The Human Brain May Work More Like AI Than Anyone Expected

For decades, scientists believed human language understanding relied mainly on rigid grammatical rules and symbolic structures. However, a new breakthrough study suggests something far more dynamic: the human brain may process spoken language in a way strikingly similar to modern artificial intelligence systems.

According to researchers from the Hebrew University of Jerusalem, Google Research, and Princeton University, the brain builds meaning gradually—layer by layer—much like advanced AI language models such as GPT-2 and Llama 2. Their findings, published in Nature Communications, reveal that understanding does not happen instantly but evolves through contextual accumulation.

How Scientists Compared the Brain With AI

To uncover this connection, scientists used electrocorticography (ECoG) to record neural activity while participants listened to a 30-minute podcast. This allowed researchers to observe how language signals traveled through the brain in real time.

They then compared these neural patterns with internal representations generated by large language models.

The result was remarkable.

Early brain signals closely matched the initial processing layers of AI models, which focus on basic word properties. Later neural responses aligned with deeper AI layers responsible for semantic understanding and contextual integration. The strongest correlation appeared in Broca’s area—a critical region involved in human language comprehension.

This layered similarity echoes many ideas already discussed in our analysis of when AI goes off script and produces unexpected outcomes, where we explored how deep contextual processing can lead to emergent behavior in machine learning systems:

Dr. Ariel Goldstein, who led the study, noted that what surprised the team most was how closely the brain’s timing of meaning construction mirrors the internal transformation stages of AI systems.

Meaning Emerges Through Context, Not Fixed Rules

Traditional linguistics emphasizes phonemes, morphemes, and syntax. But this research shows that such rigid components explain real-time brain activity far less effectively than contextual representations generated by AI.

In practical terms, both humans and machines appear to rely on flowing context rather than static rules.

This insight also helps explain broader cycles in artificial intelligence development, including periods of optimism and disappointment. We previously examined this phenomenon in detail while covering the rise and decline patterns known as AI winter, highlighting how expectations often outpace underlying cognitive reality.

Why This Discovery Matters for Artificial Intelligence

This is not merely a neuroscience breakthrough—it directly impacts how future AI systems may be designed.

The study suggests that:

  • Human language understanding is probabilistic rather than rule-based

  • Contextual accumulation drives meaning in both brains and machines

  • Brain-inspired architectures could significantly improve AI reliability

  • Large language models can now serve as scientific tools for cognitive research

At the same time, this convergence raises important societal questions. As AI systems grow more human-like in reasoning, their influence expands beyond technology into economics, creativity, and labor—issues we addressed in our recent piece on the hidden social cost of AI in 2025, which explores how rapid automation reshapes work and decision-making.

A New Public Dataset for Global Research

To accelerate progress, the researchers released their complete neural recordings and language feature datasets to the public. This allows scientists worldwide to compare competing theories of language understanding and build models that more closely resemble human cognition.

The original research was conducted by the Hebrew University of Jerusalem in collaboration with Google Research and Princeton University. You can explore more about their neuroscience initiatives directly through the Hebrew University of Jerusalem’s official research portal.

Final Thoughts

This study reshapes how we think about intelligence.

Rather than operating on fundamentally different principles, humans and artificial systems appear to share a layered, context-driven pathway toward understanding. Meaning is not retrieved—it is constructed over time.

As large language models continue to evolve, they may do more than generate content. They may help us uncover how the human mind itself works.