- Neuroscience research shows that artificial languages activate Broca’s and Wernicke’s areas, similar to natural languages.
- Syntax complexity influences brain activity levels, with structured conlangs requiring more cognitive effort.
- Learning artificial languages enhances cognitive flexibility, memory, and pattern recognition.
- AI and machine learning benefit from artificial languages by improving natural language processing models.
- Conlangs have potential applications in education, therapy, and neuroplasticity research.
Understanding Artificial Languages
Artificial languages, often referred to as “constructed languages” (or conlangs), are meticulously designed linguistic systems created for specific purposes—ranging from international communication to science fiction and linguistic experimentation. Unlike natural languages that evolve organically through cultural and historical shifts, conlangs are built with intentional structure and grammar. Some well-known artificial languages include Esperanto (designed for global communication), Klingon (from Star Trek), and High Valyrian (from Game of Thrones).
Conlangs can be categorized into three main types
- A priori languages – Constructed with entirely original vocabulary and grammar (e.g., Lojban).
- A posteriori languages – Derived from existing natural languages (e.g., Esperanto, rooted in European languages).
- Engineered languages – Created for philosophical, experimental, or scientific study (e.g., Ithkuil).
Despite their artificial nature, some conlangs function as fully expressive languages, capable of conveying complex ideas, emotions, and narratives—leading to the question: how does the human brain process them compared to natural languages?
The Brain’s Language Processing System
Language comprehension, production, and processing are governed by multiple interconnected regions of the brain. Among them, the most prominent are
- Broca’s Area – Located in the left frontal lobe, Broca’s area is crucial for grammar processing, sentence structure, and speech production.
- Wernicke’s Area – Found in the temporal lobe, this region plays a key role in language comprehension and meaning extraction.
- The Auditory Cortex – Located in the superior temporal gyrus, it processes spoken sounds and phonetic structures.
- The Motor Cortex – Supports speech articulation and physical production of language.
- The Angular Gyrus – Plays a role in reading, writing, and translating language into meaning.
When people encounter new languages—whether natural or artificial—their brains rely on these interconnected regions to decode syntax, process semantics, and produce appropriate responses. The extent to which artificial languages stimulate these areas sheds light on the cognitive complexities of language acquisition and usage.
Neuroscience Research on Artificial Language Processing
Recent neuroscience studies indicate that conlangs activate the brain’s language centers similarly to natural languages. Functional MRI (fMRI) scans reveal that Broca’s and Wernicke’s areas show strong activation when individuals engage with well-developed artificial languages. This suggests that structured conlangs follow the same neurological pathways as organic linguistic systems.
Grammar and Syntax Complexity Matter
One of the most significant findings in neuroscience research is that the complexity of a language’s grammar influences brain activation levels. For example
- Simple conlangs with minimal grammar rules require minimal cognitive effort.
- Complex conlangs, particularly those with intricate syntax and morphology, demand more neural resources.
Studies show that when subjects learn conlangs with rigorous grammatical structures (such as Ithkuil), they exhibit increased neural activity in Broca’s area compared to simpler artificial languages. This finding aligns with studies on natural language acquisition, where more grammatically complex languages demand greater cognitive effort.
Neural Plasticity and Conlang Learning
Neuroscientists also observe increased neural connectivity in individuals who become fluent in artificial languages. Learning a conlang strengthens synaptic connections in language-processing areas, enhancing cognitive flexibility. These findings parallel research on bilingualism, suggesting that exposure to artificial languages benefits brain function in ways comparable to natural bilingual learning.
Cognitive Benefits of Learning Artificial Languages
Mastering a new language—whether natural or artificial—can significantly enhance cognitive abilities. Research on bilingualism and multilingualism indicates that learning multiple languages improves
- Memory retention – Strengthening short-term and long-term recall.
- Cognitive flexibility – Enhancing the ability to switch between tasks efficiently.
- Problem-solving skills – Training the brain to recognize and apply patterns in new contexts.
How Artificial Languages Enhance Cognition
Artificial languages provide unique cognitive advantages
- Enhanced Pattern Recognition – Many conlangs are designed with strict, logical structures, helping learners recognize linguistic patterns quickly.
- Improved Attention Control – Since artificial languages require deliberate engagement, they strengthen the brain’s ability to focus on structured linguistic input.
- Better Executive Function – Learning conlangs fosters mental agility, sharpening decision-making skills.
Additionally, experimental conlangs like Lojban—with logically constructed grammar—can train the brain to process communication in a more structured, rational manner.
The Role of Artificial Languages in AI and Linguistics
Beyond human cognition, artificial languages play a crucial role in artificial intelligence (AI), computational linguistics, and natural language processing (NLP). Machine learning researchers construct formalized artificial languages to refine AI-driven language models, improving their ability to process human syntax, semantics, and communication patterns.
Artificial Languages and AI Development
- Structured syntax models – AI developers use artificial languages to test how machines analyze and reproduce knowledge representation.
- Improving neural networks – Understanding how humans process artificial languages helps refine NLP and chatbot models.
- Bridging human-computer interaction – Studies on brain language processing in conlangs guide improvements in AI communication interfaces.
Virtual assistants and translation programs continue to benefit from research into structured artificial languages. By studying how humans interact with and learn conlangs, developers create smarter, more intuitive AI-driven language models.
Artificial Languages in Education and Therapy
Conlangs as Educational Tools
Structured artificial languages, such as Esperanto, have been used in educational settings to ease students into language learning. Since Esperanto’s grammar is highly regular, it serves as an accessible gateway into multilingual studies—research suggests learning Esperanto before another foreign language can accelerate language acquisition.
Additionally, students studying conlangs develop stronger linguistic awareness by deconstructing syntax, morphology, and phonetics in ways that reinforce language learning strategies.
Therapeutic Applications of Artificial Languages
Speech-language therapists explore potential uses of artificial languages in interventions for
- Aphasia rehabilitation – Using structured, repetitive conlang exercises to aid in language recovery.
- Autism communication training – Introducing predictable conlangs to enhance social language processing.
- Cognitive decline treatment – Encouraging artificial language learning as a preventive measure against neurodegenerative conditions.
These applications highlight conlangs’ versatility beyond entertainment, making them valuable cognitive tools in medical and educational domains.
Challenges and Limitations in Studying Artificial Language Processing
Despite the growing interest in the neuroscience of conlangs, challenges remain in studying artificial language acquisition.
Fluency-Level Variability
Since most conlang learners do not achieve full fluency, many studies analyze incomplete language processing. Brain activation may differ for fully fluent speakers compared to experimental learners.
Real-Time Speech vs. Structured Learning
Neurologists note that spontaneous speech requires different cognitive mechanisms than structured language study. Much research tends to focus on controlled reading or comprehension tasks rather than real-world language production.
Comparing Artificial vs. Natural Language Processing
One major research question remains: do artificial languages stimulate the brain identically to natural languages, or are there qualitative differences? Further research must distinguish genuine fluency effects from artificial constraints in experimental design.
Future Directions in Neuroscience & Language Research
Neuroscientists and linguists continue exploring artificial languages as a gateway to understanding the human brain. Future research will likely focus on
- Advanced neuroimaging studies – Using next-generation fMRI to map detailed brain activity in fluent conlang speakers.
- Fluency-based investigations – Analyzing how long-term artificial language immersion affects cognitive performance.
- AI-human interaction research – Examining potential overlaps between conlang structures and computational language models.
As artificial languages gain recognition beyond fictional and linguistic circles, they will continue influencing cognitive science, education, and AI research.
Citations
- Friederici, A. D. (2017). Language in our brain: The origins of a uniquely human capacity. MIT Press.
- Poeppel, D., Embick, D., & Hickok, G. (2012). Towards a new neurobiology of language. Journal of Neuroscience, 32(41), 14125-14131. https://doi.org/10.1523/JNEUROSCI.3244-12.2012
- Bialystok, E., Craik, F. I. M., & Luk, G. (2012). Bilingualism: Consequences for mind and brain. Trends in Cognitive Sciences, 16(4), 240-250. https://doi.org/10.1016/j.tics.2012.03.001
- Oppenheim, G. M., & Dell, G. S. (2010). Motor movement in language production. Cognition, 116(2), 149-164. https://doi.org/10.1016/j.cognition.2010.04.009
