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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
Encoding01:19

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...

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Related Experiment Video

Updated: Jul 15, 2026

Post-Movie Subliminal Measurement (PMSM), for Investigating Implicit Social Bias
09:03

Post-Movie Subliminal Measurement (PMSM), for Investigating Implicit Social Bias

Published on: February 29, 2020

Mapping the movie-watching brain with AI-derived semantics.

Muwei Li1,2

  • 1Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States.

Imaging Neuroscience (Cambridge, Mass.)
|July 14, 2026
PubMed
Summary

This study used a multimodal large language model (Gemini) to link movie content to brain activity, revealing how artificial intelligence can help understand neural processing and individual differences.

Keywords:
LLMsfMRInaturalistic stimulisemantic encoding

Related Experiment Videos

Last Updated: Jul 15, 2026

Post-Movie Subliminal Measurement (PMSM), for Investigating Implicit Social Bias
09:03

Post-Movie Subliminal Measurement (PMSM), for Investigating Implicit Social Bias

Published on: February 29, 2020

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Naturalistic stimuli, like movies, are valuable for studying brain function.
  • Linking complex movie content to brain activity remains challenging.
  • Interpreting distributed brain responses requires robust analytical frameworks.

Purpose of the Study:

  • To use a multimodal large language model (Gemini) as a semantic annotator for naturalistic stimuli.
  • To bridge the gap between movie content, brain responses, and cognitive performance.
  • To explore the utility of AI-derived semantic features in understanding neural processing.

Main Methods:

  • Segmented movie data into clips and prompted Gemini for semantic ratings on 11 dimensions.
  • Extracted clip-wise BOLD activation patterns from fMRI data in 360 cortical regions.
  • Fitted linear regression models to predict brain responses using AI-derived semantic features.

Main Results:

  • Gemini features predicted brain activity in association cortices (temporal, medial parietal, lateral frontal) but not unimodal regions.
  • Feature-weight maps aligned with known functional specializations.
  • Individual differences in resting-state connectivity and AI predictability showed an asymmetric relationship with brain systems.

Conclusions:

  • Interpretable AI features offer a scalable framework for quantifying semantic predictability in naturalistic settings.
  • AI models can serve as semantic references to probe human neural processing.
  • AI-derived semantic predictability is linked to cognitive abilities in specific brain regions.