AI Article Synopsis

  • The study explores how comprehenders resolve filler-gap dependencies in incremental processing, focusing on the Norwegian language and examining why gap-filling is often paused in certain contexts.
  • Results from a self-paced reading study reveal that Norwegian participants show filled-gap effects in embedded questions, supporting grammar-based explanations over processing-based ones, indicating that such questions are not considered grammatical islands in Norwegian.
  • Additionally, the research investigates whether the concept of active filler-gap processing aligns with probabilistic ambiguity resolution and finds that while surprisal values can predict the location of filled-gap effects, they fail to accurately measure their magnitude, suggesting the need for further mechanisms or improved models of human expectation in processing.

Article Abstract

Filler-gap dependency resolution is often characterized as an active process. We probed the mechanisms that determine where and why comprehenders posit gaps during incremental processing using Norwegian as our test language. First, we investigated why active filler-gap dependency resolution is suspended inside island domains like embedded questions in some languages. Processing-based accounts hold that resource limitations prevent gap-filling in embedded questions across languages, while grammar-based accounts predict that active gap-filling is only blocked in languages where embedded questions are grammatical islands. In a self-paced reading study, we find that Norwegian participants exhibit filled-gap effects inside embedded questions, which are not islands in the language. The findings are consistent with grammar-based, but not processing, accounts. Second, we asked if active filler-gap processing can be understood as a special case of probabilistic ambiguity resolution within an expectation-based framework. To do so, we tested whether word-by-word surprisal values from a neural language model could predict the location and magnitude of filled-gap effects in our behavioral data. We find that surprisal accurately tracks the location of filled-gap effects but severely underestimates their magnitude. This suggests either that mechanisms above and beyond probabilistic ambiguity resolution are required to fully explain active gap-filling behavior or that surprisal values derived from long-short term memory are not good proxies for humans' incremental expectations during filler-gap resolution.

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Source
http://dx.doi.org/10.1111/cogs.13501DOI Listing

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