A word-based account of comprehension and production of Kinyarwanda nouns in the Discriminative Lexicon

Vijver, Ruben van de; Uwambayinema, Emmanuel

doi:10.1515/lingvan-2021-0160

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Error‐driven learning is a domain of general learning theory that has been applied to many topics in cognition (Hoppe et al., 2022) and has recently been applied to language (Baayen, Chuang, & Blevins, 2018; Baayen, Chuang, Shafaei‐Bajestan, & Blevins, 2019; Baayen, Hendrix, & Ramscar, 2013; Baayen, Milin, Đurđević, Hendrix, & Marelli, 2011; Baayen, Shaoul, Willits, & Ramscar, 2016a; Chuang et al., 2021; Denistia & Baayen, 2023; Nieder, Tomaschek, Cohrs, & van de Vijver, 2021; Nieder, Chuang, van de Vijver, & Baayen, 2023; van de Vijver & Uwambayinema, 2022; van de Vijver, Uwambayinema, & Chuang, 2024), and language learning (Divjak, Milin, Ez‐zizi, Józefowski, & Adam, 2021; Ellis, 2006; Harmon, Idemaru, & Kapatsinski, 2019; Nixon, 2020; Olejarczuk et al., 2018; Ramscar, Dye, & McCauley, 2013; Ramscar & Yarlett, 2007; Ramscar et al., 2010; Romain, Ez‐zizi, Milin, & Divjak, 2022).…”

Section: Introductionmentioning

confidence: 99%

One Cue's Loss Is Another Cue's Gain—Learning Morphophonology Through Unlearning

Mujezinović,

Kapatsinski,

van de Vijver

2024

Cognitive Science

Self Cite

View full text Add to dashboard Cite

A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co‐occurrence of cues and outcomes to suffice in learning these functions (Baer‐Henney, Kügler, & van de Vijver, 2015; Baer‐Henney & van de Vijver, 2012), error‐driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010). In error‐driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co‐occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account—as predicted by domain general error‐driven learning.

show abstract

Section: Introductionmentioning

confidence: 99%

One Cue's Loss Is Another Cue's Gain—Learning Morphophonology Through Unlearning

Mujezinović,

Kapatsinski,

van de Vijver

2024

Cognitive Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…We do so in two ways. First, we computationally model production and comprehension of Maltese verbs within the framework of the Discriminative Lexicon (Baayen et al, 2018;Baayen et al, 2019;Chuang et al, 2020;van de Vijver et al, 2023;van de Vijver & Uwambayinema, 2022), in the following shortened to DL. Second, we will use the measures derived from this modeling to predict the results of Ussishkin et al (2015).…”

mentioning

confidence: 99%

Emerging roots: Processing of Maltese verbs

Nieder,

van de Vijver,

Ussishkin

2023

Preprint

View full text Add to dashboard Cite

In Semitic languages, the consonantal root is considered an inherent property of the morphological system combining morphological functions and meaning. Psycholinguistic studies reinforce its importance for language processing. Yet, an unanswered question is how the meaning of the root is represented. This study sets out to explore the meaning incorporated in Maltese consonantal roots using a computational modeling procedure that is grounded in the framework of the Discriminative Lexicon. Our model is not only able to comprehend and produce Maltese verb forms but additionally, the model can predict the response latencies of a masked auditory priming experiment on Maltese verb forms based on distributional representations of meaning. We conclude that the meaning of roots emerges from sentential contexts in which word forms are used.

show abstract

German nominal number interpretation in an impaired mental lexicon

Plag,

Heitmeier,

Domahs

2024

View full text Add to dashboard Cite

There is an ongoing debate on how speakers and listeners process and interpret information in a morphological system that is very complex and not very transparent. A well-known test case is the German nominal number system. In this paper we employ discriminative learning (e.g., Ramscar & Yarlett, 2007; Baayen et al., 2011, 2019) to test whether discriminative learning networks can be used to better understand the processing of German number. We analyse behavioral data obtained from a patient with primary progressive aphasia (Domahs et al., 2017), and the unimpaired system. We test a model that implements the traditional cues borrowed from the schema approach (Köpcke, 1988, 1993; Köpcke et al., 2021), and compare it to a model that uses segmental and phonotactic information only. Our results for the unimpaired system demonstrate that a model based on only biphones as cues is better able to predict the number of a given word-form than a model using structural phonological cues. We also test whether a discriminative learning model can predict the number decisions by the aphasic patient. The results demonstrate that a biphone-based discriminative model trained on the patient’s responses is superior to a structure-based model in approximating the patient’s behavior.

show abstract

A word-based account of comprehension and production of Kinyarwanda nouns in the Discriminative Lexicon

Cited by 4 publications

References 29 publications

One Cue's Loss Is Another Cue's Gain—Learning Morphophonology Through Unlearning

One Cue's Loss Is Another Cue's Gain—Learning Morphophonology Through Unlearning

Emerging roots: Processing of Maltese verbs

German nominal number interpretation in an impaired mental lexicon

Contact Info

Product

Resources

About