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I'm disappointed by this too. The FTC fined Peak Brain Training competitor Lumosity $2 million because it "deceived customers about the cognitive and health benefits of its apps and online products."[1] I was more hopeful about the app if it was standalone.

As for the code being open sourced, the results of the research should be publicly available but the material of the research is an asset of the university (like how the computers and beakers used in other experiments wouldn't be given away to the public). Commercialization of research ("tech transfer") involves additional costs and risks that are taken on by research institutions, researchers, and private entities. This PR announcement was likely not coincidentally following the release of the app to the public. There was likely additional costs outside of the original scope of research to make the app robust for public usage outside of the experiment setting.

Personally, I'm disappointed that something that sounds promising may not have a chance to stand on its own as an example of a viable application when other "brain training" apps have shown their more akin to placebos rather.

    [1] https://www.statnews.com/2016/01/05/brain-training-lumosity/


There's no reason to believe that this new app is any less snake oil than Lumosity

Its story has the same trajectory


I agree. Summary of findings:

“Test subjects who spent hours practicing [insert proprietary game here] scored better when tested on different games that require similar skills.”

Smells like a poorly designed experiment salted with commercial interests.


Yup. A similar thing happened with a training/game called the "N-back" test that allegedly could improve executive function and IQ!

Turned out the test of IQ was Raven's Progressive Matrices and it's never been decisively validated

https://en.wikipedia.org/wiki/N-back#Use_in_tutoring_and_reh...

https://hn.algolia.com/?query=n-back&sort=byPopularity&prefi...


Dual N-back is reported to improve working memory, which can help improve IQ. A excellent report is here https://www.gwern.net/DNB-FAQ


That's great except Dual N back is, at least for me, impossibly hard to play. And I have in my estimation a good working memory that enables me to work on multiple abstraction levels simultaneously without losing track.

Are there any tips on how to not miserably fail?


That's vague. Do you have specific examples of flaws in methodology or analysis in the published peer reviewed results here [1]?

[1] https://www.frontiersin.org/articles/10.3389/fnbeh.2019.0000...


Very cool analogy about the beakers and equipment. Something I never considered.




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