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Incorporating Context into Language Encoding Models for fMRI

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What this work says

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

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Encoding and decoding in fMRI

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Natural speech reveals the semantic maps that tile human cerebral cortex

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COMPLETE FUNCTIONAL CHARACTERIZATION OF SENSORY NEURONS BY SYSTEM IDENTIFICATION

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

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Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses

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

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Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream

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BrainBench: A Brain-Image Test Suite for Distributional Semantic Models

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Deep Contextualized Word Representations

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Semi-supervised sequence tagging with bidirectional language models

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LSTM neural networks for language modeling

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Aligning context-based statistical models of language with brain activity during reading

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Pycortex: an interactive surface visualizer for fMRI

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Pixels to Voxels: Modeling Visual Representation in the Human Brain

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An overview of gradient descent optimization algorithms

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TensorFlow: A system for large-scale machine learning

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Decoding concept representations in aphasia after stroke

inferred← citeshttps://doi.org/10.64898/2026.05.13.724774openalex-snapshot

Distinct yet neighboring neural populations encode past, future, and surrounding speech context in the human temporal lobe

inferred← citeshttps://doi.org/10.64898/2026.06.30.735613openalex-snapshot

The human language processing system straightens natural speech

inferred← citeshttps://doi.org/10.70675/7e579b59za4f3z4699zae81zff865b550d84openalex-snapshot

Neural coding of uncertainty during statistical learning in humans : a study using ultra-high field functional magnetic resonance imaging

inferred← citeshttps://doi.org/10.70675/86b4843az71c8z41d4za9dcz3db2229b1254openalex-snapshot

Modelling non-native speech perception using automatic speech processing

inferred← citeshttps://doi.org/10.7554/elife.106543openalex-snapshot

Stimulus dependencies—rather than next-word prediction—can explain pre-onset brain encoding in naturalistic listening designs

inferred← citeshttps://doi.org/10.7554/elife.106543.1openalex-snapshot

Stimulus dependencies—rather than next-word prediction—can explain pre-onset brain encoding during natural listening

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