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Do you multitask? I’m not talking about literally doing two things at once, like emailing while talking on the phone, or playing the trombone while washing the dishes. I’m talking about the more common phenomenon of starting one project before you’re finished with another. For example, after I read the journal article I’ll be discussing in this post, I caught up on some email correspondence, ordered a new phone for my office, and ate lunch. Now I’m finally getting around to actually writing the post itself. Why didn’t I just read the article and then write my post while it was fresh in my mind? Wouldn’t that have been more efficient?
There’s actually been much more research about the first kind of multitasking — truly doing two things at once — than the second type, which might be better characterized as “task switching.” But the second type is no less important. Why is it that people seem to switch frequently between tasks, instead of steadfastly working on one task until it is complete?
One bit of evidence has actually come from the study of animal behavior. Suppose a bear is foraging for blueberries. Would it be more efficient for her to eat all the berries from each bush until it’s picked clean? Or would it be better to move on to a new bush when berries get harder to find on a given bush? Doesn’t it all depend on how plentiful the next bush is? If the next bush is actually sparser than the current bush, then it wouldn’t make sense to switch. How does the bear know when to move on?
In 1984, R. F. Green came up with a rule to describe an animal’s foraging behavior, using the analogy of a wind-up toy. When the bear encounters a new bush, she makes an estimate of its potential yield, equivalent to winding up the toy the first time. Then as she forages, the toy gradually winds down, but each new berry counteracts that by adding a tiny twist of energy to the toy. If the toy runs completely down before a new berry is found, then she moves on to a new bush. Otherwise, she sticks with the current bush.
Does this rule apply to human task-switching behavior as well? Stephen Payne, Geoffrey Duggan, and Hansjörg Neth developed a simple task to find out. They gave 72 students a 10-minute task: find as many words as possible from each of two separate sets of seven letters. One of the sets was “easy” (the letters LNAOIET), which they had found could create 53 Scrabble words recognizable by a typical college student. The other “hard” set (ESIFLCE) could only create 23 words.
The students were divided into three groups. The first group was required to spend exactly 5 minutes on each task, one after the other. The second group could freely switch between the two tasks whenever they chose, but was again required to spend exactly 5 minutes on each task. The final group could switch between tasks, and was allowed to spend as much time as it wanted on either task (out of the total of 10 minutes). Logically you might expect this last group to spend more time on the easy task, since they could generate more words this way. Here are the results:
As predicted, those in the third group (free switch, flexible time) spent more time on the easy task. But surprisingly this didn’t give them an advantage in number of words generated. The group that was forced to spend equal time on each task generated more words. So it appears that while we do try to optimize the time we spend on each task, it doesn’t necessarily make us more efficient.
Undaunted, Payne’s team proceeded to examine the process people do use to decide when to switch tasks. Does Green’s model fit their data? To find out, they examined a key component of the switch from one task to another: How long do people spend trying to find a new word before giving up and switching to the other task? Green’s model (which he expressed mathematically in addition to the “wind-up toy” analogy) makes a specific prediction, and the following chart compares Green’s prediction with the actual data from this study:
The large area surrounded by the dotted line shows what Green’s model predicts, and the smaller elliptical area shows Payne et al.’s data. The two ranges are significantly different from each other — Green’s model doesn’t predict what people actually do.
So Payne’s team considered an additional factor. They reasoned that people often switch between tasks when they finish an individual subtask (just as I took a break from working on CogDaily when I finished reading this journal article). What constitutes a subtask in this study? When a new word is created. The researchers developed a new model incorporating this element and found that it matched their data! They argue that humans use two independent bases to decide when to switch between tasks — both Green’s “giving up time” and the completion of a subtask.
In another experiment, the team found similar results for a completely different pair of tasks — solving easy and hard word-search puzzles.
These very simple models give us some insight into why we switch tasks before we finish them, but of course they’re just the beginning of the inquiry of how we get work done. After all, they don’t explain why I often procrastinate by surfing the web, or why I sometimes repeatedly check my email and answer even the most trivial messages instead of doing the work I should be doing. That will have to wait for their next study… unless they decide to move on to a different line of research first!
Stephen J. Payne, Geoffrey B. Duggan, Hansjörg Neth (2007). Discretionary task interleaving: Heuristics for time allocation in cognitive foraging. Journal of Experimental Psychology: General, 136 (3), 370-388 DOI: 10.1037/0096-3445.136.3.370
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. But to me, this looks like a “cost of context switch” algorithm.
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