Linguistics and human brain: a perspective of computational neuroscience
This paper explores how linguistics and neuroscience can work together to better understand how the brain processes language. It discusses different theories of language and the challenges in connecting these theories to brain activity.
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- 1 This framework is founded on the hypothesis of a Universal Grammar, arguing that children's acquisition of complex grammar must be guided by innate principles, given the impoverished and limited nature of their linguistic input.
- 2 Language is conceptualized as a multi-layered abstract symbolic system encompassing distinct yet interconnected structures ranging from sounds (phonetics and phonology) to word formation (morphology), sentence structure (syntax), and meaning (semantics) (Chomsky 2002 ) .
- 3 They also support the emerging framework of model-brain alignment, wherein internal model representations are used to predict and explain aspects of neural responses during language processing .
- 4 Importantly, such predictive alignment is best interpreted as evidence that LLMs capture representational dimensions relevant to human language processing, while further behavioral, temporal, causal, and biological evidence is needed to support stronger mechanistic claims.
Introduction
Using a finite set of discrete elements and combinatorial rules, it can generate an infinite array of expressions, enabling the flexible and precise transmission of meaning -a feature known as recursion . In parallel, neuroscience investigates how coordinated neural activity across brain circuits supports language production and comprehension .
A persistent interdisciplinary challenge, however, arises from the methodological and explanatory divide between these fields.
Conversely, neural data alone often lack the computational interpretability needed to account for the structured, rule-governed nature of language.
Research Question
This framework is founded on the hypothesis of a Universal Grammar, arguing that children’s acquisition of complex grammar must be guided by innate principles, given the impoverished and limited nature of their linguistic input.
This framework is founded on the hypothesis of a Universal Grammar, arguing that children’s acquisition of complex grammar must be guided by innate principles, given the impoverished and limited nature of their linguistic input.
Methodology
Within this network, linguistic information is processed through hierarchical, parallel, and recurrent interactions, supported by bidirectional inter-regional connections that are dynamically regulated by contexts and task demands . While functional linguistics has been highly influential in discourse analysis, sociolinguistics, and cross-cultural communication studies, its strong emphasis on usage, context, and meaning poses significant challenges for strict formalization.
Study Design
Honorific systems, contextdependent expressions, and discourse-level differences are treated not as peripheral but as core objects of grammatical analysis .
For example, multilingual naturalistic fMRI datasets based on comparable narrative materials allow researchers to examine whether neural language representations are shared across languages or shaped by language-specific morphosyntactic and writing-system properties .
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Results & Findings
Language is conceptualized as a multi-layered abstract symbolic system encompassing distinct yet interconnected structures ranging from sounds (phonetics and phonology) to word formation (morphology), sentence structure (syntax), and meaning (semantics) (Chomsky 2002 ) . Linguistics seeks to formalize these implicit rules and structures in order to uncover the cognitive architecture underlying human linguistic competence.
- Language is conceptualized as a multi-layered abstract symbolic system encompassing distinct yet interconnected structures ranging from sounds (phonetics and phonology) to word formation (morphology), sentence structure.
- Linguistics seeks to formalize these implicit rules and structures in order to uncover the cognitive architecture underlying human linguistic competence.
- Abstract linguistic theories are often formulated as symbolic, hierarchical systems that are difficult to map directly onto the dynamic, distributed patterns of neural activity observed through.
- This gap between theoretical description and empirical evidence limits a comprehensive understanding of language and its neural basis.
- Consequently, neither purely linguistic models nor isolated neural observations can fully explain the integrated mechanisms of human language processing.
These approaches are directly relevant to debates about universal grammar, typological diversity, and the neural generalizability of model-brain alignment.
Recent studies using this framework have begun to reveal several recurring patterns, although the evidence base remains comparatively limited and uneven across tasks, languages, and interaction settings.
Practical Applications
However, for languages with highly complex structures or those that differ substantially from Indo-European languages, parameter-based explanations remain controversial. This suggests that a limited set of parameters may be insufficient to capture the full richness of linguistic diversity . Their central contribution lies in offering an explicit, hierarchical representational space that enables systematic investigation of how linguistic information may be organized in the brain .
Before the rise of contemporary Transformer-based LLMs, recurrent architectures provided one of the first neural modeling frameworks that could be compared more directly with temporally resolved neural data during naturalistic language processing.
Existing work on communication impairments and clinical translation already suggests that altered coupling, reduced stability of shared representations, and degraded decoding capacity may all be relevant to understanding pathological language processing .
Theoretical foundations of linguistics
This section reviews the evolution of linguistic theories and their limitations in aligning with neural mechanisms. It emphasizes the need for interdisciplinary integration to enhance understanding of language and its neural basis.
Frameworks of modern linguistic theories
This section outlines major linguistic frameworks such as generative grammar, functional linguistics, and cognitive linguistics, discussing their perspectives on language structure and the challenges they face in mapping to neural activity.
Cross-cultural evolution of linguistic theories
This section examines the diversity of languages and the challenges in constructing a unified linguistic framework. It discusses how different linguistic theories account for cross-linguistic variation and cultural contexts.
Frequently Asked Questions
It integrates linguistics, neuroscience, computer science, and systems theory to convert formal linguistic hypotheses into testable computational models, which are then tested against neural data . This framework is founded on the hypothesis of a Universal Grammar, arguing that children’s acquisition of.
For example, multilingual naturalistic fMRI datasets based on comparable narrative materials allow researchers to examine whether neural language representations are shared across languages or shaped by language-specific morphosyntactic and writing-system properties . Major linguistic theories are largely based on static sentence analysis.
Language is conceptualized as a multi-layered abstract symbolic system encompassing distinct yet interconnected structures ranging from sounds (phonetics and phonology) to word formation (morphology), sentence structure (syntax), and meaning (semantics) (Chomsky 2002 ) . They also support the emerging framework of model-brain.
Generative grammar emphasizes abstract, structure-dependent operations and therefore predicts that neural language systems should be sensitive to hierarchical syntactic relations, recursion, and long-distance dependencies beyond surface-level word sequence statistics . EEG is therefore highly sensitive to synchronized cortical activity but provides limited.
These approaches are directly relevant to debates about universal grammar, typological diversity, and the neural generalizability of model-brain alignment. Recent studies using this framework have begun to reveal several recurring patterns, although the evidence base remains comparatively limited and uneven across tasks.
This paper explores how linguistics and neuroscience can work together to better understand how the brain processes language. It discusses different theories of language and the challenges in connecting these theories to brain activity.