The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions

Lavi-Rotbain, Ori; Arnon, Inbal

doi:10.31234/osf.io/xwgpk

Cited by 5 publications

(6 citation statements)

References 25 publications

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“…This would justify the pattern observed in the second part of the task for the high-TP “words.” Alternatively, it can also be argued that, unlike adults, children might have extracted the statistical regularities embedded in the input by using a simpler strategy, i.e., computing syllable frequency (i.e., the number of times a given syllable appeared in the speech stream) instead of the probability of one syllable to be followed by another syllable in the stream (i.e., TPs). This interpretation is supported by recent findings, suggesting that children learn better in unbalanced than balanced distributions (i.e., in Zipf distributions), as it occurs in natural languages ( Lavi-Rotbain and Arnon, 2019 , 2020 , 2021 ). Due to cognitive limitations, the children’s immature brain might simply rely on the use of a more “economic” strategy, which may even have facilitated the learning of lower frequency elements later on ( Bortfeld et al, 2005 ; Palmer et al, 2019 ; Lavi-Rotbain and Arnon, 2021 ; Soares et al, 2021a ).…”

Section: Discussionsupporting

confidence: 77%

Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

Soares

Gutiérrez-Domínguez

Lages

et al. 2022

Front. Hum. Neurosci.

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From an early age, exposure to a spoken language has allowed us to implicitly capture the structure underlying the succession of speech sounds in that language and to segment it into meaningful units (words). Statistical learning (SL), the ability to pick up patterns in the sensory environment without intention or reinforcement, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language, including the discovery of word boundaries in the continuous acoustic stream. Although extensive evidence has been gathered from artificial languages experiments showing that children and adults are able to track the regularities embedded in the auditory input, as the probability of one syllable to follow another syllable in the speech stream, the developmental trajectory of this ability remains controversial. In this work, we have collected Event-Related Potentials (ERPs) while 5-year-old children and young adults (university students) were exposed to a speech stream made of the repetition of eight three-syllable nonsense words presenting different levels of predictability (high vs. low) to mimic closely what occurs in natural languages and to get new insights into the changes that the mechanisms underlying auditory statistical learning (aSL) might undergo through the development. The participants performed the aSL task first under implicit and, subsequently, under explicit conditions to further analyze if children take advantage of previous knowledge of the to-be-learned regularities to enhance SL, as observed with the adult participants. These findings would also contribute to extend our knowledge of the mechanisms available to assist SL at each developmental stage. Although behavioral signs of learning, even under explicit conditions, were only observed for the adult participants, ERP data showed evidence of online segmentation in the brain in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural data suggests, however, that adults and children rely on different mechanisms to assist the extraction of word-like units from the continuous speech stream, hence supporting the view that SL with auditory linguistic materials changes through development.

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Section: Discussionsupporting

confidence: 77%

Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

Soares

Gutiérrez-Domínguez

Lages

et al. 2022

Front. Hum. Neurosci.

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“…Even though extracting statistics from large-scale corpus resources representing authentic language use is computationally intense, the inclusion of statistics obtained from such corpora in constructing the stimulus material to be used in language processing tasks is of importance for one simple reason: It more adequately represents the complexities of the real-world distributional statistics inherent in natural languages-thereby better reflecting the actual types of input humans are exposed to-and going beyond the simple uniform distributions traditionally used in artificial language learning paradigms (for recent exceptions, see, Lavi-Rotbain & Arnon, 2020;Li et al, 2020;Snell & Theeuwes, 2020). Our approach also has the potential to link statistical learning research more tightly with that of a related field of research on language adaptation (for an overview, see, Chang et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Tuning to Real-Life Language Statistics: Online Processing of Multiword Sequences in Native and Non-Native Speakers Across Language Registers

Kerz¹,

Wiechmann²,

Frinsel³

et al. 2020

Preprint

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A large body of research over the past two decades has demonstrated that children and adults are equipped with statistical learning mechanisms that facilitate their language processing and boost their acquisition. However, this research has been conducted primarily using artificial languages that are highly simplified relative to real language input. Here, we aimed to determine to what extent adult native and non-native speakers show sensitivity to real-life language statistics obtained from large-scale analyses of authentic language use. Through a within-subject design, we conducted a series of behavioral experiments geared towards assessing the sensitivity to two types of distributional statistics (frequency and entropy) during online processing of multiword sequences across four registers of English (spoken, fiction, news and academic language). Our results show that both native and non-native speakers are able to `tune to' multiple distributional statistics inherent in different types of real language input.

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“…This nding suggests that, unlike adults, children seem to guide the extraction of the statistical regularities embedded in the input by computing the frequency with which each syllable occurred in the stream rather than syllables' TPs. Indeed, the fact that high-TP 'words' entailed a higher number of syllables, that occurred three-times less often in the stream than low-TP 'words', might have make the immature cognitive system to use a more 'economic' strategy (syllable frequency instead of syllables' TPs) to predict the upcoming events, and to use that knowledge to facilitate the learning of lower frequency elements later on [see 36, 51,52 ]. The fact that during the rst half of the aSL task presented under explicit conditions children showed larger N400 amplitudes for low-TP 'words', and during the second half of the same task larger N400 amplitudes for high-TP 'words' seem to be in accordance with this rationale, although future research should further explore this issue by manipulating the use of different statistics (conditional vs. distributive) in the input.…”

Section: Discussionmentioning

confidence: 99%

Age-Related Differences in Auditory Statistical Learning of Complex Speech Streams: Evidence From ERPs

Soares

Gutiérrez-Domínguez

Lages

et al. 2021

Preprint

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Statistical learning (SL), the ability to pick up regularities in the sensory environment, is a fundamental skill that allows us to structure the world in a regular and predictable way. Although extensive evidence has been gathered from children and adults, the changes that SL might undergo throughout development remain contentious, particularly in the auditory modality (aSL) with linguistic materials. Here, we collected Event-Related Potentials (ERPs) while 5 years-old children and young adults (university students) were exposed to a complex speech stream in which eight three-syllable nonsense words - four with high (1.0) and four with low (.50) transitional probabilities were embedded to further examine how aSL works under less predictable conditions and to enhance age-related differences in the neural correlates of aSL. Moreover, to ascertain how previous knowledge of the to-be-learned regularities might also affect the results, participants performed the aSL task, firstly, under implicit and, subsequently, under explicit conditions. Although behavioral signs of aSL were observed only for adult participants, ERP data showed evidence of aSL in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural responses suggests, however, that adults and children rely on different mechanisms to assist aSL.

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The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions

Cited by 5 publications

References 25 publications

Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

Tuning to Real-Life Language Statistics: Online Processing of Multiword Sequences in Native and Non-Native Speakers Across Language Registers

Age-Related Differences in Auditory Statistical Learning of Complex Speech Streams: Evidence From ERPs

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