Does word meaning change according to the context? Although this hypothesis has existed for a long time, only recently it has become possible to test it based on neuroimaging. Embodiment theories of knowledge representation suggest that word meaning consist of a collection of attributes de-fined in terms of various neural systems. This approach represents an un-limited number of objects through weighted attributes and the weights may change in context. This paper aims at quantifying such dynamic meanings using computational modeling. A neural network is trained with backpropagation to map attribute-based representations to fMRI images of subjects reading everyday sentences. Backpropagation is then extended to the features, demonstrating how they change in different sentence contexts for the same word. Indeed, statistically significant changes occurred across similar contexts and across different subjects, quantifying for the first time how at-tribute weightings for the same word are modified by context. Such dynamic representations of meaning could be used in future natural language processing systems, allowing them to mirror human performance more accurately.

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In Shouyi Wang, Vicky Yamamoto, Jianzhong Su, Yang Yang, Eric Jones, Leon Iasemidis, Tom Mitchell, editors, Brain Informatics. BI 2018. Lectures Notes in Computer Sciences, 11309, 129-140, Arlington, Tx, USA, December 7-9,2018 2018. Springer, Cham.

@inproceedings{Aguirre-Celis:BrainInformatics2018, title={Combining fMRI Data and Neural Networks to Quantify Contextual Effects in the Brain}, author={Nora Aguirre-Celis and Risto Miikkulainen}, booktitle={Brain Informatics. BI 2018. Lectures Notes in Computer Sciences}, volume={11309}, month={December 7-9,2018}, editor={Shouyi Wang and Vicky Yamamoto and Jianzhong Su and Yang Yang and Eric Jones and Leon Iasemidis and Tom Mitchell}, address={Arlington, Tx, USA}, publisher={Springer, Cham}, pages={129-140}, url="http://nn.cs.utexas.edu/?Aguirre-Celis:BrainInformatics2018", year={2018} }