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  PIPS: A parallel planning model of sentence production

Brehm, L., Cho, P. W., Smolensky, P., & Goldrick, M. A. (2022). PIPS: A parallel planning model of sentence production. Cognitive Science, 46(2): e13079. doi:10.1111/cogs.13079.

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© 2022 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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 Creators:
Brehm, Laurel1, Author           
Cho, Pyeong Whan2, Author
Smolensky, Paul3, 4, Author
Goldrick, Matthew A.5, Author
Affiliations:
1Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792545              
2University of Michigan, Ann Arbor, MI, USA, ou_persistent22              
3Johns Hopkins University, Baltimore, MD, USA, ou_persistent22              
4Microsoft Research AI, Redmond, WA, USA, ou_persistent22              
5Northwestern University, Evanston, IL, USA, ou_persistent22              

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 Abstract: Subject–verb agreement errors are common in sentence production. Many studies have used experimental paradigms targeting the production of subject–verb agreement from a sentence preamble (The key to the cabinets) and eliciting verb errors (… *were shiny). Through reanalysis of previous data (50 experiments; 102,369 observations), we show that this paradigm also results in many errors in preamble repetition, particularly of local noun number (The key to the *cabinet). We explore the mechanisms of both errors in parallelism in producing syntax (PIPS), a model in the Gradient Symbolic Computation framework. PIPS models sentence production using a continuous-state stochastic dynamical system that optimizes grammatical constraints (shaped by previous experience) over vector representations of symbolic structures. At intermediate stages in the computation, grammatical constraints allow multiple competing parses to be partially activated, resulting in stable but transient conjunctive blend states. In the context of the preamble completion task, memory constraints reduce the strength of the target structure, allowing for co-activation of non-target parses where the local noun controls the verb (notional agreement and locally agreeing relative clauses) and non-target parses that include structural constituents with contrasting number specifications (e.g., plural instead of singular local noun). Simulations of the preamble completion task reveal that these partially activated non-target parses, as well the need to balance accurate encoding of lexical and syntactic aspects of the prompt, result in errors. In other words: Because sentence processing is embedded in a processor with finite memory and prior experience with production, interference from non-target production plans causes errors.

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Language(s): eng - English
 Dates: 2022-02-052022
 Publication Status: Issued
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 Rev. Type: Peer
 Identifiers: DOI: 10.1111/cogs.13079
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Title: Cognitive Science
Source Genre: Journal
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Pages: - Volume / Issue: 46 (2) Sequence Number: e13079 Start / End Page: - Identifier: ISSN: 0364-0213
CoNE: https://pure.mpg.de/cone/journals/resource/954925523741