Nai Ding

NeuralRNN unifies RNN methods for cognitive neuroscience tasks by providing a single framework for model implementation, comparison, and extension.

This paper identifies two distinct temporal timescales organizing human vocal behavior, showing they are present from infancy and support different functions in vocal production and synchronization.

The paper shows that the auditory cortex uses efficient coding to encode time intervals in speech, with neural responses adapting to duration statistics and exhibiting compressive nonlinearity.

The paper shows low-frequency neural activity reflects context-dependent parsing of word sequences, tracking sentences rather than individual words.

The paper identifies a common frontal-temporal network for motor synchronization to auditory and visual rhythms, showing it is based on intrinsic cortical connections rather than sensory experience.

The paper shows that human working memory represents sequences using 2-D neural geometry, encoding item and chunk positions, and this structure persists across different experimental conditions.

The paper uses Utah array recordings to show that the human motor cortex encodes handwriting through a sequence of primitive neural states, improving trajectory reconstruction accuracy by 84%.

This study shows that attention distribution during goal-directed reading is predicted by transformer-based DNNs optimized for the same task.

The paper investigates how the auditory system segregates speech from echoes and encodes them as separate streams, showing that this process restores missing temporal modulations in echoic speech.

The paper analyzes speech envelope modulation spectrum and local temporal features to determine their relationship with syllable rhythm across multiple languages.

This study investigates how the brain integrates hierarchical linguistic units across auditory, visual, and audio-visual modalities using EEG and TMS, finding that sentence integration involves the left prefrontal cortex.

This study shows delta-band cortical activity tracks word rhythm during natural speech comprehension, but not the acoustic envelope, and this tracking diminishes when attention is diverted.

The paper investigates how noise and linguistic knowledge affect bilingual speech comprehension, finding that noise impairs neural tracking of speech structure in second languages.

This study shows that neural activity tracks task-related chunks rather than word properties during speech listening.

The paper investigates whether neural rhythms reflect internal syntactic processes by analyzing EEG and behavioral data from participants listening to grammatical and ungrammatical sentences.

This study investigates how auditory and language mechanisms contribute to noise-robust speech envelope tracking in humans by comparing native and foreign language listeners using EEG.

This study shows that top-down attention is required for lexical knowledge-based grouping of syllables into words, as evidenced by reduced neural entrainment to words when attention is diverted.

This study compares the slow temporal modulations in speech and music across languages and instruments, finding distinct rhythmic patterns that can reliably distinguish the two.