The human brain is an extremely advanced information processor composed of over 86 billion neurons. People are adept at recognizing patterns from complex networks, comparable to languages, without any formal instruction. Previously, cognitive scientists tried to clarify this ability by depicting the brain as a highly optimized computer; however, there is now discussion among neuroscientists that this model may not precisely mirror how the brain works.
Now, Penn researchers have developed a unique model for how the brain interprets patterns from complex networks. Revealed in Nature Communications, this new model exhibits that the ability to detect patterns stems partly from the brain’s goal to signify issues in the simplest way possible.
Their model depicts the brain as balancing accuracy with simplicity when making decisions. The work was carried out by physics PhD student Christopher Lynn, neuroscience PhD pupil Ari Kahn, and professor Danielle Bassett.
This new model is constructed upon the idea that individuals make errors while making an attempt to make sense of patterns, and these errors are important to get a glimpse of the bigger picture.
To check their hypothesis, the researchers conducted a set of experiments similar to an earlier study by Kahn. That study discovered that when participants have been shown repeating elements in a sequence, such as A-B-C-B, etc., they have been sensitive to certain patterns without being conscious that the patterns existed.