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Neural dynamics of phoneme sequences: Position-invariant code for content and order

https://doi.org/10.1101/2020.04.04.025684

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What this work says

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

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Detection of phonological features in continuous speech using neural networks

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Acoustic-phonetic representations in word recognition

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Shortlist: a connectionist model of continuous speech recognition

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Processing interactions and lexical access during word recognition in continuous speech

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The TRACE model of speech perception

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Serial order and consonant–vowel structure in a graphemic output buffer model

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Non-linear processing of a linear speech stream: The influence of morphological structure on the recognition of spoken Arabic words

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Deep neural network models of sensory systems: windows onto the role of task constraints

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Prediction in the service of comprehension: Modulated early brain responses to omitted speech segments

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Temporal Predictive Codes for Spoken Words in Auditory Cortex

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Rapid Transformation from Auditory to Linguistic Representations of Continuous Speech

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Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech

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MNE software for processing MEG and EEG data

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Low-frequency cortical responses to natural speech reflect probabilistic phonotactics

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Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations

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Hippocampal “Time Cells” Bridge the Gap in Memory for Discontiguous Events

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The Encoding of Speech Sounds in the Superior Temporal Gyrus

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inferredcites →https://doi.org/10.1017/s0952675700000658openalex-snapshot

Towards an articulatory phonology

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Articulatory gestures as phonological units

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Perceptual restoration of masked speech in human cortex

inferredcites →https://doi.org/10.1038/nn.2641openalex-snapshot

Categorical speech representation in human superior temporal gyrus

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Tracking the signal, cracking the code: speech and speech comprehension in non-invasive human electrophysiology

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Discriminating the Influence of Correlated Factors from Multivariate Observations: the Back-to-Back Regression

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The N1 Wave of the Human Electric and Magnetic Response to Sound: A Review and an Analysis of the Component Structure

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Speaker identification on the SCOTUS corpus

inferredcites →https://doi.org/10.1126/science.1245994openalex-snapshot

Phonetic Feature Encoding in Human Superior Temporal Gyrus

inferredcites →https://doi.org/10.1146/annurev.neuro.29.051605.113038openalex-snapshot

The Neural Basis of Decision Making

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Mechanics of the Mammalian Cochlea

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In Spoken Word Recognition, the Future Predicts the Past

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The Human Brain Encodes a Chronicle of Visual Events at Each Instant of Time Through the Multiplexing of Traveling Waves

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Dynamic Encoding of Acoustic Features in Neural Responses to Continuous Speech

inferredcites →https://doi.org/10.1523/jneurosci.5069-11.2012openalex-snapshot

Predictive Top-Down Integration of Prior Knowledge during Speech Perception

inferredcites →https://doi.org/10.1524/auto.2011.0951openalex-snapshot

Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”

inferredcites →https://doi.org/10.18653/v1/w18-0104openalex-snapshot

Phonological (un)certainty weights lexical activation

inferredcites →https://doi.org/10.2307/1420917openalex-snapshot

Short-Term Memory for Phonemically Similar Lists

inferredcites →https://doi.org/10.31219/osf.io/fq6gdopenalex-snapshot

Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech

inferredcites →https://doi.org/10.3389/fnhum.2016.00604openalex-snapshot

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

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The English Lexicon Project

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Is neuroimaging measuring information in the brain?

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In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Modulation of brain activity by psycholinguistic information during naturalistic speech comprehension and production

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Latent neural dynamics encode temporal context in speech

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Multivariate analysis of speech envelope tracking reveals coupling beyond auditory cortex

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Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex

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Perceiving and remembering speech depend on multifractal nonlinearity in movements producing and exploring speech

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Perceiving and remembering speech depend on multifractal nonlinearity in movements producing and exploring speech

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inferred← citeshttps://doi.org/10.1111/psyp.14491openalex-snapshot

Multivariate pattern analysis of EEG reveals nuanced impact of negation on sentence processing in the N400 and later time windows

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Interdependence of “What” and “When” in the Brain

inferred← citeshttps://doi.org/10.1162/nol_a_00084openalex-snapshot

Auditory Word Comprehension Is Less Incremental in Isolated Words

inferred← citeshttps://doi.org/10.1523/eneuro.0069-26.2026openalex-snapshot

Electrophysiological Indices of Hierarchical Speech Processing Differentially Reflect the Comprehension of Speech in Noise

inferred← citeshttps://doi.org/10.1523/jneurosci.0906-20.2021openalex-snapshot

Neurophysiological Indices of Audiovisual Speech Processing Reveal a Hierarchy of Multisensory Integration Effects

inferred← citeshttps://doi.org/10.31234/osf.io/6ng2kopenalex-snapshot

Decoding the temporal dynamics of spoken word and nonword processing from EEG

inferred← citeshttps://doi.org/10.31234/osf.io/6ng2k_v1openalex-snapshot

Decoding the temporal dynamics of spoken word and nonword processing from EEG

inferred← citeshttps://doi.org/10.31234/osf.io/pa5fqopenalex-snapshot

Defining brain pathways for human spoken language and vocal learning

inferred← citeshttps://doi.org/10.31234/osf.io/xs5kropenalex-snapshot

A mathematical neural process model of language comprehension, from syllable to sentence

inferred← citeshttps://doi.org/10.3389/fnhum.2021.585817openalex-snapshot

Phonological Underspecification: An Explanation for How a Rake Can Become Awake

inferred← citeshttps://doi.org/10.3390/brainsci12050681openalex-snapshot

DIANA, a Process-Oriented Model of Human Auditory Word Recognition

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An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions

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A tradeoff between acoustic and linguistic feature encoding in spoken language comprehension

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