ArticlePLoS computational biology2022
Inferring the nature of linguistic computations in the brain.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Left Perisylvian Rhythms Encode Prosody and Syntax during Delayed Sentence Repetition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Structural and sequential regularities modulate phrase-rate neural tracking.Scientific reports · 2024Article
- "Not" in the brain and behavior.PLoS biology · 2024Article
- A tradeoff between acoustic and linguistic feature encoding in spoken language comprehension.eLife · 2023Article
- Naturalistic Spoken Language Comprehension Is Supported by Alpha and Beta Oscillations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
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Authors and funding
3 authors.
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Abstract
Sentences contain structure that determines their meaning beyond that of individual words. An influential study by Ding and colleagues (2016) used frequency tagging of phrases and sentences to show that the human brain is sensitive to structure by finding peaks of neural power at the rate at which structures were presented. Since then, there has been a rich debate on how to best explain this pattern of results with profound impact on the language sciences. Models that use hierarchical structure building, as well as models based on associative sequence processing, can predict the neural response, creating an inferential impasse as to which class of models explains the nature of the linguistic computations reflected in the neural readout. In the current manuscript, we discuss pitfalls and common fallacies seen in the conclusions drawn in the literature illustrated by various simulations. We conclude that inferring the neural operations of sentence processing based on these neural data, and any like it, alone, is insufficient. We discuss how to best evaluate models and how to approach the modeling of neural readouts to sentence processing in a manner that remains faithful to cognitive, neural, and linguistic principles.
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