Comprehension and production of Kinyarwanda verbs in the Discriminative Lexicon

van de Vijver, Ruben; Uwambayinema, Emmanuel; Chuang, Yu-Ying

doi:10.1515/ling-2021-0164

Cited by 5 publications

(4 citation statements)

References 108 publications

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“…Therefore, it is important to note that the DLM's performance also depends on many modeling choices, such as the chosen form granularity, semantic vectors etc. Ideal modeling choices can often vary across languages—for instance, while for English, Dutch and German, trigrams are often the unit of choice (Heitmeier et al, 2021 , 2023b ), previous work has found that for Vietnamese, bigrams are preferable (Pham and Baayen, 2015 ), while for Maltese, Kinyarwanda and Korean, form representations based on syllables perform well (Nieder et al, 2023 ; van de Vijver et al, 2023 , Chuang et al, 2022 ; an in-depth discussion of the various considerations when modeling a language with the DLM can be found in Heitmeier et al, 2021 ). While the present study is limited to Dutch, Mandarin and English, future work should further verify the efficacy of FIL on morphologically more diverse languages.…”

Section: Discussionmentioning

confidence: 99%

Frequency effects in linear discriminative learning

Heitmeier,

Chuang,

Axen

et al. 2024

Front. Hum. Neurosci.

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Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM) models lexical processing with mappings between words' forms and their meanings. Comprehension and production are modeled via linear mappings between the two domains. So far, the mappings within the model can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modeling the theoretical endstate of learning (EL) where all words are learned optimally. In the present study we show how an efficient, yet frequency-informed mapping between form and meaning can be obtained (Frequency-informed learning; FIL). We find that FIL well approximates an incremental solution while being computationally much cheaper. FIL shows a relatively low type- and high token-accuracy, demonstrating that the model is able to process most word tokens encountered by speakers in daily life correctly. We use FIL to model reaction times in the Dutch Lexicon Project by means of a Gaussian Location Scale Model and find that FIL predicts well the S-shaped relationship between frequency and the mean of reaction times but underestimates the variance of reaction times for low frequency words. FIL is also better able to account for priming effects in an auditory lexical decision task in Mandarin Chinese, compared to EL. Finally, we used ordered data from CHILDES to compare mappings obtained with FIL and incremental learning. We show that the mappings are highly correlated, but that with FIL some nuances based on word ordering effects are lost. Our results show how frequency effects in a learning model can be simulated efficiently, and raise questions about how to best account for low-frequency words in cognitive models.

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Section: Discussionmentioning

confidence: 99%

Frequency effects in linear discriminative learning

Heitmeier,

Chuang,

Axen

et al. 2024

Front. Hum. Neurosci.

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“…Error-driven learning is a domain 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, and Blevins, 2018, Baayen, Hendrix, and Ramscar, 2013, Baayen et al, 2011, 2016a, Chuang et al, 2020, Denistia and Baayen, 2023, Nieder et al, 2021, van de Vijver and Uwambayinema, 2022, van de Vijver, Uwambayinema, and Chuang, 2024, and language learning (Divjak et al, 2021, Ellis, 2006, Harmon, Idemaru, and Kapatsinski, 2019, Nixon, 2020, Olejarczuk, Kapatsinski, and Baayen, 2018, Ramscar, Dye, and McCauley, 2013, Ramscar and Yarlett, 2007, Ramscar et al, 2010, Romain et al, 2022.…”

Section: Error-driven Learning In Languagementioning

confidence: 99%

One cue’s loss is another cue’s gain – Learning morphophonology through unlearning

Mujezinović,

Kapatsinski,

van de Vijver

2023

Preprint

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A word often expresses many different morphological functions. Which part of a form contributes to which part of the overall meaning is often unclear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume cooccurrence of cues and outcomes to suffice in learning these functions, recent studies suggest contingency rather than contiguity to be crucial (Nixon, 2020, Ramscar et al., 2010). Within error-driven learning theory, cues gain association strength if they predict a certain outcome, and 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 which 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 functions – plural and diminutive – co-occurred within the same word forms. In another block, a single cue to only one of these two functions was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue’s association to the incorrect function, and if this results in a higher relative weight to this function for the absent cue. Our results show that participants learned that the absent cue predicts the absent function better, if unlearning was possible. This effect was stronger, in case the present cue was more salient. This shows that unlearning takes place, even if no alternative cues to an absent outcome are provided, highlighting that learners take both positive and negative evidence into account – as predicted by domain general error driven learning.

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“…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

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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.

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Comprehension and production of Kinyarwanda verbs in the Discriminative Lexicon

Cited by 5 publications

References 108 publications

Frequency effects in linear discriminative learning

Frequency effects in linear discriminative learning

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

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