idw – Informationsdienst Wissenschaft

Nachrichten, Termine, Experten

Grafik: idw-Logo
Grafik: idw-Logo

idw - Informationsdienst
Wissenschaft

Science Video Project
idw-Abo

idw-News App:

AppStore

Google Play Store



Instance:
Share on: 
08/11/2025 12:36

Study: Language Models from AI Can Predict How the Human Brain Responds to Visual Stimuli

Christine Xuan Müller Stabsstelle Kommunikation und Marketing
Freie Universität Berlin

    Guest professor at the Cognitive Computational Neuroscience Lab, Freie Universität Berlin, uses language models similar to those behind ChatGPT

    Large language models (LLMs) from the field of artificial intelligence can predict how the human brain responds to visual stimuli. This is shown in a new study published in Nature Machine Intelligence by Professor Adrien Doerig (Freie Universität Berlin) together with colleagues from Osnabrück University, University of Minnesota, and Université de Montréal, titled “High-Level Visual Representations in the Human Brain Are Aligned with Large Language Models.” For the study, the team of scientists used LLMs similar to those behind ChatGPT.

    When we look at the world, our brains do not just recognize objects like “a tree” or “a car” – they also grasp meaning, relationships, and context. Until recently, scientists lacked tools to capture and quantitatively investigate this high-level visual understanding. In this new study, a team led by cognitive neuroscientist Adrien Doerig, guest professor at the Cognitive Computational Neuroscience Lab, Freie Universität Berlin, used LLMs to extract “semantic fingerprints” from scene descriptions.

    The researchers used these “semantic fingerprints” to model functional MRI data recorded while participants viewed everyday images, depicting scenes such as “children playing Frisbee in the schoolyard” or “a dog standing on a sailing boat.” Leveraging LLM representations allowed the team to predict neural activities and to decode textual descriptions of what the people were seeing based only on the neuroimaging measurements.

    To predict the semantic fingerprints directly from the images, they also trained computer vision models. These models – guided by linguistic representations – aligned better with human brain responses than state-of-the-art image classification systems.

    “Our results suggest that human visual representations mirror how modern language models represent meaning – which opens new doors for both neuroscience and AI,” says Doerig.


    Contact for scientific information:

    Prof. Dr. Adrien Doerig, Department of Education and Psychology, Freie Universität Berlin, Email: adrien.doerig@fu-berlin.de


    Original publication:

    https://www.nature.com/articles/s42256-025-01072-0


    Images

    Cognitive neuroscientist Adrien Doerig is guest professor at the Cognitive Computational Neuroscience Lab, Freie Universität Berlin.
    Cognitive neuroscientist Adrien Doerig is guest professor at the Cognitive Computational Neuroscienc ...
    Source: Joëlle Schwitguébel


    Criteria of this press release:
    Journalists, Scientists and scholars, all interested persons
    Biology, Information technology, Psychology
    transregional, national
    Research results, Scientific Publications
    English


     

    Help

    Search / advanced search of the idw archives
    Combination of search terms

    You can combine search terms with and, or and/or not, e.g. Philo not logy.

    Brackets

    You can use brackets to separate combinations from each other, e.g. (Philo not logy) or (Psycho and logy).

    Phrases

    Coherent groups of words will be located as complete phrases if you put them into quotation marks, e.g. “Federal Republic of Germany”.

    Selection criteria

    You can also use the advanced search without entering search terms. It will then follow the criteria you have selected (e.g. country or subject area).

    If you have not selected any criteria in a given category, the entire category will be searched (e.g. all subject areas or all countries).