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New Algorithm That may Predict Your Intelligence

The personality test they used divides personality into five scales -- openness to experience, conscientiousness, extraversion, agreeableness, neuroticism

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New algorithms to spot online trolls: Study
New algorithms to spot online trolls: Study. (IANS)

Be careful if you lie about your IQ as researchers have developed a machine-learning algorithm that may tell how smart you are by looking at a scan of your brain.

The study showed that the new computing tool can predict a person’s intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity.

The fMRI develops a map of brain activity by detecting changes in blood flow to specific brain regions.

“We found if we just have people lie in the scanner and do nothing while we measure the pattern of activity in their brain, we can use the data to predict their intelligence,” said co-author Ralph Adolphs from the California Institute of Technology (Caltech).

To train their algorithm on the complex patterns of activity in the human brain, the researchers downloaded the brain scans and intelligence scores from almost 900 individuals.

The researchers found that after processing the data, the algorithm was able to predict intelligence at statistically significant levels across the subjects.

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Representational image.

“The information that we derive from the brain measurements can be used to account for about 20 per cent of the variance in intelligence we observed in our subjects,” said Julien Dubois from the Cedars-Sinai Medical Centre.

In predicting intelligence from brain scans, the algorithm is doing something that humans cannot, because even an experienced neuroscientist cannot look at a brain scan and tell how intelligent a person is, the researchers said.

The researchers also conducted a parallel study, using the same test population and approach, that attempted to predict personality traits from fMRI brain scans.

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The personality test they used divides personality into five scales — openness to experience, conscientiousness, extraversion, agreeableness, neuroticism.

However, it has turned out to be much more difficult to predict personality using the method the team used for predicting intelligence.

The two studies are yet to be published in the separate journals Personality Neuroscience and Philosophical Transactions of the Royal Society. (IANS)

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Here’s how MRI may Predict Intelligence Level in Children

MRI may predict intelligence level in children

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Researchers have found that MRI scanning can help predict the intelligence level in children. Pixabay

Researchers have used ensemble methods based on deep learning 3D analysis networks to answer the global Magnetic Resonance Imaging (MRI) prevision challenge for children.

Importantly, they made predictions for both the fluid intelligence level and the target variable independent from age, gender, brain size or MRI scanner used.

MRI is a common technique used to obtain images of human internal organs and tissues. Scientists wondered whether the intelligence level can be predicted from an MRI brain image.

“Our team develops deep learning methods for computer vision tasks in MRI data analysis, amongst other things,” said study researcher Ekaterina Kondratyev from Skolkovo Institute of Science and Technology (Skoltech) in Russia.

“In this study, we applied ensembles of classifiers to 3D of super precision neural networks: with this approach, one can classify an image as it is, without first reducing its dimension and, therefore, without losing valuable information,” said Kondratyeva.

The US National Institutes of Health (NIH) database contains a total of over 11,000 structural and functional MRI images of children aged 9-10.

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MRI is a common technique used to obtain images of human internal organs and tissues. Pixabay

In 2013, NIH launched the first grand-scale study of its kind in adolescent brain research, Adolescent Brain Cognitive Development, to see if and how teenagers’ hobbies and habits affect their further brain development.

NIH scientists launched an international competition, making the enormous NIH database available to a broad community for the first time ever.

The participants were given a task of building a predictive model based on brain images.

As part of the competition, the Skoltech team applied neural networks for MRI image processing.

To do this, they built a network architecture enabling several mathematical models to be applied to the same data in order to increase the prediction accuracy, and used a novel ensemble method to analyse the MRI data.

In their recent study, Skoltech researchers focused on predicting the intelligence level, or the so called “fluid intelligence”, which characterises the biological abilities of the nervous system and has little to do with acquired knowledge or skills.

Importantly, they made predictions for both the fluid intelligence level and the target variable independent from age, gender, brain size or MRI scanner used.

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The results of the study helped find the correlation between the child’s “fluid intelligence” and brain anatomy.

Although the prediction accuracy is less than perfect, the models produced during this competition will help shed light on various aspects of cognitive, social, emotional and physical development of adolescents. (IANS)