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Distributed Code for Semantic Relations Predicts Neural Similarity

View ORCID ProfileJeffrey N. Chiang, Yujia Peng, Hongjing Lu, Keith J. Holyoak, Martin M. Monti
doi: https://doi.org/10.1101/596726
Jeffrey N. Chiang
aDepartment of Psychology, University of California, Los Angeles, CA 90095-1563
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  • For correspondence: njchiang@g.ucla.edu
Yujia Peng
aDepartment of Psychology, University of California, Los Angeles, CA 90095-1563
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Hongjing Lu
aDepartment of Psychology, University of California, Los Angeles, CA 90095-1563
bDepartment of Statistics, University of California, Los Angeles, CA 90095-1563
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Keith J. Holyoak
aDepartment of Psychology, University of California, Los Angeles, CA 90095-1563
cBrain Research Institute, University of California, Los Angeles, CA 90095-1563
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Martin M. Monti
aDepartment of Psychology, University of California, Los Angeles, CA 90095-1563
cBrain Research Institute, University of California, Los Angeles, CA 90095-1563
dDepartment of Neurosurgery, University of California, Los Angeles, CA 90095-1563
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Abstract

The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.

Significance Relations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.

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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 June 12, 2019.
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Distributed Code for Semantic Relations Predicts Neural Similarity
Jeffrey N. Chiang, Yujia Peng, Hongjing Lu, Keith J. Holyoak, Martin M. Monti
bioRxiv 596726; doi: https://doi.org/10.1101/596726
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Distributed Code for Semantic Relations Predicts Neural Similarity
Jeffrey N. Chiang, Yujia Peng, Hongjing Lu, Keith J. Holyoak, Martin M. Monti
bioRxiv 596726; doi: https://doi.org/10.1101/596726

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