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Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech

https://doi.org/10.1101/2020.10.02.324392

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The Temporal Structures and Functional Significance of Scale-free Brain Activity

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Slow Cortical Dynamics and the Accumulation of Information over Long Timescales

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A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex

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Large-Scale Gradients in Human Cortical Organization

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Natural speech reveals the semantic maps that tile human cerebral cortex

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A hierarchy of intrinsic timescales across primate cortex

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Multiple timescales of neural dynamics and integration of task-relevant signals across cortex

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Incorporating Context into Language Encoding Models for fMRI

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Speaker identification on the SCOTUS corpus

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Predicting Human Brain Activity Associated with the Meanings of Nouns

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COMPLETE FUNCTIONAL CHARACTERIZATION OF SENSORY NEURONS BY SYSTEM IDENTIFICATION

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A place for time: the spatiotemporal structure of neural dynamics during natural audition

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A Selected Core Microbiome Drives the Early Stages of Three Popular Italian Cheese Manufactures

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The Hierarchical Cortical Organization of Human Speech Processing

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Topographic Mapping of a Hierarchy of Temporal Receptive Windows Using a Narrated Story

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Critical Behavior in Physics and Probabilistic Formal Languages

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Regularizing and Optimizing LSTM Language Models

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Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

What points at this work

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Cognitive Signals of Language Processing

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Brain-inspired computing based on deep learning for human-computer interaction: A review

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Neural populations in the language network differ in the size of their temporal receptive windows

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The language network as a natural kind within the broader landscape of the human brain

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A natural language fMRI dataset for voxelwise encoding models

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HED LANG – A Hierarchical Event Descriptors library extension for annotation of language cognition experiments

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The cortical representation of language timescales is shared between reading and listening

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Testing the limits of natural language models for predicting human language judgements

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Bilingual language processing relies on shared semantic representations that are modulated by each language

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A unifying computational account of temporal context effects in language across the human cortex

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Illusions of Alignment Between Large Language Models and Brains Emerge From Fragile Methods and Overlooked Confounds

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A two-dimensional space of linguistic representations shared across individuals

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Correspondence between the Video-Learning Deep Neural Networks and EEG Brain Activity during Naturalistic Video Viewing

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Unpacking the Expressed Consequences of AI Research in Broader Impact Statements

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Language in Brains, Minds, and Machines

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Exploring Temporal Sensitivity in the Brain Using Multi-timescale Language Models: An EEG Decoding Study

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The Voxelwise Encoding Model framework: A tutorial introduction to fitting encoding models to fMRI data

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Probing Word Syntactic Representations in the Brain by a Feature Elimination Method

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Non-Complementarity of Information in Word-Embedding and Brain Representations in Distinguishing between Concrete and Abstract Words

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Attention weights accurately predict language representations in the brain

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Multi-timescale Representation Learning in LSTM Language Models

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The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data

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Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses

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Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

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Divergences between Language Models and Human Brains

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Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

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