How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning

Heitmeier, Maria; Chuang, Yu-Ying; Baayen, R. Harald

doi:10.1016/j.cogpsych.2023.101598

Cited by 10 publications

(10 citation statements)

References 155 publications

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“…As fixed effects we entered Group (two levels: [−unlearning], [+unlearning]), Category (two levels: diminutive, plural) and Sequence-the trial order during the test as well as their interactions (pairwise and three way). We included Sequence because test trial order has been shown to affect learning (Heitmeier, Chuang, & Baayen, 2023). A change in accuracy in later trials may reflect either participants' change in confidence or a reevaluation of their cue-outcome mappings.…”

Section: Resultsmentioning

confidence: 99%

“…While semantic outcomes are often discrete (Kapatsinski, 2023b), recent studies have moved away from discrete representations. Nieder et al (2023), for example, use continuous cue-outcome representations to model Maltese inflection, while Heitmeier et al (2023) successfully modeled trial-by-trial effects of a lexical decision experiment in the same way. These studies use one-hot encoded vectors to represent phonology, and word embeddings to represent meaning, replacing the Rescorla-Wagner equations with the similar, but computationally more powerful Widrow-Hoff delta rule (Widrow & Hoff, 1960).…”

Section: Implications For Natural Language Learningmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Implications For Natural Language Learningmentioning

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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“…In first language acquisition, Nixon and Tomaschek (2020, 2021) present a computational model of early infants' learning of speech by using the incoming acoustic signal to predict upcoming acoustic signal. Apart from phonetic learning, error‐driven learning has also been found to play a role in word learning (Ramscar et al., 2013a; Ramscar et al., 2010; Ramscar et al., 2011), morphological learning (Hoppe et al., 2020; Ramscar & Yarlett, 2007; Ramscar et al., 2013b; Tomaschek et al., 2019), lexical decision (Heitmeier, Chuang, & Baayen, 2023), speech production (Tucker et al., 2019; Tomaschek et al., 2019; Tomaschek & Ramscar, 2022), and speech perception (Arnold et al., 2017; Shafaei‐Bajestan & Baayen, 2018). There is also evidence for trial‐by‐trial error‐driven learning in the brain (Lentz et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning

Tomaschek,

Ramscar,

Nixon

2024

Cognitive Science

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Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences—and the relations between the elements they comprise—are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for “chunking” of these letters into “words.” The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error‐driven learning. Experiments 1 and 2 showed that error‐driven learning was a better predictor of response latencies than either n‐gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error.

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“…The initial stage of speech production is modeled as involving a mapping in the opposite direction, starting with a high-dimensional semantic vector (known as embeddings in computational linguistics) and targeting a vector specifying which phone combinations drive articulation. The DLM has been successful in modeling a range of different morphological systems (e.g., Chuang et al, 2020 , 2022 ; Denistia and Baayen, 2021 ; Heitmeier et al, 2021 ; Nieder et al, 2023 ) as well as behavioral data such as acoustic durations (Schmitz et al, 2021 ; Stein and Plag, 2021 ; Chuang et al, 2022 ), (primed) lexical decision reaction times (Gahl and Baayen, 2023 ; Heitmeier et al, 2023b ), and data from patients with aphasia (Heitmeier and Baayen, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Recent modeling efforts with the DLM have been limited by the disadvantages of EL and WHL: they either had to opt for EL, which resulted in models that were not informed about word frequencies (e.g., Heitmeier et al, 2023b ), or for WHL, which limited the amount of data the models could be trained on (e.g., Chuang et al, 2021 ; Heitmeier et al, 2021 ). The present paper aims to solve this problem by introducing a new method for computing the mapping matrices that takes frequency of use into account but is computationally efficient by making use of a numerically efficiently solvable solution: “Frequency-informed learning” (FIL).…”

Section: Introductionmentioning

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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How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning

Cited by 10 publications

References 155 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

The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning

Frequency effects in linear discriminative learning

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