Naturalistic Sentence Comprehension in the Brain
Bibliographic Data
| ID | 7900772 |
|---|---|
| Authors | Jonathan Brennan (0000-0002-3639-350X, University of Michigan, corresponding author) |
| Year | 2016 |
| Volume | 10 |
| Issue | 7 |
| Pages | 299-313 |
| Publication date | 2016-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Language and Linguistics Compass (JOURNAL) |
| Journal identifiers | ISSN: 1749-818X • E-ISSN: 1749-818X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/lnc3.12198 |
| OpenAlex | W2473017576 |
| Language | EN |
| Citations received | 5 |
| References cited | 65 |
The cognitive neuroscience of language relies largely on controlled experiments that are different from the everyday situations in which we use language. This review describes an approach that studies specific aspects of sentence comprehension in the brain using data collected while participants perform an everyday task, such as listening to a story. The approach uses ‘neuro‐computational’ models that are based on linguistic and psycholinguistic theories. These models quantify how a specific computation, such as identifying a syntactic constituent, might be carried out by a neural circuit word‐by‐word. Model predictions are tested for their statistical fit with measured brain data. The paper discusses three applications of this approach: (i) to probe the location and timing of linguistic processing in the brain without requiring unnatural tasks and stimuli, (ii) to test theoretical hypotheses by comparing the fits of different models to naturalistic data, and (iii) to study neural mechanisms for language processing in populations that are poorly served by traditional methods
Active listening · Cognition · Cognitive psychology · Cognitive science · Comprehension · Linguistics · Natural language processing · Psycholinguistics · Sentence · Sentence processing · Child and Animal Learning Development · Communication · Computer Science · Neurobiology of Language and Bilingualism · Neuroscience · Psychology · Reading and Literacy Development · Artificial Intelligence
Depth of processing in language comprehension
Prediction during language comprehension
Rational integration of noisy evidence and prior semantic expectations in sentence interpretation
Nonparametric statistical testing of EEG- and MEG-data
A probabilistic earley parser as a psycholinguistic model
Syntactically Based Sentence Processing Classes
Speaker–listener neural coupling underlies successful communication
Natural speech reveals the semantic maps that tile human cerebral cortex
The syntactic positive shift (sps) as an erp measure of syntactic processing
Thinking ahead
Probabilistic word pre-activation during language comprehension inferred from electrical brain activity
The independence of combinatory semantic processing
Uncertainty About the Rest of the Sentence
Good-Enough Representations in Language Comprehension
Aligning Grammatical Theories and Language Processing Models
Information-theoretical Complexity Metrics
The 'Good Enough' Approach to Language Comprehension
| Citations per year | 1 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |