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‘MIND-CAPTIONING’ AI DECODES BRAIN ACTIVITY TO TURN THOUGHTS INTO
TEXT  
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Max Kozlov
November 5, 2025
Nature [[link removed]]

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_ A non-invasive imaging technique can translate scenes in your head
into sentences. It could help to reveal how the brain interprets the
world. _

Functional magnetic resonance imaging is a non-invasive way to
explore brain activity, National Institute of Mental Health/National
Institutes of Health/SPL

 

Reading a person’s mind using a recording of their brain activity
sounds futuristic, but it’s now one step closer to reality. A
technique called ‘mind captioning’ generates descriptive sentences
of what a person is seeing or picturing in their mind using a read-out
of their brain activity, with impressive accuracy.

The technique, described in a paper published today in _Science
Advances_1
[[link removed]], also
offers clues for how the brain represents the world before thoughts
are put into words. And it might be able to help people with language
difficulties [[link removed]],
such as those caused by strokes, to better communicate.

The model predicts what a person is looking at “with a lot of
detail”, says Alex Huth, a computational neuroscientist at the
University of California, Berkeley. “This is hard to do. It’s
surprising you can get that much detail.”

SCAN AND PREDICT

Researchers have been able to accurately predict what a person is
seeing or hearing using their brain activity
[[link removed]] for more than a
decade. But decoding the brain’s interpretation of complex content,
such as short videos or abstract shapes, has proved more difficult.

Previous attempts have identified only key words that describe what a
person saw rather than the complete context, which might include the
subject of a video and actions that occur in it, says Tomoyasu
Horikawa, a computational neuroscientist at NTT Communication Science
Laboratories in Kanagawa, Japan. Other attempts have used artificial
intelligence (AI) models that can create sentence structure
themselves, making it difficult to know whether the description was
actually represented in the brain, he adds.

Horikawa’s method first used a deep-language AI model to analyse the
text captions of more than 2,000 videos, turning each one into a
unique numerical ‘meaning signature’. A separate AI tool was then
trained on six participants’ brain scans and learnt to find the
brain-activity patterns that matched each meaning signature while the
participants watched the videos.

THE RISE OF BRAIN-READING TECHNOLOGY: WHAT YOU NEED TO KNOW
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Once trained, this brain decoder could read a new brain scan from a
person watching a video and predict the meaning signature. Then, a
different AI text generator would search for a sentence that comes
closest to the meaning signature decoded from the individual’s
brain.

For example, a participant watched a short video of a person jumping
from the top of a waterfall. Using their brain activity, the AI model
guessed strings of words, starting with ‘spring flow’, progressing
to ‘above rapid falling water fall’ on the tenth guess and
arriving at ‘a person jumps over a deep water fall on a mountain
ridge’ on the 100th guess.

The researchers also asked participants to recall video clips that
they had seen. The AI models successfully generated descriptions of
these recollections, demonstrating that the brain seems to use a
similar representation for both viewing and remembering.

READING THE FUTURE

This technique, which uses non-invasive functional magnetic resonance
imaging, could help to improve the process by which implanted
brain–computer interfaces
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people’s non-verbal mental representations directly into text. “If
we can do that using these artificial systems, maybe we can help out
these people with communication difficulties,” says Huth, who
developed a similar model in 2023 with his colleagues that decodes
language from non-invasive brain recordings2
[[link removed]].

These findings raise concerns about mental privacy
[[link removed]], Huth says, as
researchers grow closer to revealing intimate thoughts, emotions and
health conditions that could, in theory, be used for surveillance,
manipulation or to discriminate against people. Neither Huth’s model
nor Horikawa’s cross a line, they both say, because these techniques
require participants’ consent and the models cannot discern private
thoughts. “Nobody has shown you can do that, yet,” says Huth.

_doi: [link removed]

REFERENCES

*
Horikawa, T. _Sci_. _Adv._ 11, eadw1464 (2025).

ARTICLE [[link removed]] GOOGLE SCHOLAR
[[link removed].] 

*
Tang, J., LeBel, A., Jain, S. & Huth, A. G. _Nature Neurosci_. 26,
858–866 (2023).

ARTICLE [[link removed]] PUBMED
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SCHOLAR
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* Science
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* artificial intelligence
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* psychology
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* neuroscience
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* neurobiology
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