Neuro-Computational Models of Natural Language
In this project we build computational models of sentence understanding and evaluate their fit against neural signals collected from people performing a relatively natural task, like listening to a story.
Funding NSF #IIS-1607251, 2017-2019
Publications
Decoding the Neural Dynamics of Headed Syntactic Structure Building
The brain builds hierarchical phrases during language comprehension; however, the representational details and dynamics of the …
Hierarchical syntactic structure in human-like language models
Language models (LMs) are a meeting point for cognitive modeling and computational linguistics. How should they be designed to serve as …
Localizing Syntactic Composition with Left-Corner Recurrent Neural Network Grammars
In computational neurolinguistics, it has been demonstrated that hierarchical models such as Recurrent Neural Network Grammars (RNNGs), …
Expectations modulate retrieval interference during ellipsis resolution
Memory operations during language comprehension are subject to interference: re-trieval is harder when items are linguistically similar …
Modeling structure-building in the brain with CCG parsing and large language models
To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to …
Neural correlates of object-extracted relative clause processing across English and Chinese
Are the brain bases of language comprehension the same across all human languages, or do these bases vary in a way that corresponds to …
Neuro-computational models of language processing
Efforts to understand the brain bases of language face the mapping problem: at what level do linguistic computations and …
Le Petit Prince: A Multilingual fMRI Corpus Using Ecological Stimuli
Neuroimaging using more ecologically valid stimuli such as audiobooks has advanced our understanding of natural language comprehension …
Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar
Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to …
Localizing syntactic predictions using recurrent neural network grammars
Brain activity in numerous perisylvian brain regions is modulated by the expectedness of linguistic stimuli. We leverage recent …
The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension
The Alice Datasets combine observations from magnetic resonance imaging as well as electrophysiology while human participants listened …
The Little Prince in 26 Languages: Towards a Multilingual Neuro-Cognitive Corpus
The Alice Datasets combine observations from magnetic resonance imaging as well as electrophysiology while human participants listened …
Phase synchronization varies systematically with linguistic structure composition
Computation in neuronal assemblies is putatively reflected in the excitatory and inhibitory cycles of activation distributed throughout …
Text Genre and Training Data Size in Human-Like Parsing
Domain-specific training typically makes NLP systems work better. We show that this extends to cognitive modeling as well by relating …
Hierarchical structure guides rapid linguistic predictions during naturalistic listening
The grammar, or syntax, of human language is typically understood in terms of abstract hierarchical structures. However, theories of …
Finding syntax in human encephalography with beam search
Recurrent neural network grammars (RNNGs) are generative models of (tree, string) pairs that rely on neural networks to evaluate …
Localising memory retrieval and syntactic composition: an fMRI study of naturalistic language comprehension
This study examines memory retrieval and syntactic composition using fMRI while participants listen to a book, The Little Prince. These …
Differentiating Phrase Structure Parsing and Memory Retrieval in the Brain
On some level, human sentence comprehension must involve both memory retrieval and structural composition. This study differentiates …
MEG Evidence for Incremental Sentence Composition in the Anterior Temporal Lobe
Research investigating the brain basis of language comprehension has associated the left anterior temporal lobe (ATL) with …
Naturalistic Sentence Comprehension in the Brain
The cognitive neuroscience of language relies largely on controlled experiments that are different from the everyday situations in …
Abstract linguistic structure correlates with temporal activity during naturalistic comprehension
Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information …
Temporal Lobes as Combinatory Engines for both Form and Meaning
The relative contributions of meaning and form to sentence processing remains an outstanding issue across the language sciences. We …
Modeling fMRI time courses with linguistic structure at various grain sizes
Neuroimaging while participants listen to audiobooks provides a rich data source for theories of incremental parsing. We compare nested …
Talks
Exploring Temporal Response Functions (TRFs) in Real-World Speech Processing
Workshop discussing the application of temporal response functions to understand the neural electrophysiological dynamics of every-day language use.
Society for the Neurobiology of Language
Lab members presenting work on L1/L2 surprisal and layerwise alignment between artificial neural networks and human electrophysiology.
Colloquium: McMaster University Linguistics
Grammatical dynamics and neural decoding with EEG
Colloquium: Northwestern University Linguistics
Grammatical dynamics and neural decoding with EEG
Society for the Neurobiology of Language
Lab members presenting work on decoding verb phrases, retrieval during naturalistic composition, and morphological decomposition.
Colloquium: UC Irvine Language Science
Linking neural systems to syntax using parsing models
MPI for Human Cognitive and Brain Sciences, Language Cycles Group
Linking neural systems to syntax using parsing models
Society for the Neurobiology of Language
Posters on decoding grammar from EEG, modeling memory operations with neural networks, and naturalistic electrophysiological datasets for L2 conversation
NLP@Michigan 2022
Keynote presentation Big data needs big theory to understand understanding
Conference on Human Sentence Processing
Join us virtually for posters on morphological priming, memory interference, and multi-modal approaches to understanding …
Neurobiology of Language: Key Issues and Ways Forward
The Max Planck Institute for Psycholinguistics hosts virtual a workshop on the challenges and the promises are for the neurobiology of language.
Society for the Neurobiology of Language
Posters on neural entrainment, relative clause processing, and accented-speech comprehension!
CUNY Conference on Human Sentence Processing
All the latest from the lab! Including: a talk from Rachel Weissler about expectation-based processing for African American Language, …
Symposium on Emergence in Communication & Learning
University of Michigan’s Center for the Study of Complex Systems hosts a discussion of communicating systems.
Society for the Neurobiology of Language
Student posters on dialect processing and neural entrainment, new work on memory mechanisms, and my ‘Early Career’ talk!
