More news in the department of “People are Notoriously Wrong about Themselves”, this time under the subheading of distraction and multitasking. Distractions are not the same as multitasking, but they do go hand-in-hand when working with students to improve learning strategies. There are a few myths regarding distraction and multitasking that you may want to share with your students:
People are good at multitasking. This is untrue for a few different reasons. Multitasking isn’t even how the brain operates. And even if it was, people still can’t multitask well!
Multitasking in the brain is fictional; the brain cannot parallel process cognitively heavy tasks. The brain can switch between tasks (“network switching”), but there is overhead in those transactions. In fact, former Microsoft executive Linda Stone dubbed this constant multitasking “continuous partial attention” (Gazzaley & Rosen, 2016, p. 111). Part of this misunderstanding is because the brain actually can process plenty of information at the same time. In fact, the brain does this all the time. In addition to the automatic tasks (breathing, circulation), some tasks can be automated to a reflex – like chewing gum. And walking. And both those things can be done without a decay of processing.
But if the two goals both require cognitive control to enact them, such as holding the details of a complex scene in mind (working memory) at the same time as searching the ground for a rock (selective attention), then they will certainly compete for limited prefrontal cortex resources (Gazzaley & Rosen, 2016, p. 60).
There is a popular activity you might challenge your students to engage in that wonderfully reveals the challenges with task-switching. Ask the students for volunteers who want to demonstrate how hard it is to do two things at once. Have that person stand up and count aloud from one to ten. Then have them recite the alphabet. Finally, have the student switch between the two (“1 A 2 B 3 C 4 D…”). Typically people don’t get much further than F or G. Don’t believe me? Try it yourself right now. And remind the students that they’ve been singing the ABCs for decades. Texting or engaging in social media are cognitively taxing events, much more than counting to ten.
As another example, ask your students if they have ever been at a restaurant and heard their name said at an adjacent table. Even after recognizing that the mention was just a coincidence,
…it still captures your attention against your will because of the strong bottom-up salience of your name (a limitation in your selectivity). But then it also takes time for you to withdraw your attention from this distraction, and to reallocate it to the conversation you were having at your table. Even for a simple distracting stimulus, the “recovery time” from having your attention captured takes tenths of a second. And so, the speed of attention, both its allocation and disengagement, represents yet another limitation of this cognitive control ability (Grazzaley & Rosen, 2016, p. 73).
And that example isn’t high-stakes! Imagine studying a difficult concept and then hearing an alert come in (social media, email). Not only is there cognitive overhead when it comes time to switch back to the task, but there’s also an environmental disruption. Maybe the windows on the monitor need to be closed. Maybe the student needs to switch from their phone to the computer. Maybe the distraction required them to get up. All these costs add up. A 2007 study showed that there is time delay when interrupted (partially because after an interruption people tend to digitally wander before resuming their task):
We discovered that, following an alert based suspension, subjects would often visit several applications in addition to the notifying application. We found that 27% of task suspensions resulted in more than two hours of time until resumption. In interviews, users attributed long delays to the loss of context associated with the task switch. Findings about the association between greater visibility of windows of suspended applications and faster resumption of tasks suggest that visual cues may serve as reminders to return to suspended applications. After reviewing sets of results gleaned from monitoring users,we presented design implications for reminder and recovery tools and discussed research directions (Iqbal & Horvitz, 2007, p. 9).
And while that study was conducted when interrupting technology was nascent and notifications were not as sensitive to disruptions, it does reveal pernicious behavior.
Context doesn’t matter. While people often report that their “disruption cost” (or the time it takes to reorient to a task after a disruption) is lower when the interruption is aligned with the current task, some research indicates a disruption of any type is equally costly (Mark et al., 2008, p.4). For instance, I might be reading about partial differential equations and a study partner interrupts me to ask about a particular homework question regarding partial differential equations. While I might not perceive this as distracting as if they had asked about my plans for the weekend, it is most likely equally distracting.
Technology is a distractor, but not the distractor. James Lang writes that “current reporting from people who study the brain, and especially attention and the brain, is that we don’t yet have any conclusive evidence to support the notion that human attention has suffered some architectural diminishment in today’s technological era” (Lang, 2020, p. 43). He continues “science tells us that there is a negative relationship between using more media simultaneously and working memory capacity. And we know working memory capacity correlates with language comprehension, academic performance, and a whole host of outcome variables that we care about” (Lang, 2020, p. 43). Distractions have always been present, but the more distractions we have, the better.
And now for a potent passive example of task interruption – the “to be continued” interruption! Next week we’ll look at strategies to combat disruptions that students can use to potentiate their abilities.
Gazzaley, A., & Rosen, L. D. (2016). The distracted mind: Ancient brains in a high-tech world. Mit Press.
Iqbal, S. T., & Horvitz, E. (2007, April). Disruption and recovery of computing tasks: field study, analysis, and directions. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 677-686).
Lang, J. M. (2020). Distracted: Why students can’t focus and what you can do about it. Hachette UK.
Mark, G., Gudith, D., & Klocke, U. (2008, April). The cost of interrupted work: more speed and stress. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (pp. 107-110).
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