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Updated: Jan 15, 2026

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NeuroAdaptive multi-resolution integration network for decoding cognitive complexity levels in EEG-based pronoun

Wenlong Wu¹, Mengyuan Zhao², Zhong Yin¹

¹Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, PR China; School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 13, 2026

Summary

This summary is machine-generated.

Researchers developed a new AI model, NeuroAdaptive Multi-resolution Integration Network (NAMINet), to decode brain activity during pronoun resolution. NAMINet successfully differentiates cognitive complexity levels in language processing, offering insights into brain-language relationships.

Keywords:

Biomedical signal processing Deep learning Electroencephalogram Neurophysiological classification Pronoun resolution

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Published on: May 9, 2019

Area of Science:

Cognitive Neuroscience
Computational Linguistics
Neuroimaging

Background:

Pronoun resolution is key to language comprehension but its cognitive complexity varies.
Existing models struggle with neural interference, multi-region analysis, and individual differences in pronoun processing.
Understanding the neural basis of graded complexity in language is crucial.

Purpose of the Study:

To investigate neural processing differences across three cognitive complexity levels of pronoun resolution.
To develop a computational model capable of decoding these complexity levels from EEG data.
To address limitations in existing models regarding background interference and individual variability.

Main Methods:

Utilized electroencephalogram (EEG) recordings from 35 participants.
Developed the NeuroAdaptive Multi-resolution Integration Network (NAMINet) for EEG decoding.
NAMINet incorporates spatial and dual-domain encoders with a fusion module for multi-resolution analysis.

Main Results:

Distinct neural processing strategies were identified for different pronoun complexity levels.
NAMINet achieved significant classification accuracies: 54.21% (participant-dependent) and 43.59% (cross-participant).
Results showed variations in behavioral performance and EEG spectral patterns correlating with complexity.

Conclusions:

Established the neural substrates underlying graded cognitive complexity in language processing.
Provided a computational framework for decoding hierarchical linguistic mechanisms from neural data.
Advanced the understanding of brain-language relationships in pronoun resolution.