Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Nonconscious Mimicry

Nonconscious Mimicry

Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.

Neural Control of Respiration

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...

Higher Mental Functions of the Brain: Language

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...

High-Level and Low-Level Awareness

High-Level and Low-Level Awareness

Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Rapid, clean and quantitative analysis of Cu<sup>2+</sup> in copper refining electrolyte <i>via</i> chemometrics-driven Vis-SWNIR absorption spectroscopy.

Analytical methods : advancing methods and applications·2025

Same author

Electro-fermentation driven detoxification of furfural to methane.

Bioresource technology·2025

Same author

Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025

Same author

N, O-Doped surface modulation of ZnIn<sub>2</sub>S<sub>4</sub> with high hydrophilicity for enhanced photocatalytic hydrogen evolution.

Journal of colloid and interface science·2024

Same author

Enhanced Photocatalytic Hydrogen Production Activity Driven by TiO<sub>2</sub>/(MoP/CdS): Insights from Powder Particles to Thin Films.

Langmuir : the ACS journal of surfaces and colloids·2024

Same author

Carbon-coated nickel phosphide enhances efficiently electron transfer of cadmium sulfide for photocatalytic hydrogen production.

Journal of colloid and interface science·2024

Search research articles

Related Experiment Video

Updated: Jul 9, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Brain-Inspired Large Model Mindreading.

Jia Jin¹, Yunsong Hu¹, Zhongfeng Wang¹

¹Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 200083, China.

|July 7, 2026

Summary

This summary is machine-generated.

Human brain activity differs when Multimodal Large Language Models (MLLMs) err on visual Theory of Mind (ToM) tasks. Understanding these neural patterns can improve AI mind-reading capabilities.

Keywords:

Theory of Mind brain-inspired intelligence mentalization multimodal large language models visual question answering

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Related Experiment Videos

Last Updated: Jul 9, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Area of Science:

Cognitive Neuroscience
Artificial Intelligence
Neuroimaging

Background:

Multimodal Large Language Models (MLLMs) exhibit deficits in visual Theory of Mind (ToM).
Human mind-reading research can inform MLLM development.
Visual ToM tasks present a unique challenge for AI.

Purpose of the Study:

To compare neural processing between human-correct/MLLM-incorrect and mutually correct visual ToM conditions.
To identify neural signatures associated with MLLM errors in visual ToM.
To inform the optimization of MLLMs for enhanced visual ToM.

Main Methods:

fMRI data from 83 participants performing visual ToM tasks.
Comparison of neural activity in MLLM-incorrect/human-correct (MLLMI) vs. mutually correct (MLLMC) conditions.
Analysis of questionnaire data and natural language responses.

Main Results:

Human confidence and expectations for MLLM performance were lower in the MLLMI condition.
Human responses were more context-aligned, concise, and certain than MLLM responses.
MLLMI condition showed increased activation in bilateral precuneus and middle temporal gyrus, with enhanced functional connectivity in attention and coordination networks.

Conclusions:

Distinct neural patterns accompany MLLM errors in visual ToM tasks.
A 2-layer Transformer model achieved 78.6% accuracy in decoding MLLMI vs. MLLMC conditions using neural signatures.
The proposed Knowledge-Thinking-Adaptation (KTA) framework offers a roadmap for developing AI with human-like visual ToM.