2018
DOI: 10.1016/j.jml.2017.08.004
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Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling

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Cited by 72 publications
(67 citation statements)
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References 77 publications
(160 reference statements)
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“…If an animate noun is searched for at the verb, greater difficulty will be experienced in (1a) compared to (1b), because the animacy feature matches two different nouns in (1a), making the nouns difficult to distinguish. This proposal can be made mathematically precise in computationally implemented models [17,18,19], and using these models quantitative predictions can derived that can then be tested against data. This review covers recent developments relating to two computationally implemented models of retrieval processes which aim to explain interference effects: the activation model [17], and the direct-access model [3,18,20].…”
mentioning
confidence: 99%
“…If an animate noun is searched for at the verb, greater difficulty will be experienced in (1a) compared to (1b), because the animacy feature matches two different nouns in (1a), making the nouns difficult to distinguish. This proposal can be made mathematically precise in computationally implemented models [17,18,19], and using these models quantitative predictions can derived that can then be tested against data. This review covers recent developments relating to two computationally implemented models of retrieval processes which aim to explain interference effects: the activation model [17], and the direct-access model [3,18,20].…”
mentioning
confidence: 99%
“…Informally, in an accuracy value there is less information encoded than in, for example, reading or listening times. In future work, we aim to implement an approach modeling both accuracies and listening times (Nicenboim & Vasishth, ). Also, counting each parsing failure as “wrong” might yield overly conservative accuracy values for the model; this can be addressed by assigning a random component into the calculation.…”
Section: Discussionmentioning
confidence: 99%
“…Computational modeling can help evaluate these different proposals quantitatively. Specifically, the cue‐based retrieval account of Lewis and Vasishth (), which was developed within the ACT‐R framework (Anderson et al., ), is a computationally implemented model of unimpaired sentence comprehension that has been used to model a broad array of empirical phenomena in sentence processing relating to similarity‐based interference effects (Engelmann, Jäger, & Vasishth, ; Jäger, Engelmann, & Vasishth, ; Nicenboim & Vasishth, ) and the interaction between oculomotor control and sentence comprehension (Engelmann, Vasishth, Engbert, & Kliegl, )…”
Section: Introductionmentioning
confidence: 99%
“…Second, Bayesian procedures allow us to fit virtually any kind of distribution in a straightforward way. In the past, we have fit hierarchical mixture models (Nicenboim & Vasishth, 2018;Vasishth, Nicenboim, Chopin, & Ryder, 2017) and hierarchical measurement error models (Nicenboim, Roettger, & Vasishth, 2017;Vasishth, Beckman, Nicenboim, Li, & Kong, 2017). In this paper, we fit shifted lognormal mixed models, which lie outside the class of generalized linear models.…”
Section: Advantages Of Bayesian Modelingmentioning
confidence: 99%