A computational evaluation of two models of retrieval processes in sentence processing in aphasia

Lissón, Paula; Pregla, Dorothea; Nicenboim, Bruno; Paape, Dario; Nederend, Mick L. van het; Burchert, Frank; Stadie, Nicole; Caplan, David; Vasishth, Shravan

doi:10.31234/osf.io/r7dn5

Cited by 21 publications

(8 citation statements)

References 46 publications

(83 reference statements)

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“…Informally, high retrieval activation threshold represents higher rigor: a structure is built only when the model is confident that it should be built. However, the best‐fitting value of noise seems implausible as the estimated value (0.5) was higher than the estimate obtained for participants with aphasia (0.45, Lissón et al., 2021; Mätzig, Vasishth, Engelmann, Caplan, & Burchert, 2018).…”

Section: Computational Simulation With the Lewis And Vasishth Modelmentioning

confidence: 79%

Semantic Attraction in Sentence Comprehension

Laurinavichyute

Malsburg

2022

Cognitive Science

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Agreement attraction is a cross-linguistic phenomenon where a verb occasionally agrees not with its subject, as required by grammar, but instead with an unrelated noun ('The key to the cabinets were . . . '). Despite the clear violation of grammatical rules, comprehenders often rate these sentences as acceptable. Contenders for explaining agreement attraction fall into two broad classes: Morphosyntactic accounts specifically designed to explain agreement attraction, and more general sentence processing models, such as the Lewis and Vasishth model ( 2005), which explain attraction as a consequence of how linguistic structure is stored and accessed in content-addressable memory. In the present research, we disambiguate between these two classes by testing a surprising prediction made by the Lewis and Vasishth model but not by the morphosyntactic accounts, namely that attraction should not be limited to morphosyntax, but that semantic features of unrelated nouns equally induce attraction. A recent study by Cunnings and Sturt (2018) provided initial evidence that this may be the case. Here, we report three single-trial experiments in English (N 1 = 1, 072, N 2 = 1, 426, and N 3 = 2, 454) that compared semantic and agreement attraction and tested whether and how the two interact. All three experiments showed strong semantically induced attraction effects closely mirroring agreement attraction effects. We complement these results with computational simulations confirming that the Lewis and Vasishth model can faithfully reproduce the observed results. In sum, our findings suggest that attraction is a more general phenomenon than is commonly believed, and therefore favor more general sentence processing models such as the Lewis and Vasishth model.

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Section: Computational Simulation With the Lewis And Vasishth Modelmentioning

confidence: 79%

Semantic Attraction in Sentence Comprehension

Laurinavichyute

Malsburg

2022

Cognitive Science

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“…One fruitful approach that we used in the present work is to establish a linking hypothesis between online measures such as reading times and offline measures such as acceptability judgments or answers to comprehension questions and to analyze both within a single latent‐process model (e.g., Ferreira & Yang, 2019; Paape et al., 2021; Nicenboim & Vasishth, 2018). Another promising avenue is to model reading times and reaction times with mixture distributions (e.g., Vasishth, Chopin, Ryder, & Nicenboim, 2017; Vasishth et al., 2017; Lissón et al., 2021), that is, to assume that the data points within conditions may be generated by different processes with different mean latencies and potentially different amounts of variability. By using these approaches, new insights can be gathered from existing datasets.…”

Section: Discussionmentioning

confidence: 99%

Estimating the True Cost of Garden Pathing: A Computational Model of Latent Cognitive Processes

Paape

Vasishth

2022

Cognitive Science

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What is the processing cost of being garden-pathed by a temporary syntactic ambiguity? We argue that comparing average reading times in garden-path versus non-garden-path sentences is not enough to answer this question. Trial-level contaminants such as inattention, the fact that garden pathing may occur non-deterministically in the ambiguous condition, and "triage" (rejecting the sentence without reanalysis; Fodor & Inoue, 2000) lead to systematic underestimates of the true cost of garden pathing. Furthermore, the "pure" garden-path effect due to encountering an unexpected word needs to be separated from the additional cost of syntactic reanalysis. To get more realistic estimates for the individual processing costs of garden pathing and syntactic reanalysis, we implement a novel computational model that includes trial-level contaminants as probabilistically occurring latent cognitive processes. The model shows a good predictive fit to existing reading time and judgment data. Furthermore, the latent-process approach captures differences between noun phrase/zero complement (NP/Z) gardenpath sentences and semantically biased reduced relative clause (RRC) garden-path sentences: The NP/Z garden path occurs nearly deterministically but can be mostly eliminated by adding a comma. By contrast, the RRC garden path occurs with a lower probability, but disambiguation via semantic plausibility is not always effective.

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“…The alternative is to openly accept the uncertainty inherent in data (Vasishth & Gelman 2021). My own experience has has been that it is usually possible to publish underpowered studies in mainstream journals without overstating the claims (e.g., Nicenboim et al 2018b;Vasishth et al 2018a;Jäger et al 2020;Nicenboim et al 2020;Avetisyan et al 2020;Lissón et al 2021).…”

Section: The Elephant In the Room: How To Express Uncertainty And Sti...mentioning

confidence: 99%

Some Right Ways to Analyze (Psycho)Linguistic Data

Vasishth

2023

Annu. Rev. Linguist.

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Much has been written on the abuse and misuse of statistical methods, including p values, statistical significance, and so forth. I present some of the best practices in statistics using a running example data analysis. Focusing primarily on frequentist and Bayesian linear mixed models, I illustrate some defensible ways in which statistical inference—specifically, hypothesis testing using Bayes factors versus estimation or uncertainty quantification—can be carried out. The key is to not overstate the evidence and to not expect too much from statistics. Along the way, I demonstrate some powerful ideas, including the use of simulation to understand the design properties of one's experiment before running it, visualization of data before carrying out a formal analysis, and simulation of data from the fitted model to understand the model's behavior. Expected final online publication date for the Annual Review of Linguistics, Volume 9 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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A computational evaluation of two models of retrieval processes in sentence processing in aphasia

Cited by 21 publications

References 46 publications

Semantic Attraction in Sentence Comprehension

Semantic Attraction in Sentence Comprehension

Estimating the True Cost of Garden Pathing: A Computational Model of Latent Cognitive Processes

Some Right Ways to Analyze (Psycho)Linguistic Data

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