Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry

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Closest papers

1. Very close Cited by this paper Not relevant

   Shared neural geometries for bilingual semantic representations in human hippocampal neurons

   Xin-Yuan Yan, Ana G. Chavez, Melissa C. Franch et al. · 2025 · bioRxiv · Not summarized yet
2. Very close Cited by this paper Not relevant

   Semantic similarity across languages reflects neurocognitive dimensions shaped by climate

   Ze Fu, Yuxi Chu, Tangxiaoxue Zhang et al. · 2026 · Nature Communications · Not summarized yet

   Abstract

   Human languages differ widely, yet they share systematic regularities in the underlying semantic representations being expressed. How such similarities and differences arise remains unclear, in part because semantic theories often lack a principled link to neurocognitive constraints. Drawing on neurocognitive accounts in which semantic knowledge is grounded in biologically salient information dimensions, we examine how environmental factors shape conceptual representations in language. Here we show that word meanings across languages are organized along shared neurocognitive dimensions, while systematic variation along these dimensions is associated with climate. Using word embeddings from 5…

3. Very close Cited by this paper Not relevant

   Bilingual language processing relies on shared semantic representations that are modulated by each language

   Catherine Chen, Xue L. Gong, Christine Tseng et al. · 2025 · bioRxiv · Not summarized yet

   Abstract

   Billions of people throughout the world are bilingual, and they can extract meaning from multiple languages. While some evidence suggests that there is a shared system in the human brain for processing semantic information from native and non-native languages, other evidence suggests that semantic processing is language-specific. We conducted a study to determine how semantic information for different languages is represented in the brains of bilinguals. Functional magnetic resonance imaging (fMRI) was used to record brain responses while participants read several hours of natural narratives in their native (Chinese) and non-native (English) languages. These data were then used to compare se…

4. Close Cited by this paper Not relevant

   Natural speech reveals the semantic maps that tile human cerebral cortex

   Alexander G. Huth, W. D. de Heer, T. Griffiths et al. · 2016 · Nature · Not summarized yet

   Abstract

   The meaning of language is represented in regions of the cerebral cortex collectively known as the ‘semantic system’. However, little of the semantic system has been mapped comprehensively, and the semantic selectivity of most regions is unknown. Here we systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI (fMRI) data collected while subjects listened to hours of narrative stories. We show that the semantic system is organized into intricate patterns that seem to be consistent across individuals. We then use a novel generative model to create a detailed semantic atlas. Our results suggest that most areas within the semantic system represent i…

5. Close Cited by this paper Not relevant

   Semantic encoding during language comprehension at single-cell resolution

   Mohsen Jamali, Benjamin L. Grannan, Jing Cai et al. · 2024 · Nature · Not summarized yet

   Abstract

   From sequences of speech sounds1,2 or letters3, humans can extract rich and nuanced meaning through language. This capacity is essential for human communication. Yet, despite a growing understanding of the brain areas that support linguistic and semantic processing4–12, the derivation of linguistic meaning in neural tissue at the cellular level and over the timescale of action potentials remains largely unknown. Here we recorded from single cells in the left language-dominant prefrontal cortex as participants listened to semantically diverse sentences and naturalistic stories. By tracking their activities during natural speech processing, we discover a fine-scale cortical representation of s…

6. Close Cited by this paper Not relevant

   An investigation across 45 languages and 12 language families reveals a universal language network

   Saima Malik-Moraleda, Dima Ayyash, J. Gallée et al. · 2022 · Nature Neuroscience · Not summarized yet

   Abstract

   To understand the architecture of human language, it is critical to examine diverse languages; however, most cognitive neuroscience research has focused on only a handful of primarily Indo-European languages. Here we report an investigation of the fronto-temporo-parietal language network across 45 languages and establish the robustness to cross-linguistic variation of its topography and key functional properties, including left-lateralization, strong functional integration among its brain regions and functional selectivity for language processing. fMRI reveals similar topography, selectivity and inter-connectedness of language brain areas across 45 languages. These properties may allow the l…

7. Close Not relevant

   How the brain distinguishes a bilingual’s languages is partly shared across people and partly their own

   Sinem Demirkan, Demian Wassermann, B. Sagot · 2026 · bioRxiv · Not summarized yet

   Abstract

   How personal is a cognitive distinction? People performing the same task show similar brain activity, which group-level neuroimaging builds on. Yet brain organization differs reliably between individuals, and people bring different experiences to every task. Bilingual language use is a natural test case. We asked whether the distinction between a bilingual’s languages is common to everyone performing the task, or whether each person keeps a part of their own. We decoded cortex-wide fMRI activity within each of 77 bilinguals naming pictures in Chinese, their dominant language, or English, switching languages from one moment to the next. Within a person, the language in use can be classified o…

8. Close Not relevant

   Divergent neural dynamics of multidimensional semantic access for concrete and abstract words.

   Yun-Hao Zhang, Shao-Nan Wang, Cheng-Qing Zong et al. · 2026 · Brain and Language · Not summarized yet

   Abstract

   Semantic comprehension requires accessing multiple dimensions of meaning, yet the temporal dynamics of these dimensions remain unclear. Previous research has predominantly examined the neural dynamics of lexical semantics as a whole, which is equivalent to investigating the average of neural dynamics across various dimensions of semantic processing. Here, we analyzed openly released electrocorticography (ECoG) recordings from 9 participants listening to naturalistic stories and used time-resolved encoding models to decompose neural responses into six semantic dimensions: vision, motor, space, time, socialness, and emotion. When analyzing all words together, nearly all dimensions showed a com…

Prior work it builds on

1. Close Not relevant

   A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages

   Alexander B. Silva, Jessie R. Liu, Sean L. Metzger et al. · 2024 · Nature Biomedical Engineering · Not summarized yet

   Abstract

   Advancements in decoding speech from brain activity have focused on decoding a single language. Hence, the extent to which bilingual speech production relies on unique or shared cortical activity across languages has remained unclear. Here, we leveraged electrocorticography, along with deep-learning and statistical natural-language models of English and Spanish, to record and decode activity from speech-motor cortex of a Spanish–English bilingual with vocal-tract and limb paralysis into sentences in either language. This was achieved without requiring the participant to manually specify the target language. Decoding models relied on shared vocal-tract articulatory representations across lang…

2. Close Not relevant

   Semantic reconstruction of continuous language from non-invasive brain recordings

   Jerry Tang, Amanda LeBel, Shailee Jain et al. · 2022 · bioRxiv · Not summarized yet

   Abstract

   A brain–computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the dec…

3. Close Not relevant

   Neurolinguistics: Structural plasticity in the bilingual brain

   A. Mechelli, J. Crinion, U. Noppeney et al. · 2004 · Nature · Not summarized yet
4. Close Not relevant

   A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations

   Ariel Goldstein, Hao-Cheng Wang, Leonard Niekerken et al. · 2025 · Nature Human Behaviour · Not summarized yet

   Abstract

   This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals across 100 h of speech production and comprehension as participants engaged in open-ended real-life conversations. We extracted low-level acoustic, mid-level speech and contextual word embeddings from a multimodal speech-to-text model (Whisper). We developed encoding models that linearly map these embeddings onto brain activity during speech production and comprehension. Remarkably, this model accurately predicts neural activity at each level of the…

5. Close Not relevant

   A synchronized multimodal neuroimaging dataset for studying brain language processing

   Shaonan Wang, Xiaohan Zhang, Jiajun Zhang et al. · 2022 · Scientific Data · Not summarized yet

   Abstract

   We present a synchronized multimodal neuroimaging dataset for studying brain language processing (SMN4Lang) that contains functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data on the same 12 healthy volunteers while the volunteers listened to 6 hours of naturalistic stories, as well as high-resolution structural (T1, T2), diffusion MRI and resting-state fMRI data for each participant. We also provide rich linguistic annotations for the stimuli, including word frequencies, syntactic tree structures, time-aligned characters and words, and various types of word and character embeddings. Quality assessment indicators verify that this is a high-quality neuroimaging da…