Statistical Learning in a Natural Language by 8‐Month‐Old Infants

Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.

doi:10.1111/j.1467-8624.2009.01290.x

Cited by 322 publications

(297 citation statements)

References 34 publications

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“…In recent years, such statistical learning has been demonstrated not only in the auditory domain using syllables (Saffran et al 1996;Pelucchi et al 2009) and tones (Saffran et al 1999;Durrant et al 2013), but also in the visual domain using abstract symbols (Fiser and Aslin 2001;Turk-Browne et al 2008). It has been shown in infants (Saffran et al 1996), adults (Saffran et al 1999), and even non-human primates (Hauser et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Cross-modal transfer of statistical information benefits from sleep

Durrant

Cairney

Lewis

2016

Cortex

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Extracting regularities from a sequence of events is essential for understanding our environment. However, there is no consensus regarding the extent to which such regularities can be generalised beyond the modality of learning. One reason for this could be the variation in consolidation intervals used in different paradigms, also including an opportunity to sleep. Using a novel statistical learning paradigm in which structured information is acquired in the auditory domain and tested in the visual domain over either 30min or 24hr consolidation intervals, we show that cross-modal transfer can occur, but this transfer is only seen in the 24hr group. Importantly, the extent of cross-modal transfer is predicted by the amount of SWS obtained. Additionally, cross-modal transfer is associated with the same pattern of decreasing MTL and increasing striatal involvement which has previously been observed to occur across 24 hours in unimodal statistical learning. We also observed enhanced functional connectivity after 24 hours in a network of areas which have been implicated in cross-modal integration including the precuneus and the middle occipital gyrus. Finally, functional connectivity between the striatum and the precuneus was also enhanced, and this strengthening was predicted by SWS. These results demonstrate that statistical learning can generalise to some extent beyond the modality of acquisition, and together with our previously published unimodal results, support the notion that statistical learning is both domain-general and domain-specific.

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Section: Introductionmentioning

confidence: 99%

Cross-modal transfer of statistical information benefits from sleep

Durrant

Cairney

Lewis

2016

Cortex

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“…People certainly employ statistical learning in procedural tasks, like learning artificial grammars (Pelucchi, Hay, & Saffran, 2009) and motor sequences (Daselaar, Rombouts, Veltman, Raaijmakers, & Jonker, 2003), regardless of whether the features are processed consciously or not. The feature approach is well established in memory research.…”

Section: R Emembering Music Is An Important Partmentioning

confidence: 99%

The Role of Features and Context in Recognition of Novel Melodies

Müllensiefen

Halpern

2012

Music Perception

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“…Many accounts of adjacent dependency learning posit that learners track statistical information about the transitions from one unit (e.g., syllable or word) to the next (Romberg & Saffran, 2013;Saffran, Aslin, & Newport, 1996). However, while most studies of adjacent dependencies have found success in a variety of learning scenarios (Pelucchi, Hay, & Saffran, 2009;Saffran et al, 1996; Thompson & Newport, 2007), the studies of non-adjacent dependencies to date have only found learning in limited situations, with some studies reporting success in learning and others reporting failure.(e.g., success: Gómez, 2002;Gómez & Maye, 2005;Romberg & Saffran, 2013; failure: Newport & Aslin, 2004; mixed: Peña, Bonatti, Nespor & Mehler, 2002; Santelmann & Jusczyk, 1998). In…”

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confidence: 99%

Top-down structure influences learning of nonadjacent dependencies in an artificial language.

Wang

Mintz

2017

Journal of Experimental Psychology: General

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Due to the hierarchical organization of natural languages, words that are syntactically related are not always linearly adjacent. For example, the subject and verb in the child always runs agree in person and number, although they are not adjacent in the sequences of words.Since such dependencies are indicative of abstact linguistc structure, it is of significant theoretical interest how these relationships are acquired by language learners. Most experiments that investigate non-adjacent dependency (NAD) learning have used artificial languages in which the to-be-learned dependencies are isolated, by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. We report the first demonstration to our knowledge of successful learning of embedded NADs, in which silences do not mark dependency boundaries. Subjects heard passages of English with a predictable structure, interspersed with passages of the artificial language. The English sentences were designed to induce boundaries in the artificial languages. In Experiment 1 & 3 the artificial NADs were contained within the induced boundaries and subjects learned them, whereas in Experiment 2 & 4, the NADs crossed the induced boundaries and subjects did not learn them. We take this as evidence that sentential structure was "carried over" from the English sentences and used to organize the artificial language. This approach provides several new insights into the basic mechanisms of NAD learning in particular and statistical learning in general.Keywords: Language acquisition; Bracketing; Grammatical Entrainment; Statistical learning; Non-adjacent dependency TOP-DOWN INFLUENCES ON DEPENDENCY LEARNING 3Due to the hierarchical organization of the syntax of natural languages, lexical items (and morphemes) that are syntactically related are not always linearly adjacent. Thus, to acquire the specifics of the hierarchical grammar, learners must be able to track both adjacent and nonadjacent dependencies in a linear sequence of words. For example, in the child runs, the third person singular subject, child, and the agreeing inflected verb, runs, are linearly adjacent; however, they are non-adjacent in the child always runs. For language learners just beginning to learn their language's syntax, evidence about which elements are related in grammatical processes could provide extremely useful information for further grammatical learning. This kind of information is not only beneficial for theories that view language acquisition as a largely domain-general learning problem, but also for theories in which learners have innate domainspecific constraints on representing and processing language. Therefore, an important question in language acquisition research is how learners detect adjacent and non-adjacent grammatical dependencies, as doing so could help learners understand how their language is structured.There has been considerable interest in investigating learning mechanisms that co...

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Statistical Learning in a Natural Language by 8‐Month‐Old Infants

Cited by 322 publications

References 34 publications

Cross-modal transfer of statistical information benefits from sleep

Cross-modal transfer of statistical information benefits from sleep

The Role of Features and Context in Recognition of Novel Melodies

Top-down structure influences learning of nonadjacent dependencies in an artificial language.

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