No statistical learning advantage in children over adults: Evidence from behaviour and neural entrainment

Moreau, Christine; Joanisse, Marc F.; Mulgrew, Jerrica; Batterink, Laura

doi:10.1016/j.dcn.2022.101154

Cited by 9 publications

(16 citation statements)

References 56 publications

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“…In a series of studies [ 76 ], have demonstrated that this online perceptual transformation is a critical component of learning and can be reliably studied using neural oscillation data. Furthermore, this research indicates that both children and adults demonstrate robust neural entrainment to words and syllables, paralleled by comparable behavioural results on explicit SL tasks [ 79 ]. However, for the children, unlike the adults, the increase of entrainment to words was not accompanied by a decrease in entrainment to syllables, as shown by the inter-trial phase coherence (ITC) measure for syllables.…”

Section: Discussionmentioning

confidence: 81%

The association between statistical learning and language development during childhood: A scoping review

Abreu¹,

Postarnak²,

Vulchanov³

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 81%

The association between statistical learning and language development during childhood: A scoping review

Abreu¹,

Postarnak²,

Vulchanov³

et al. 2023

Heliyon

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“…I next asked whether a basic Hebbian learning model can explain periodic neural activity (e.g., Buiatti et al, 2009;Batterink & Paller, 2017;Fló et al, 2022;Kabdebon et al, 2015;Moreau et al, 2022;Moser et al, 2021), at least for the forgetting rates for which the network preferred words to part-words. In a first analysis, I recorded the total network activation after each syllable in a word had been presented.…”

Section: Resultsmentioning

confidence: 99%

“…Strong evidence in favor of this possibility comes from electrophysiology, where rhythmic activity has been observed in response to statistically structured sequences. In the time domain, different authors have observed amplitude peaks around the boundaries of statistically defined words (e.g., Abla et al, 2008;Cunillera et al, 2006;Kudo et al, 2011;Sanders et al, 2002;Teinonen et al, 2009); in the frequency domain, a frequency response with a period of the word duration emerges as participants learn the statistical structure of the speech stream (e.g., Buiatti et al, 2009;Batterink & Paller, 2017;Fló et al, 2022;Kabdebon et al, 2015;Moreau et al, 2022;Moser et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…If so, they should also show a neural rhythm with the same periodicity. While Buiatti et al (2009) detected such a rhythm only when words were separated by brief silences, later investigations found such rhythms in continuous sequences in adults (Batterink & Paller, 2017) (see also Moser et al, 2021 for a magneto-encephalography study), children (Moreau, Joanisse, Mulgrew, & Batterink, 2022), infants (Kabdebon, Pena, Buiatti, & Dehaene-Lambertz, 2015) and even newborns (Fló et al, 2022).…”

Section: Electrophysiological Correlates Of Statistical Learningmentioning

confidence: 98%

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Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Endress¹

2023

Preprint

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In many domains, learners need to extract recurring units from continuous sequences composed of discrete units. For example, fluent speech is perceived as a continuous signal (at least in unknown languages). Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism tracks how predictive syllables(or other items) are of one another. Syllables within the same word are more predictive of one another than syllables straddling word boundaries. Behaviorally, however, it is controversial whether such statistical learning abilities lead to memories of the underlying units beyond pairwise associations among syllables. The strongest evidence for this possibility comes from evoked electrophysiological responses time-locked to word boundaries (e.g., N400’s) and rhythmic neural activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences, the network activation is rhythmic with a periodicity of the word duration and an activation maximum on word-final syllables. This is because word-final syllables receive more excitatory input from earlier syllables with which they are associated than syllables that occur earlier in words (and are less predictable). Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. N400’s time-locked to word boundaries might thus index the reduced predictability of word-initial syllables rather than word-onsets. Learners might thus need to rely on other cues to learn the words of their native language.

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“…Since SL has been proposed to play a key role in language acquisition (Bates & Elman, 1996;Romberg & Saffran, 2010), children's advantage for language learning may be partially accounted for by age-related changes in SL ability. Unlike the clear developmental advantage of adults in visual SL that has been attribute to more mature domain-general cognitive capacity Raviv & Arnon, 2017;Fortenbaugh et al, 2015;see Forest et al, 2023 for an review), surprisingly, the few existing studies examining the developmental differences in SL in the auditory speech domain suggested a lack of or a weak agerelated effect (e.g., Saffran et al, 1997;Raviv & Arnon, 2017;Moreau et al, 2022;Ren et al, 2023). The fact that adults and older children, with more mature cognitive capacity and more prior language experiences, are not performing necessarily better than younger children in auditory linguistic SL, suggest certain underlying processing mechanisms are advantageous in children than adults.…”

Section: Introductionmentioning

confidence: 99%

Children Track Probabilistic Information in Speech Differently from Adults

Weng,

Schneider,

2024

Preprint

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Language learning is a sophisticated process as learners need to detect and extract rich regularities embedded in the continuous speech inputs. Children, compared to adults, appear to learn languages more effortlessly. Nevertheless, early studies in implicit statistical learning revealed little developmental differences between children and adults. Recent work has found the speed of statistical learning in adults is associated with their neural sensitivity to probabilistic information in speech. It is not well understood, however, whether children share similar or different underlying neural processes for probabilistic information compared to adults. Specifically, are children similar to faster or slower adult statistical learners, or neither of them? In the current study, children aged between 5 and 12 completed a passive auditory oddball task, where they listened to syllables at different local and global frequency of occurrence. We used two neurophysiological measures, auditory mismatch responses (MMR) and late discriminative negativity (LDN) to compare children’s sensitivity to distributional probabilities in speech with adults. We found that children were more sensitive to probabilistic information in speech inputs at both the local and the global level than both faster and slower adult statistical learners. Moreover, unlike adults who integrate probabilistic information across global and local hierarchies, children seem to process different levels of probabilistic information in parallel.

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No statistical learning advantage in children over adults: Evidence from behaviour and neural entrainment

Cited by 9 publications

References 56 publications

The association between statistical learning and language development during childhood: A scoping review

The association between statistical learning and language development during childhood: A scoping review

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Children Track Probabilistic Information in Speech Differently from Adults

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