A Transparent Model of Syntactic and Semantic Cue-based Retrieval

Shisen Yue; John Hale

A Transparent Model of Syntactic and Semantic Cue-based Retrieval

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Abstract

Human comprehenders have greater difficulty forming pairwise grammatical dependencies in cases where the earlier word competes with a "distractor" to which it is similar. Cue-based retrieval theories (see e.g., Lewis et al., 2006) address this "interference" phenomenon with explicit quantifications of memory retrieval difficulty. We propose a computational model, consistent with Cue-based retrieval, that separately quantifies two different kinds of similarity. A linear combination of the two reproduces the graded interference pattern reported in Van Dyke (2007). This simple account offers a more straightforward mechanistic interpretation than Attention-based predictors from opaque Transformer based models.

Anthology ID:: 2026.scil-main.38
Volume:: Proceedings of the Society for Computation in Linguistics 2026
Month:: July
Year:: 2026
Address:: San Diego, CA
Editors:: Rob Voigt, Alex Warstadt, Naomi Feldman, Tal Linzen
Venues:: SCiL | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 411–422
Language:
URL:: https://aclanthology.org/2026.scil-main.38/
DOI:
Bibkey:
Cite (ACL):: Shisen Yue and John T. Hale. 2026. A Transparent Model of Syntactic and Semantic Cue-based Retrieval. In Proceedings of the Society for Computation in Linguistics 2026, pages 411–422, San Diego, CA. Association for Computational Linguistics.
Cite (Informal):: A Transparent Model of Syntactic and Semantic Cue-based Retrieval (Yue & Hale, SCiL 2026)
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PDF:: https://aclanthology.org/2026.scil-main.38.pdf

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@inproceedings{yue-hale-2026-transparent,
    title = "A Transparent Model of Syntactic and Semantic Cue-based Retrieval",
    author = "Yue, Shisen  and
      Hale, John T.",
    editor = "Voigt, Rob  and
      Warstadt, Alex  and
      Feldman, Naomi  and
      Linzen, Tal",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2026",
    month = jul,
    year = "2026",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.scil-main.38/",
    pages = "411--422",
    ISBN = "979-8-89176-412-5",
    abstract = "Human comprehenders have greater difficulty forming pairwise grammatical dependencies in cases where the earlier word competes with a ``distractor'' to which it is similar. Cue-based retrieval theories (see e.g., Lewis et al., 2006) address this ``interference'' phenomenon with explicit quantifications of memory retrieval difficulty. We propose a computational model, consistent with Cue-based retrieval, that separately quantifies two different kinds of similarity. A linear combination of the two reproduces the graded interference pattern reported in Van Dyke (2007). This simple account offers a more straightforward mechanistic interpretation than Attention-based predictors from opaque Transformer based models."
}

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    <abstract>Human comprehenders have greater difficulty forming pairwise grammatical dependencies in cases where the earlier word competes with a “distractor” to which it is similar. Cue-based retrieval theories (see e.g., Lewis et al., 2006) address this “interference” phenomenon with explicit quantifications of memory retrieval difficulty. We propose a computational model, consistent with Cue-based retrieval, that separately quantifies two different kinds of similarity. A linear combination of the two reproduces the graded interference pattern reported in Van Dyke (2007). This simple account offers a more straightforward mechanistic interpretation than Attention-based predictors from opaque Transformer based models.</abstract>
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%0 Conference Proceedings
%T A Transparent Model of Syntactic and Semantic Cue-based Retrieval
%A Yue, Shisen
%A Hale, John T.
%Y Voigt, Rob
%Y Warstadt, Alex
%Y Feldman, Naomi
%Y Linzen, Tal
%S Proceedings of the Society for Computation in Linguistics 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, CA
%@ 979-8-89176-412-5
%F yue-hale-2026-transparent
%X Human comprehenders have greater difficulty forming pairwise grammatical dependencies in cases where the earlier word competes with a “distractor” to which it is similar. Cue-based retrieval theories (see e.g., Lewis et al., 2006) address this “interference” phenomenon with explicit quantifications of memory retrieval difficulty. We propose a computational model, consistent with Cue-based retrieval, that separately quantifies two different kinds of similarity. A linear combination of the two reproduces the graded interference pattern reported in Van Dyke (2007). This simple account offers a more straightforward mechanistic interpretation than Attention-based predictors from opaque Transformer based models.
%U https://aclanthology.org/2026.scil-main.38/
%P 411-422

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Markdown (Informal)

[A Transparent Model of Syntactic and Semantic Cue-based Retrieval](https://aclanthology.org/2026.scil-main.38/) (Yue & Hale, SCiL 2026)

A Transparent Model of Syntactic and Semantic Cue-based Retrieval (Yue & Hale, SCiL 2026)

ACL

Shisen Yue and John T. Hale. 2026. A Transparent Model of Syntactic and Semantic Cue-based Retrieval. In Proceedings of the Society for Computation in Linguistics 2026, pages 411–422, San Diego, CA. Association for Computational Linguistics.