Probabilistic language models in cognitive neuroscience: Promises and pitfalls

Area of Science:

• Cognitive neuroscience
• Computational linguistics
• Neuroimaging

Background:

• Cognitive neuroscientists investigate the neural basis of language comprehension.
• Explicitly defining computational and processing complexity is crucial for understanding language.
• Probabilistic language models provide a computational framework for language complexity.

Purpose of the Study:

• To review the theoretical foundations of probabilistic language models in language comprehension.
• To present example neuroimaging studies utilizing these models.
• To highlight the advantages, potential pitfalls, and future directions of this approach.

Main Methods:

• Review of theoretical underpinnings of probabilistic language models.
• Analysis of neuroimaging studies applying these models to language comprehension.
• Discussion of computational and neurobiological signal evaluation.

Main Results:

• Probabilistic language models offer explicit accounts of language complexity.
• These models are increasingly evaluated against neurobiological signals, not just behavioral data.
• The approach provides tools for testing neural hypotheses in language comprehension.

Conclusions:

• Probabilistic language models are valuable for understanding the information-processing nature of language.
• Integrating these models with neuroimaging advances the study of neural computations in language.
• Further research should explore the full potential and limitations of this interdisciplinary approach.