Brain complexity: theory of pattern recognition (Introduction)

by David Turell , Sunday, May 10, 2020, 21:50 (1447 days ago) @ David Turell

Our brain helps us see patterns. A newer theory:

https://medicalxpress.com/news/2020-05-brain-complex.html

"The human brain is a highly advanced information processor composed of more than 86 billion neurons. Humans are adept at recognizing patterns from complex networks, such as languages, without any formal instruction. Previously, cognitive scientists tried to explain this ability by depicting the brain as a highly optimized computer, but there is now discussion among neuroscientists that this model might not accurately reflect how the brain works.

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"this new model shows that the ability to detect patterns stems in part from the brain's goal to represent things in the simplest way possible. Their model depicts the brain as constantly balancing accuracy with simplicity when making decisions.

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"Using tools from information theory and reinforcement learning, the researchers were able to use this data to implement a metric of complexity called entropy. "Being very random is the least complex thing you could do, whereas if you were learning the sequence very precisely, that's the most complex thing you can do. The balance between errors and complexity, or negative entropy, gives rise to the predictions that the model gives," says Lynn.

"Their resulting model of how the brain processes information depicts the brain as balancing two opposing pressures: complexity versus accuracy. "You can be very complex and learn well, but then you are working really hard to learn patterns," says Lynn. "Or, you have a lower complexity process, which is easier, but you are not going to learn the patterns as well."

"With their new model, the researchers were also able to quantify this balance using a parameter beta. If beta is zero, the brain makes a lot of errors but minimizes complexity. If beta is high, then the brain is taking precautions to avoid making errors. "All beta does is tune between which is dominating," says Lynn. In this study, 20% of the participants had a small beta, 10% had high beta values, and the remaining 70% were somewhere in between. "You do see this wide spread of beta values across people," he says.

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"And what about the role of making mistakes? Their model provides support for the idea that the human brain isn't an optimal learning machine but rather that making mistakes, and learning from them, plays a huge role in behavior and cognition. It seems that being able to look at complex systems more broadly, like stepping away from a pointillist painting, gives the brain a better idea of overall relationships.

"'Understanding structure, or how these elements relate to one another, can emerge from an imperfect encoding of the information. If someone were perfectly able to encode all of the incoming information, they wouldn't necessarily understand the same kind of grouping of experiences that they do if there's a little bit of fuzziness to it," says Kahn.

"'The coolest thing is that errors in how people are learning and perceiving the world are influencing our ability to learn structures. So we are very much divorced from how a computer would act," says Lynn.

"The researchers are now interested in what makes the modular network easier for the brain to interpret and are also conducting functional MRI studies to understand where in the brain these network associations are being formed. They are also curious as to whether people's balance of complexity and accuracy is fluid, whether people can change on their own or if they are "set,"and also hope to do experiments using language inputs sometime in the future."

Comment: We certainly don't act like computers in our form of pattern recognition. Without question, our brain is built to help us understand what we are seeing. I find this explanation kind of muddy.