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Brain and cognitive sciences

The brain’s language network has to put in more effort when dealing with complicated and unfamiliar sentences.

MIT neuroscientists have used an artificial language network to determine the type of sentences that most stimulate the brain's key language processing regions. Their study reveals that the brain reacts more to complex sentences with unusual grammar or unexpected meaning. Straightforward sentences or nonsensical sequences show little engagement. The researchers focused on language processing regions…

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Intricate and unfamiliar sentence structures place increased strain on the brain’s language processing network.

A team of neuroscientists from the Massachusetts Institute of Technology (MIT) have used an artificial language network to identify the type of sentences that activate the brain's critical language processing centres. The team learned that more complex sentences, which feature unusual grammar or unexpected meanings, generate strong responses from these centres, while straightforward sentences barely…

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Intricate and unknown phrases put more strain on the brain’s language processing system.

Using an artificial language network, neuroscientists from MIT have identified the type of sentences that most effectively activate the human brain's language processing centres. Their findings, published in Nature Human Behavior, show that the most stimulating sentences are those which are complex due to uncommon words or grammar, or unexpected meanings. Simplistic sentences or nonsensical…

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Charting the neural routes associated with visual recall in the brain.

For almost ten years, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have conducted studies to understand why some images are more memorable than others. The team used magnetoencephalography (MEG), which records timing of brain activity, and functional magnetic resonance imaging (fMRI), which identifies active brain regions, to discern when and where in…

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Complicated and unfamiliar phrases put more strain on the brain’s language processing system.

A recent study from MIT has uncovered that the human brain's principal language processing centers are most activated while reading complex, unusual sentences. The artificial language network assisted study revealed that the more intricate a sentence was, either through unconventional grammar or unexpected meaning, the more these language processing centers were activated. In contrast, simple…

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The language network in the brain is taxed more heavily by complicated sentences that aren’t easily recognized.

MIT researchers have used an artificial language network to decipher which types of sentences activate the brain's language processing centers most effectively. Their study reveals that sentences of higher complexity with unusual grammar or unexpected meanings engage these centers to a greater degree than straightforward sentences or nonsensical series of words. Their findings are based…

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There is potential in deep neural networks as they could effectively serve as models for human auditory perception.

A new MIT study has found that computational models derived from machine learning that mimic the structure and function of the human auditory system could help improve the design of hearing aids, cochlear implants, and brain-machine interfaces. Its research explored deep neural networks trained to perform auditory tasks and showed that these models generate internal…

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The potential of deep neural networks as models for human auditory perception is quite promising.

A recent study from MIT has shown that computational models that mimic the structure and function of the human auditory system could significantly aid research into more sophisticated hearing aids, cochlear implants, and brain-machine interfaces. Modern computational models that use machine learning have already made progress in this area. The MIT team carried out the…

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Deep neural networks demonstrate potential in simulating human auditory perception.

A new study from MIT reveals that modern computational models based on machine learning, which mimic the structure and function of the human auditory system, are coming closer to potentially aiding the design of improved hearing aids, cochlear implants, and brain-machine interfaces. The MIT team’s research is the most extensive to date on deep neural networks,…

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Deep neural networks exhibit potential for modelling human auditory processes.

A new study from MIT suggests that modern computational models powered by machine learning could potentially aid the design of better hearing aids, cochlear implants, and brain-machine interfaces. These models, specifically deep neural networks, are starting to encompass functions that replicate the structure of the human auditory system.  The study further illuminates how to best train…

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