PIPS: A Parallel Planning Model of Sentence Production

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.

Conceptual Number in Bilingual Agreement Computation: Evidence from German Pseudo-Partitives

“…This is under the assumption that grammatical computations are cognitively more taxing for bilingual than for monolingual speakers and that thus cognitive resource limitations may affect bilinguals to a greater extent than monolinguals. Such cognitive resource limitations have been proposed as a potential reason for differences between bilingual and monolingual processing in a range of grammatical phenomena, including subject-verb agreement (e.g., Brehm et al 2022;Cunnings 2017;Foote 2011;Hopp 2006Hopp , 2010Keating 2009;Lehtonen et al 2023;McDonald 2006;McDonald and Roussel 2010;Sagarra and Herschensohn 2010), and for findings suggesting a greater dependence of agreement processing on domain-general abilities in bilinguals as compared to monolinguals (e.g., Gangopadhyay et al 2016;Reifegerste et al 2020;Wood et al 2021). If such effects are also at play here, bilingual speakers-who were shown by Jessen et al (2021) to be aware that German pseudo-partitives allow for optionality in number agreement-might show larger effects of conceptual number compared to L1 speakers.…”

“…If this is true, then circumstances of limited resource availability could provide fertile grounds for the emergence of notionalnumber effects. Agreement computation may present such a situation for bilingual speakers, as it is argued to be more cognitively taxing for bilingual speakers than for speakers with only one native language (Brehm et al 2022;Cunnings 2017;Foote 2011;Hopp 2006Hopp , 2010Keating 2009;Lehtonen et al 2023;McDonald 2006;McDonald and Roussel 2010;Sagarra and Herschensohn 2010). A number of empirical studies have reported greater correlations between domain-general cognitive abilities (e.g., nonverbal working memory or interference control) and performance in tasks tapping morphosyntactic knowledge and processing, including agreement computation, for bilinguals than for monolinguals (e.g., Gangopadhyay et al 2016;Reifegerste et al 2020;Wood et al 2021).…”

Modelación de errores de concordancia plural en español LE mediante sistemas dinámicos

“…Debido a la acción del "ruido", el sistema puede ser empujado hacia atractores de estructuras mal formadas, o sea, ser inducido a error. En dicha dirección están los trabajos de Smith y Tabor (2018), Smith et al (2021) y el paradigma de Gradient Symbolic Computation (Smolensky et al, 2014;Cho et al, 2017Cho et al, , 2018Cho et al, , 2020Brehm et al, 2022). Por otra parte, se encuentran los modelos que aplican teoría de los juegos evolutiva al lenguaje.…”