Abstract

Sustained anterior negativities have been the focus of much neurolinguistics research concerned with the language-memory interface, but what neural computations do they actually reflect? During the comprehension of sentences with long-distance dependencies between elements (such as object wh-questions), prior event-related potential (ERP) work has demonstrated sustained anterior negativities (SANs) across the dependency region. SANs have been traditionally interpreted as an index of working memory (WM) resources responsible for storing the first element (e.g., wh-phrase) until the second element (e.g., verb) is encountered and the two can be integrated. However, it is also known that humans pursue top-down approaches in processing long-distance dependencies—predicting units and structures before actually encountering them. This study tests the hypothesis that SANs are a more general neural index of syntactic prediction. Across three experiments, we evaluated SANs in traditional wh-dependency contrasts, but also in sentences in which subordinating adverbials (e.g., although) trigger a prediction for a second clause, compared to temporal adverbials (e.g., today) that do not. We find no SAN associated with subordinating adverbials, contra the syntactic prediction hypothesis. More surprisingly, we observe SANs across matrix questions but not embedded questions. Since both involved identical long-distance dependencies, these results are also inconsistent with the traditional syntactic working memory account of the SAN. We suggest that a more general hypothesis that sustained neural activity supports working memory can be maintained, however, if the sustained anterior negativity reflects working memory encoding at the non-linguistic discourse representation level, rather than at the sentence level.

This content is only available as a PDF.

Author notes

Competing Interests: The authors have declared that no competing interests exist.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.