Brain training, like computational thinking, is unlikely to transfer to everyday problem-solving

March 18, 2016 at 7:26 am 5 comments

In a recent blog post, I argued that problem-solving skills learned for solving problems in computational contexts (“computational thinking”) were unlikely to transfer to everyday situations (see post here).  We see a similar pattern in the recent controversy about “brain training.”  Yes, people get better at the particular exercises (e.g., people can learn to problem-solve better when programming). And they may still be better years later, which is great. That’s an indication of real learning.  But they are unlikely to transfer that learning to non-exercise contexts. Most surprisingly, they are unlikely to transfer that learning even though they are convinced that they do.  Just because you think you’re doing computational thinking doesn’t mean that you are.

Ten years later, tests showed that the subjects trained in processing speed and reasoning still outperformed the control group, though the people given memory training no longer did. And 60 percent of the trained participants, compared with 50 percent of the control group, said they had maintained or improved their ability to manage daily activities like shopping and finances. “They felt the training had made a difference,” said Dr. Rebok, who was a principal investigator.

So that’s far transfer — or is it? When the investigators administered tests that mimicked real-life activities, like managing medications, the differences between the trainees and the control group participants no longer reached statistical significance.

In subjects 18 to 30 years old, Dr. Redick also found limited transfer after computer training to improve working memory. Asked whether they thought they had improved, nearly all the participants said yes — and most had, on the training exercises themselves. They did no better, however, on tests of intelligence, multitasking and other cognitive abilities.

Source: F.T.C.’s Lumosity Penalty Doesn’t End Brain Training Debate – The New York Times

Entry filed under: Uncategorized. Tags: , , , .

ICER 2016 Call for Papers: Abstracts due April 15 Forbes weighs in on Computational Thinking: I’m one of *those* critics!

5 Comments Add your own

  • 1. fgmart  |  March 20, 2016 at 7:53 am

    Isn’t the point of CT that you start to see the world in terms of how you do use computational approaches to get things done? So it’s a mindset, a tool belt. “When all you have is a hammer, everything looks like a nail” — but in a good way. You don’t just have the one hammer; you have a whole set of CT tools, and are bringing these approaches to solving problems in a unique way (computational).

    • 2. Mark Guzdial  |  March 20, 2016 at 8:28 am

      If you actually mean “tools” (as in, “I know how to use Python to do the graphing I need for my work”), then I agree. If you mean “tools” as in “ways of thinking,” then no, I don’t agree. We may think we do that, but we fool ourselves.

      Here’s the part in the original CT paper that I don’t believe — I don’t believe that normal people will think about prefetching and caching when packing a backpack, even if they know prefetching and caching:

      “When your daughter goes to school in the morning, she puts in
      her backpack the things she needs for the day; that’s
      prefetching and caching. When your son loses his
      mittens, you suggest he retrace his steps; that’s backtracking.
      At what point do you stop renting skis and
      buy yourself a pair?; that’s online algorithms. Which
      line do you stand in at the supermarket?; that’s performance
      modeling for multi-server systems.”

      • 3. chaikens  |  March 20, 2016 at 1:15 pm

        Turning it around, it’s compelling for CS professors and texts to teach about caching etc. by likening them to everyday activities and to help their students remember the word “cache” by referring to its usage in romantic pirate stories. It would be cool to investigate whether teaching how Turing modelled computation done by people using paper may help to teach the difficult notions of variables and how their values are affected by code. While CS notions might not be very helpful tools in everyday thinking, the imaginative recognition and application of everyday experiences to scientific questions is sometimes helpful in science. And they make science more fun.

  • […] fool ourselves about what we learn from and what we don’t learn from.  It’s like the brain training work.  We’re convinced that we’re learning from it, even if we’re not. This student […]

  • […] Roger Schank (famous AI and cognitive science researcher, the guy who coined the term “learning sciences”) is putting his expertise to the task of creating cyberattack defenders.  The description of his process (linked below) is interesting.  It has all the hallmarks of his work — innovative, informed by research, driven by concrete tasks.  Notice the strong claim that I quoted below.  We shouldn’t be aiming for general cyber attack defense skills.  These skills are going to be industry-by-industry specific.  He’s directly informed by the research that suggests that these skills are unlikely to generalize. […]


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