Brain mapping with neural networks and language symbols.

Decoding the Brain: How Neuroscience is Unraveling the Mysteries of Meaning

Kai Mendoza in Mind & Education June 2025 • 3 min read.

"New studies are revealing how our brains represent and process language in surprisingly structured ways, offering potential insights into learning, communication, and even treating cognitive disorders."

For centuries, philosophers and linguists have grappled with the elusive concept of 'meaning.' Now, neuroscientists are joining the quest, armed with advanced tools to explore how our brains learn, represent, and utilize meanings to facilitate cognition and communication. The challenge? To bridge the gap between abstract ideas and the complex neural activity that brings them to life.

A groundbreaking study by Alexander Huth, Jack Gallant, and their team has integrated functional magnetic resonance imaging (fMRI), compelling audiobook narratives, and sophisticated machine-learning algorithms derived from computational linguistics. This innovative approach aims to decipher how meanings are mapped onto specific locations within the brain.

By comparing various mapping methods, researchers can now assess what types of meaning representations the brain utilizes in realistic, everyday scenarios. This leap forward promises a deeper understanding of how our brains process and interpret the world around us.

Mapping Meaning: From Words to Brain Activity

Brain mapping with neural networks and language symbols.

Historically, studying semantics (the study of meaning) has been challenging due to the lack of an animal model for human language. However, recent advancements, particularly those employing mathematically rigorous frameworks from linguistics, have started to change this landscape.

Linguists often differentiate between two key aspects of meaning:

Formal Semantics: This focuses on how individual words combine to create novel messages.
Lexical Semantics: This deals with the specific, often arbitrary, meanings associated with individual words.

Computational work in lexical semantics relies heavily on the concept of 'word embeddings,' where a word's meaning is encoded as a vector within an abstract semantic space. This space is constructed from a set of primitive words or semantic features. By counting how often a word co-occurs with each feature in a large body of text, researchers can define the word's location in this semantic space.

The Future of Meaning: Implications and Applications

The study by Huth et al. underscores two significant trends in cognitive neuroscience: the drive for greater ecological validity and the use of computational models to test theories about the cognitive algorithms our brains employ. By bridging the gap between abstract models and real-world scenarios, neuroscience is poised to unlock even more secrets of the human mind.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.tins.2018.08.003, Alternate LINK

Title: Mapping Meanings

Subject: General Neuroscience

Journal: Trends in Neurosciences

Publisher: Elsevier BV

Authors: Jonathan R. Brennan

Published: 2018-11-01

Everything You Need To Know

What methods are neuroscientists using to study how the brain encodes meaning?

Neuroscientists are employing fMRI and computational linguistics to map how the brain encodes meaning. Alexander Huth, Jack Gallant, and their team integrated functional magnetic resonance imaging (fMRI), audiobook narratives, and sophisticated machine-learning algorithms derived from computational linguistics. This approach helps decipher how meanings are mapped onto specific brain locations. By comparing mapping methods, researchers assess how the brain utilizes meaning representations in realistic scenarios, offering deeper insights into brain processing and interpretation.

What are the key distinctions between Formal Semantics and Lexical Semantics in linguistics?

Linguists differentiate between Formal Semantics and Lexical Semantics. Formal Semantics focuses on how individual words combine to create novel messages, emphasizing the structural aspects of meaning construction. Lexical Semantics deals with the specific meanings associated with individual words. This includes understanding that the meaning of a word is often arbitrary, highlighting the relationship between individual words and their meanings, crucial for understanding the nuances of language.

How do 'word embeddings' contribute to the study of Lexical Semantics?

Computational work in lexical semantics relies heavily on 'word embeddings.' These encode a word's meaning as a vector within an abstract semantic space. This space is constructed from primitive words or semantic features. By counting how often a word co-occurs with each feature in a large body of text, researchers define the word's location in this semantic space, offering a quantifiable method for understanding lexical meaning.

What are the implications of the study by Huth et al. for cognitive neuroscience?

The study by Huth et al. underscores two significant trends. Firstly, it emphasizes the drive for greater ecological validity, aiming to study brain processes in realistic, everyday scenarios. Secondly, it highlights the use of computational models to test theories about the cognitive algorithms our brains employ. By bridging abstract models and real-world scenarios, neuroscience is poised to unlock more secrets of the human mind, particularly in areas like cognitive therapies and AI.

Why has studying semantics historically been challenging, and how are recent advancements changing this?

Historically, studying semantics has been challenging due to the lack of an animal model for human language. The human brain's unique language capabilities make direct comparative studies difficult. However, recent advancements, particularly those employing mathematically rigorous frameworks from linguistics, have started to change this landscape. Tools like fMRI, sophisticated machine-learning algorithms, and concepts like 'word embeddings' provide new ways to explore how the brain processes and represents meaning.