Is It a Good Idea to Enable the iPhone’s Predictive Keyboard and Auto-correction?

A couple of months before I started university, I spent some of my evenings learning to type on a typewriter. (A year or so before I purchased a Mac Plus.) My girlfriend of the day pleaded with me to engage in more pleasurable summer evening activities with her. However, I knew that I’d be writing documents for the next four years and beyond, and that I had better learn to type. I wanted to be able to focus on my ideas so they might flow as fluidly from my brain as they needed to. My playmate decompensated; but I stuck to my guns, which delivered the intended benefits.

But like millions of people, I became a novice typist again when I bought my first iPhone…

iOS provides several features, such as predictive keyboard and auto-correction that are supposed to help with typing. Should one use them? It depends on one’s goal. If you want to improve your typing so that you can later focus on the content you are writing, then enabling the iOS predictive keyboard and auto-correct is, I predict, counterproductive. (But see the caveats below.)

In order to develop any skill, it is essential to receive feedback, attend to it, interpret the feedback, and to correct one’s behavior. The predictive keyboard and autocorrect hinder learning to type unassisted. So you will tend to repeat the same mistakes. For instance, you might frequently mix the “i” and the “o”. This can have incidental, secondary adverse effects (such as hindering the accuracy of the device’s predictions, because the more accurate you are to begin with, the more accurate its suggestions will be.)

Another problem with predictive keyboards is distraction. To use one, you need to divide your most precious and extremely limited capacity, your mental resources (including “working memory”), between the stream of text you’re monitoring on the screen, the predictive keyboard and the cognitive processes generating the content about which you are typing. This might seem like minimal cognitive overhead. However, attentional resources are extremely limited and precious.

Auto-correction has similar drawbacks. Whether or not you intend to process your device’s suggestions, your brain will, at least to some extent, attend to them. (Research suggests there is not a sharp distinction between “attentive” and “pre-attentive” processing.) The more resources you deploy to the suggestions, the fewer are available for the other components of the task. It’s like trying to swat flies while having a conversation. Enabling both auto-correct and predictive keyboard will consume more resources than just using one. Admittedly, with sufficient, and sufficiently attentive, practice you’ll become better at using these features, and they will demand fewer resources. But there will be a penalty.

To compound the problems here: odds are you will not engage in deliberate practice with these features, which means you’ll be slow to learn how to use them. (Sorry, but that’s how the brain works, which is why I dedicated 3 large chapters in Cognitive Productivity to deliberate practice.) It’s noteworthy that a proven rule of traditional touch-typing in transcription is not to look at the output document but to keep one’s eyes on the input document (the source of the transcription). What they were teaching us, which was quite valid, is the opposite of what we do when the features in questions are enabled.

Tips. To improve your typing skills you actually need to spend time offline learning how to type. Also, beware that if you flip between software keyboards and device orientation (portrait vs. landscape) you will increase the time to learn to type properly on the device. Here’s a relevant quote from a 2000 paper by Robert W. Howard (well before software keyboards were widely used) on the subject of “generalization and transfer” (a major theme of Cognitive Productivity).

Consider typing. The underlying skill is precisely structured, and a minor situational difference (such as a different-sized keyboard or different layout) may disrupt it completely. (p. 213)

Even doing something as simple as turning on or off emoji will significantly increase your error rates. Your typing speed and accuracy might also take a hit when you switch to a device of a different size, as mine did when I upgraded from the iPhone 5 to the iPhone 6S. (But contrast Kim & Thamsuwan, 2014.)

Portrait vs. landscape keyboard; 2 keyboards (U.S. + Emoji) vs. 1 (U.S.).
Figure 1: Portrait vs. landscape keyboard; 2 keyboards

I’m not saying “Never enable these features.” Actually, I don’t want to give advice here; I’m mainly highlighting some relevant psychological considerations. Not everyone can or should take time off to learn to type better. If you want to improve your typing skill, then should you use a typing tutor app? The ones I’ve tried are not good enough to recommend. Teaching oneself to type is not particularly difficult. It’s even somewhat interesting: it’s a safe, low-cost opportunity to peer into and reflect upon the very important process of skill acquisition — a cognitive analog of the biologist’s drosophila. Furthermore, based on the fact that users seem to make systematic errors in typing on smartphones (e.g., Azenkot & Zhai, 2012), there seems to be great potential for rapidly improving typing with careful deliberate practice. What’s the payoff? Being able to dedicate more of your precious mental resources to the meaning of what you are writing and fewer to the typing itself.

If you’re to take time to practice, you might as well practice transcribing informative materials. Keep track of your typing mistakes as you make them and practice correcting them using principles of skill acquisition.

You might then selectively enable the predictive features as a function of the type of task (e.g., its working memory demands), your typing skill level, urgency, importance of accuracy. There will come a point where you might choose to keep the predictive typing feature on most of the time.

More caveats. While my predictions are derived from theories of expertise that are based on a large amount of empirical data (on skill acquisition in various domains, expertise and working memory), and while there is related empirical research, my predictions would need to be verified empirically. For instance, in modern typing studies, auto-correction tends to be disabled. (It is still interesting to note that typewriter typing was one of the first types of expertise studied by psychologists.) It’s also possible that technology will change too fast for you to reap the benefits of deliberate practice, given the transfer and generalization limits mentioned above.

Beyond Transfer in Typing: On Broader, Deeper, Affectively Complex Learning

My teen-aged decision to teach myself to touchtype perhaps reflected and enhanced my “meta-effectiveness”, i.e., my ability and motivation to develop myself. It certainly boosted other aspects of my cognitive productivity: I had previously done a lot of my thinking by writing by hand; since then, I’ve been doing a lot of my thinking and problem solving while typing.

I was also presented, in the events alluded to in the first paragraph of this article, with the opportunity to learn how and when to exit from intimate relationships — my successful adolescent experience proved to be much more difficult to generalize from in adulthood. A general theory of meta-effectiveness needs to explain this much more affectively complex type of “transfer” problem, how one can fail to achieve such broader transfer, and to show how such broad transfer can be achieved. That is a tall order. In Transfer of learning, Robert E. Haskell presented an excellent, expansive theory of transfer. In Cognitive Productivity I also tried to explain and improve meta-effectiveness. Both of those books deal with a variety of cases, including psychotherapeutic learning.

This being the beginning of the year, I invite you to identify and try to understand and compare a small and a big transfer problem; compare also one where you were successful with one where you’d do a lot better this time around. You might find these difficult questions to be potent spurs for (learning about) personal development.

References

Azenkot, S., & Zhai, S. (2012). Touch behavior with different postures on soft smartphone keyboards (pp. 251–260). Proceedings of the 14th international conference on human-computer interaction with mobile devices and services. http://dl.acm.org/citation.cfm?id=2371612

Beaudoin, L. P. (2015), Cognitive Productivity: Using Knowledge to Become Profoundly Effective. BC: CogZest.

Chaparro, B. S., Phan, M. H., & Siu, C. (2014). User performance and satisfaction of tablet physical keyboards. Journal of Usability Studies, 9(2), 70–80.

Daley, S. E., Burge, D., & Hammen, C. (2000). Borderline personality disorder symptoms as predictors of 4-year romantic relationship dysfunction in young women: Addresing issues of specificity. Journal of Abnormal Psychology, 109(3), 451–460.

Haskell, R. E. (2000). Transfer of learning: Cognition and instruction. San Francisco, CA: Academic Press.

Howard, R. W. (2000). Generalization and transfer: An interrelation of paradigms and a taxonomy of knowledge extension processes. Review of General Psychology, 4(3), 211–237. http://doi.org/10.1037//1089-2680.4.3.211

Kim, J. H., Aulck, L., Thamsuwan, O., Bartha, M. C., & Johnson, P. W. (2014). The effect of key size of touch screen virtual keyboards on productivity, usability, and typing biomechanics. Human Factors: the Journal of the Human Factors and Ergonomics Society, 56(7), 1235–1248. http://doi.org/10.1177/0018720814531784

Speelman, C. P., & Kirsner, K. (2005). Beyond the learning curve: The construction of mind. New York, NY: Oxford University Press, USA.

Footnotes


In Cognitive Productivity I explored several concepts related to meta-effectiveness, such as fluid expertise (Bereiter’s term), effectance (Robert White’s), and fluid rationality (Keith Stanovich’s). They are summarized on my SFU blog: Meta-effectiveness, Effectance, Mindware and Other Key Concepts for Understanding the Development of Adult Competence.

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Luc P. Beaudoin

Head of CogZest. Author of Cognitive Productivity . Cognitive productivity consultant and public speaker. Adjunct Professor of Education, Simon Fraser University Co-founder of CogSci Apps Corp. See About Me for more information.

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