Dali helps scientists crack our brain code

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Dali helps scientists crack our brain code

#1  Postby DougC » Apr 22, 2016 3:29 am


B.B.C. Article

Scientists at Glasgow University have established a world first by cracking the communication code of our brains.
Pioneering research in the field of cognitive neuroimaging has revealed how brains process what we see.
The work has been led by Prof Philippe Schyns, the head of Glasgow's school of psychology, with more than a little help from Voltaire and Salvador Dali.

By asking test subjects which image they saw, researchers were able to map how the brains processed the information

How Dali's mind worked is a matter of continuing conjecture. But one of his works has helped unlock how our minds work. Or more precisely, how our brains see.
Prof Schyns explains: "Our main interest was to study how the brain works as an information processing machine.
"Typically we observe brain signals but it is quite difficult to know what they do.
"Do they code information from the visual world - do they not? If so, how?
"Do they send information from one region of the brain to another region of the brain? If so, how?"

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Re: Dali helps scientists crack our brain code

#2  Postby Calilasseia » Apr 22, 2016 6:44 am

The full paper is available as a free download (for once) from the publisher.

Tracing The Flow Of Perceptual Features In An Algorithmic Brain Network by Robin A. A. Ince, Nicola J. van Rijsbergen, Gregor Thut, Guillaume A. Rousselet, Joachim Gross, Stefano Panzeri & Philippe G. Schyns, Scientific Reports, DOI: 10.1038/srep17681 (December 2015) [Full paper downloadable from here.

Ince et al, 2015 wrote:Abstract

The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level–i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.
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