at once is actually doing one thing, stopping, switching to another, and then switching back. Researchers call this task switching, and it involves two distinct cognitive stages:
- Goal Shifting: Telling yourself “I want to do this now instead of that”.
- Rule Activation: Turning off the mental rules for the previous task and loading the rules for the new one.
Both stages happen below conscious awareness. Both take time. And both consume the brain’s limited executive resources. Each switch might only cost a few tenths of a second. But across a day of constant switching, that time adds up quickly.
The Cognitive Cost of Task Switching
If multitasking is really just task switching, the next question is: what does that switching actually cost us?
The 40% Productivity Tax
Rubinstein, Meyer, and Evans (2001) conducted a series of experiments comparing single-task performance against task-switching performance. They found that participants lost significant time when switching between tasks. And the more complex the task, the greater the loss.
According to Meyer, task switching can cost up to 40% of someone’s productive time. That’s not a minor inefficiency. It’s nearly half the working day, not lost to distraction itself but to the invisible overhead of switching between tasks.
The Stanford Surprise
In 2009, researchers Ophir, Nass, and Wagner at Stanford set out to discover what heavy multitaskers are better at. They tested 262 students, comparing heavy media multitaskers against light multitaskers on memory, attention filtering, and task switching.
The assumption was that frequent multitaskers would show superior cognitive control. They practise it constantly, after all. Instead, they found the opposite.
Heavy multitaskers were worse at filtering irrelevant information, worse at organising their working memory, and (surprisingly) worse at switching between tasks. As Eyal Ophir put it: “We kept looking for what they’re better at, and we didn’t find it.”
The 23-Minute Recovery
Gloria Mark, a researcher at UC Irvine, studied knowledge workers in real office environments and found that after a single interruption, it takes an average of 23 minutes and 15 seconds to fully refocus on the original task. That’s enough time to watch an entire episode of The Office.
Her follow-up research found that the problem is accelerating. The average time a person spends on a single screen before switching has collapsed from 2.5 minutes in 2004 to just 47 seconds today. If we’re switching that often, that means we never reach full cognitive recovery before the next interruption arrives.
It’s not a cycle of distraction and refocus. It’s permanent shallow processing.
Why It Happens: Cognitive Load Theory
The underlying explanation is cognitive load theory. The brain’s working memory (the system responsible for holding and processing new information) has a fixed and limited capacity.
Every task switch consumes some of that capacity for goal shifting and rule activation. Every notification diverts some of it to assessing relevance. And every open tab holds some of it in reserve.
What’s left for the actual learning (encoding, comprehension, and transfer) is whatever the brain has after everything else has taken its share. And for most people in most modern work environments, that share is a fraction of what’s available.
In other words, the brain isn’t failing. It’s simply being asked to run five programmes on a processor that’s built for one.
What About Supertaskers?
If you’re reading the research above and thinking “that doesn’t apply to me”, you’re not alone. Most people believe they’re above average when it comes to multitasking. Case in point: Sanbonmatsu et al. (2013) tested 277 participants and found that 70% rated their multitasking ability as above average.
However, perception and reality are two different things.
Watson and Strayer (2010) at the University of Utah tested 200 participants in a high-fidelity driving simulator, asking them to drive while simultaneously performing a demanding memory and maths task.
- For 97.5% of participants, performance dropped significantly.
- Braking reaction times increased by 20%.
- Following distances grew by 30%.
- And memory performance declined by 11%.
But 2.5% showed something remarkable: zero performance degradation. Watson and Strayer dubbed them “supertaskers”. They’re a tiny minority who genuinely can handle two cognitively demanding tasks without cost.
Now before you self-diagnose: the researchers found that most people who believe they’re excellent multitaskers are actually the worst at it. Indeed, those who multitask the most often do so because they’re less able to block out distractions and focus on a single task.
The habit wasn’t a strength. It was a symptom. So ask yourself this: are you really part of the 2.5%?
Can the Brain Learn to Multitask?
More recent research suggests there may be a longer path to multitasking than raw talent. A 2026 study by Riesenhuber and colleagues at Georgetown University found that the brain can develop a workaround. It just takes time and repetition.
In their experiment, participants spent five to ten weeks sorting computer-generated images, repeating the task over 30,000 times. At the start, brain imaging showed the prefrontal cortex (the brain’s one-thing-at-a-time processor) doing the work.
However, by the end, the task had migrated to the temporal cortex. This is the brain region involved in long-term memory. The prefrontal cortex had essentially automated the task and offloaded it, freeing itself up for something else.
This explains why experienced drivers can hold a conversation while navigating. They’ve practised enough that it no longer requires conscious attention.
But habit formation takes real work. And the researchers found considerable variability between individuals in how quickly (or indeed whether) the transfer occurred.
The implication for your training design is important. You cannot simply assume your learners have automated the background tasks competing for their attention. Unless those tasks have been practised to the point of automaticity, every switch has a cognitive cost.
How Multitasking Affects Learning and Retention
As you might imagine, the implications for training are severe. And largely unacknowledged.
Most eLearning is designed to be consumed alongside everything else on the learner’s screen. The module sits in one tab. Email sits in another. Slack or Microsoft Teams is open. Their phone is within easy reach. The implicit assumption is that the learner can handle all of this simultaneously.
They can’t. And every time they switch away from their training, they pay the switching cost. Executive resources are consumed by the goal shift and rule activation. Working memory is disrupted. The material they were encoding is interrupted mid-process.
Worse still, when they switch back, they don’t resume where they left off. They resume several cognitive steps behind, and they need time to rebuild the context. It’s not that the training is badly designed. It’s because your learners’ environments are poorly optimised.
If this sounds like the desirable difficulties framework — it isn’t. Task switching provides the wrong sort of difficulty. The effort goes into managing the switch, not into encoding, retrieval, or comprehension.
It’s the cognitive equivalent of running on a treadmill while trying to read. There’s no doubt that you’re working hard, but the effort isn’t going where the learner needs it to. This is an undesirable difficulty in its purest form.
This also has implications for how your training is structured. Long, unbroken modules that demand sustained attention are fighting a losing battle against the learner’s environment. After all, Mark’s research shows attention spans have collapsed to under a minute between switches.
Training design needs to work with that reality, not against it.
How to Design Training for Focused Attention
If the brain can only attend to one thing at a time, training design should respect that constraint rather than ignore it. Here’s how you can achieve this goal.
- Design for Short Bursts: Microlearning isn’t a trend. It’s a response to how attention actually works. Short modules (5-10 minutes) that deliver a single concept, followed by a retrieval event, work with the brain’s attention limits, supporting better knowledge retention.
- Build Retrieval Into the Rhythm: Any retrieval event (a quiz, a flashcard, a knowledge contest, etc.) forces the brain back into focused engagement with the material. It also creates a natural breakpoint: complete the retrieval, then switch if you need to.
- Eliminate Competing Channels: If a module contains high-stakes information (think compliance, safety, product knowledge, etc.), then design the environment to reduce competing inputs. This includes full-screen delivery, disabled notifications, and timed sections.
- Embrace Game Mechanics: The dopamine response triggered by game mechanics (such as points, streaks, and leaderboards) creates intrinsic motivation to stay focused. Gamification doesn’t just make training more engaging. It makes task switching less appealing.
- Batch Rather Than Scatter: Training distributed as three 10-minute sessions across a week will produce better outcomes than a single 30-minute session. This isn’t just because of the spacing effect. It’s because each shorter session is more likely to receive the learner’s full attention.
The Myth of Multitasking and Neurogogy
The myth of multitasking reinforces why neurogogy matters. Every principle in the framework is designed to work with the brain’s constraints rather than against them.
Attention is finite. Working memory is limited. Encoding requires focus. Training that ignores these constraints only produces the illusion of learning. But training that respects them produces the real thing.
The Impact Suite is engineered around this understanding. Think short, focused learning events with built-in retrieval, gamified engagement that sustains attention, and adaptive delivery that works with how the brain actually processes information.
Final Words
For 97.5% of us, multitasking is a myth. Not because we don’t attempt it, but because our brains simply aren’t built for it. What we experience as multitasking is actually task switching, and each switch carries a heavy cost. This includes lost time, reduced accuracy, weaker encoding, and fragmented attention.
Yes, the brain can learn to automate certain tasks through extensive repetition. But that takes thousands of hours of practice, and these are not the conditions under which most training is consumed. The Georgetown researchers were clear: this isn’t a shortcut. It’s a slow, effortful rewiring that varies enormously between individuals.
For L&D professionals, the practical takeaway hasn’t changed. Design shorter. Build in retrieval. Reduce competing inputs. And stop assuming your learners can give you their full attention when everything else on their screen is fighting for it.
The brain does one thing at a time. Design your training accordingly.
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