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Unlocking the Power of Hits: How They Affect Your Brain and Shape the Future of Music
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Unlocking the Power of Hits: How They Affect Your Brain and Shape the Future of Music

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The algorithmically generated playlists on music streaming services like Spotify give you a better idea of ​​what kind of music you’ll enjoy based on what you listen to and what you enjoy.

And new research shows how these playlists can be even better depending on how the latest songs affect your brain.

And when American researchers trained machine learning algorithms to analyze the physical reactions of music listeners, they were able to predict which tracks people would like the most with up to 97% accuracy.

All the algorithms had to do was measure a person’s emotions and attention to tone, using their neurophysiological state as a proxy.

The longer a person “indulges” in a neurotic song, the more likely it is that the song will become a national hit.

In fact, it was a much better indicator of a tune’s success than whether or not the person said they liked the song.

In other words, just because you consciously enjoy a piece of music doesn’t mean that others will enjoy it too. However, you can get to know your unconscious state better.

“Instead of asking users if they like a new song, wearable neurotech like the one used in this study can automatically assess the neurological value of content,” the researchers write.

And even if the algorithm received physiological data in just one minute of listening to a song, it could still predict a hit with an accuracy of up to 82%.

The new method outperforms similar studies that have used brain scans to assess musical responses. They were only able to predict a rate of about 50%.

And in the new experiments, individual listeners could only guess whether a song was successful if they had heard it before—a bias that, if removed, makes us much worse at predicting success or failure.

“By applying machine learning to neurophysiological data, we can roughly determine which songs are successful,” says neuroeconomist Paul Zack of Claremont University in California. “The neural activity of 33 people can predict whether millions of other people listened to these songs.” “It’s just amazing. Nothing close to this accuracy has ever been demonstrated before.”

Study participants first sat in a room with heart rate monitors and listened to the last 24 songs played through loudspeakers. Thirteen songs were considered successful on streaming platforms, but the members were not told which ones.

At the end of the experiment, the group was asked to rate the songs they liked the most.

The data collected from their heart sensors was then uploaded to a commercial neuroscience platform that uses heart rate data to infer the state of a person’s brain. Oxytocin and dopamine, for example, are two neurohormones known to affect the heart. They are also released when you are satisfied.

And when you sing or listen to music, for example, evidence suggests that your brainstem often releases oxytocin, and dopamine is released and binds to your prefrontal cortex when you give something extra attention or when you “indulge” in it. .

Previous research has attempted to do this by focusing on one area of ​​the brain involved in the reward system. But the researchers didn’t have much success.

The current research focuses on various neurophysiological cues that are importantly associated with emotional responses. He also used a bunch of machine learning algorithms rather than just one.

“Measuring emotional responses using neurophysiological methods provides artists, record producers and broadcasters with a new way to delight listeners with new music,” the researchers write. “Our contribution is to show that comprehensive neurobiological measurements of the peripheral nervous system accurately classify states of injury and destruction. “.

In theory, this information can be used to create separate playlists for a specific mood, but it doesn’t just apply to music.

Neuroprediction can also be applied to any form of entertainment, giving people what they want before they even know it. The current study is small and some details are yet to be worked out, but as a proof of concept it seems encouraging.

The study was published in the journal Frontiers in Artificial Intelligence.

Source: Science Alert

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