” This suggests that individual differences in many cognitive tasks are a stable trait marker.”
There’s a new Oxford research study circulating through scientific communities and around the world. From Science (Task-free MRI predicts individual differences in brain activity during task performance), Tavor and collegues applied machine-learning principles to test subjects in a “resting state” to see how they could predict their performances on various cognitive tasks.
What was the result?
They could predict subject’s responses in 46 out of 47 tasks (and maybe there’s a reason why the 47th one didn’t work…it involved more subcortical activity). Tasks included responses to mental math, sentence and story processing, but also higher order problem solving, social perception, and working memory.
The data have a lot of ramifications in terms of understanding how connectivity (and the research methods that document differences) has real-world associations with cognitive performance. Also, it may provide future groundwork for the holy grail of education: personalized learning.
What about dyslexia? Neuroscience is making progress there too. Another research group from Europe use a machine-learning algorithm on brain scans to predict whether college students were dyslexic or not. A trained anatomical classifier identified 80% of students with dyslexia correctly and 78% of non-dyslexic correctly. Not bad – especially considering that here in the US, some 3 out of 4 students are being missed by public schools.
For Premium Subscribers, we’ll do a deeper dive into the dyslexia paper! Not a subscriber? Join Premium and you’ll help more dyslexic kids and get smarter every day! : D