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Combining computational controls with natural text reveals new aspects of meaning composition

View ORCID ProfileMariya Toneva, Tom M. Mitchell, View ORCID ProfileLeila Wehbe
doi: https://doi.org/10.1101/2020.09.28.316935
Mariya Toneva
1Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
2Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
3Neuroscience Institute, Princeton University, Princeton, USA
4Max Planck Institute for Software Systems, Saarbrücken, Germany
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  • ORCID record for Mariya Toneva
Tom M. Mitchell
1Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
2Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
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Leila Wehbe
1Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
2Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
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  • ORCID record for Leila Wehbe
  • For correspondence: lwehbe@cmu.edu
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Abstract

To study a core component of human intelligence—our ability to combine the meaning of words—neuroscientists have looked to theories from linguistics. However, linguistic theories are insufficient to account for all brain responses that reflect linguistic composition. In contrast, we adopt a data-driven computational approach to study the combined meaning of words beyond their individual meaning. We term this product “supra-word meaning” and investigate its neural bases by devising a computational representation for it and using it to predict brain recordings from two imaging modalities with complementary spatial and temporal resolutions. Using functional magnetic resonance imaging, we reveal that hubs that are thought to process lexical-level meaning also maintain supra-word meaning, suggesting a common substrate for lexical and combinatorial semantics. Surprisingly, we cannot detect supra-word meaning in magnetoencephalography, which suggests the hypothesis that composed meaning might be maintained through a different neural mechanism than the synchronized firing of pyramidal cells. This sensitivity difference has implications for past neuroimaging results and future wearable neurotechnology.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Added results with GPT-2 that replicate our results with ELMo in fMRI and MEG; included more discussion about future work

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 25, 2022.
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Combining computational controls with natural text reveals new aspects of meaning composition
Mariya Toneva, Tom M. Mitchell, Leila Wehbe
bioRxiv 2020.09.28.316935; doi: https://doi.org/10.1101/2020.09.28.316935
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Combining computational controls with natural text reveals new aspects of meaning composition
Mariya Toneva, Tom M. Mitchell, Leila Wehbe
bioRxiv 2020.09.28.316935; doi: https://doi.org/10.1101/2020.09.28.316935

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