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Decoding Natural Behavior from Neuroethological Embedding
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Neural Encoding and Decoding With Distributed Sentence Representations.

Jingyuan Sun, Shaonan Wang, Jiajun Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 14, 2020
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    Distributed semantic models (DSMs) effectively predict brain activity during sentence reading, with transformer-based DSMs outperforming others. Semantic representation granularity explains model performance in mapping language to neural patterns.

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    Area of Science:

    • Cognitive Neuroscience
    • Computational Linguistics
    • Neuroimaging

    Background:

    • Understanding the human linguistic system relies on computational models of cortical language representation.
    • Distributed semantic models (DSMs), particularly transformer-based ones, show promise for probing brain language processing.
    • While DSMs explain word-level cortical responses, their application to sentence processing and neural representations is less explored.

    Purpose of the Study:

    • To investigate the relationship between cortical sentence representations and DSMs.
    • To identify linguistic features that best correlate DSMs with brain activity during sentence comprehension.
    • To explore if distributed sentence representations reveal semantic selectivity in different brain regions.

    Main Methods:

    • Utilized neural encoding and decoding approaches with advanced natural language representation learning.
    • Evaluated 12 DSMs for predicting and deciphering functional magnetic resonance imaging (fMRI) data from human sentence readers.
    • Employed probing and ablation tasks to analyze model performance and semantic representation granularity.

    Main Results:

    • Most DSMs achieved high accuracy in predicting fMRI data in the left middle temporal gyrus (LMTG) and left occipital complex (LOC).
    • Transformer-based DSM encoders significantly outperformed unsupervised structured models and unstructured baselines.
    • Model performance differences were partly explained by the granularity of semantic representations, with DSMs showing selectivity for concept categories.

    Conclusions:

    • Transformer-based DSMs are superior for modeling neural representations of sentence processing.
    • The granularity of semantic information within DSMs is crucial for accurately predicting brain activity.
    • Findings support the use of DSMs for understanding semantic selectivity in the brain and developing brain-machine interfaces.