The human brain may work more like AI than anyone expected

Science News from research organizations The human brain may work more like AI than anyone expected Date: January 21, 2026 Source: The Hebrew University of Jerusalem Summary: Scientists have discovered that the human brain understands spoken language in a way that closely resembles how advanced AI...

Science News from research organizations The human brain may work more like AI than anyone expected Date: January 21, 2026 Source: The Hebrew University of Jerusalem Summary: Scientists have discovered that the human brain understands spoken language in a way that closely resembles how advanced AI language models work. By tracking brain activity as people listened to a long podcast, researchers found that meaning unfolds step by step—much like the layered processing inside systems such as GPT-style models. Share: Facebook Twitter Pinterest LinkedIN Email FULL STORY The human brain appears to build meaning from speech in the same layered, step-by-step way as advanced AI language models. This discovery reshapes long-held ideas about language, suggesting understanding grows gradually through context rather than fixed rules. Credit: Shutterstock A new study suggests that the human brain understands spoken language through a stepwise process that closely resembles how advanced AI language models operate. By recording brain activity from people listening to a spoken story, researchers found that later stages of brain responses match deeper layers of AI systems, especially in well known language regions like Broca's area. The results call into question long standing rule-based ideas of language comprehension and are supported by a newly released public dataset that offers a powerful new way to study how meaning is formed in the brain. The research, published in Nature Communications, was led by Dr. Ariel Goldstein of the Hebrew University with collaborators Dr. Mariano Schain of Google Research and Prof Uri Hasson and Eric Ham from Princeton University. Together, the team uncovered an unexpected similarity between how humans make sense of speech and how modern AI models process text. Using electrocorticography recordings from participants who listened to a thirty-minute podcast, the scientists tracked the timing and location of brain activity as language was processed. They found that the brain follows a structured sequence that closely matches the layered design of large language models such as GPT-2 and Llama 2. How the Brain Builds Meaning Over Time As we listen to someone speak, the brain does not grasp meaning all at once. Instead, each word passes through a series of neural steps. Goldstein and his colleagues showed that these steps unfold over time in a way that mirrors how AI models handle language. Early layers in AI focus on basic word features, while deeper layers combine context, tone, and broader meaning. Human brain activity followed the same pattern. Early neural signals matched the early stages of AI processing, while later brain responses lined up with the deeper layers of the models. This timing match was especially strong in higher level language areas such as Broca's area, where responses peaked later when linked to deeper AI layers. According to Dr. Goldstein, "What surprised us most was how closely the brain's temporal unfolding of meaning matches the sequence of transformations inside large language models. Even though these systems are built very differently, both seem to converge on a similar step-by-step buildup toward understanding" Why These Findings Matter The study suggests that artificial intelligence can do more than generate text. It may also help scientists better understand how the human brain creates meaning. For many years, language was thought to rely mainly on fixed symbols and rigid hierarchies. These results challenge that view and instead point to a more flexible and statistical process in which meaning gradually emerges through context. The researchers also tested traditional linguistic elements such as phonemes and morphemes. These classic features did not explain real time brain activity as well as the contextual representations produced by AI models. This supports the idea that the brain relies more on flowing context than on strict linguistic building blocks. A New Resource for Language Neuroscience To help move the field forward, the team has made the complete set of neural recordings and language features publicly available. This open dataset allows researchers around the world to compare theories of language understanding and to develop computational models that more closely reflect how the human mind works. RELATED TOPICS Mind & Brain Intelligence Language Acquisition Perception Gender Difference Computers & Math Neural Interfaces Computers and Internet Statistics Communications RELATED TERMS Workaholic Android Mathematical model Robot Brain Cognition Limbic system Central nervous system Story Source: Materials provided by The Hebrew University of Jerusalem. Note: Content may be edited for style and length. Journal Reference: Ariel Goldstein, Eric Ham, Mariano Schain, Samuel A. Nastase, Bobbi Aubrey, Zaid Zada, Avigail Grinstein-Dabush, Harshvardhan Gazula, Amir Feder, Werner Doyle, Sasha Devore, Patricia Dugan, Daniel Friedman, Michael Brenner, Avinatan Hassidim, Yossi Matias, Orrin Devinsky, Noam Siegelman, Adeen Flinker, Omer Levy, Roi Reichart, Uri Hasson. Temporal structure of natural language processing in the human brain corresponds to layered hierarchy of large language models. Nature Communications, 2025; 16 (1) DOI: 10.1038/s41467-025-65518-0 Cite This Page: MLA APA Chicago The Hebrew University of Jerusalem. "The human brain may work more like AI than anyone expected." ScienceDaily. ScienceDaily, 21 January 2026. . The Hebrew University of Jerusalem. (2026, January 21). The human brain may work more like AI than anyone expected. ScienceDaily. Retrieved July 13, 2026 from www.sciencedaily.com/releases/2026/01/260120000308.htm The Hebrew University of Jerusalem. "The human brain may work more like AI than anyone expected." ScienceDaily. www.sciencedaily.com/releases/2026/01/260120000308.htm (accessed July 13, 2026). Explore More from ScienceDaily RELATED STORIES Researchers Tested AI Against 100,000 Humans on Creativity Jan. 25, 2026 — A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. ... Why GPT Can't Think Like Us Feb. 21, 2025 — Artificial Intelligence (AI), particularly large language models like GPT-4, has shown impressive performance on reasoning tasks. But does AI truly understand abstract concepts, or is it just ... 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Source: ScienceDaily AI — Published January 21, 2026 at 06:49 UTC — Category: Research