How close are we to reading minds? A new study decodes language and meaning from brain scans

• Neuroscientists at the University of Texas have for the first time decoded data from non-invasive brain scans and used them to reconstruct language and meaning from stories that people hear, see or even imagine.
• Technology that can create language from brain signals could be enormously useful for people who cannot speak due to conditions such as motor neurone disease.

Language decoded

• Language decoding models, also called “speech decoders”, aim to use recordings of a person’s brain activity to discover the words they hear, imagine or say.
• Other decoders which used non-invasive brain activity recordings have been able to decode single words or short phrases, but not continuous language.
• By focusing on patterns of activity in brain regions and networks that process language, the researchers found their decoder could be trained to reconstruct continuous language (including some specific words and the general meaning of sentences).

How does it work?

• After training, the encoder could quite accurately predict how each participant’s brain signals would respond to hearing a given string of words.
• However, going in the opposite direction – from recorded brain responses to words – is trickier.
• The encoder model is designed to link brain responses with “semantic features” or the broad meanings of words and sentences.

Words and meanings

• This decoder works instead at the level of ideas and meanings.
• The blood oxygen level dependent signal rises and falls over approximately a 10-second period, during which time a person might have heard 20 or more words.
• As a result, this technique cannot detect individual words, but only the potential meanings of sequences of words.

No need for privacy panic (yet)

• These experiments showed we don’t need to worry just yet about having our thoughts decoded while we walk down the street, or indeed without our extensive cooperation.
• A decoder trained on one person’s thoughts performed poorly when predicting the semantic detail from another participant’s data.
• Considering these requirements, and the need for high-powered computational resources, it is highly unlikely that someone’s thoughts could be decoded against their will at this stage.