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.
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.
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.
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