Statistical learning of tone sequences by human infants and adults

Saffran, Jenny R.; Johnson, Elizabeth; Aslin, Richard Ν.; Newport, Elissa L.

doi:10.1016/s0010-0277(98)00075-4

Cited by 1,138 publications

(1,050 citation statements)

References 42 publications

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“…Because tone transitions between words were more variable than within words, the amplitudes for the final tone within words were significantly lower than those for the initial tone, after learning word borders (i.e., word segmentation). However, word segmentation is not sufficient to account for all levels of the language learning process, such as the acquisition of phrase structures involved in grammatical categories (Saffran et al, 1999;Hauser et al, 2002). In the present study, the time course of learning both word orders and word borders was investigated by embedding hierarchical statistical rules in the vowel sequence.…”

Section: Event-related Responses As An Index Of Statistical Learningmentioning

confidence: 98%

“…This notion may, on the other hand, raise the question of whether adults retain the ability for statistical learning even after they have shifted to domain-specific native-language perception such as syntax and words. Regarding this question, the previous studies demonstrated that adults who had acquired native language also performed statistical learning when they tried to learn an unknown language (Saffran et al, 1999;Gómez, 2002;Peña et al, 2002;Daikoku et al, 2015): adults can extract statistical regularities such as transitional probabilities included in speech sequences and can recognize both word boundaries and word order in speech sequences (i.e., word segmentation and syntax, respectively) (Frost and Monaghan, 2016). However, previous studies have suggested that learners had difficulty in extracting grammatical-like regularity based on non-adjacent dependency included in speech sequences, and the discovery of the grammatical system appears to require a different type of computation as well as transitional probability (Gomez, 2002;Peña et al, 2002).…”

Section: Statistical Learning In Language Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

2017

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Section: Event-related Responses As An Index Of Statistical Learningmentioning

confidence: 98%

Section: Statistical Learning In Language Acquisitionmentioning

confidence: 99%

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

2017

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“…Although closely related to statistical learning (Perruchet and Pacton 2006), there are important differences (artificial grammars usually contain explicit rules, while statistical learning is based on transition probabilities), and this could potentially influence transfer. A recent statistical learning study by Vouloumanos et al (2012) used the Saffran paradigm (Saffran et al 1996(Saffran et al , 1999 in which a stimulus stream can be segmented into distinct units based on the transition statistics, and the task involves abstraction and identification of those units. Transfer to new stimuli with acoustically different properties (but still within the auditory domain) was seen in this study, but performance was weaker than for the original stimulus set.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…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: 98%

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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“…One important discovery using this technique has come from the work of Saffran and colleagues (2)(3)(4)(5), who have examined the powerful role that statistical learning-the detection of consistent patterns of sounds-plays in infant word segmentation. Syllables that are part of the same word tend to follow one another predictably, whereas syllables that span word boundaries do not.…”

Section: Discovering the Units Of Languagementioning

confidence: 99%

The acquisition of language by children

Saffran

Senghas

Trueswell

2001

Proc. Natl. Acad. Sci. U.S.A.

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I magine that you are faced with the following challenge. You must discover the internal structure of a system that contains tens of thousands of units, all generated from a small set of materials. These units, in turn, can be assembled into an infinite number of combinations. Although only a subset of those combinations is correct, the subset itself is for all practical purposes infinite. Somehow you must converge on the structure of this system to use it to communicate. And you are a very young child.This system is human language. The units are words, the materials are the small set of sounds from which they are constructed, and the combinations are the sentences into which they can be assembled. Given the complexity of this system, it seems improbable that mere children could discover its underlying structure and use it to communicate. Yet most do so with eagerness and ease, all within the first few years of life.Below we describe three recent lines of research that examine language learning, comprehension, and genesis by children. We begin by asking how infants break into the system, finding the words within the acoustic stream that serves as input to language learning. We then consider how children acquire the ability to rapidly combine linguistic elements to determine the relationships between these elements. Finally, we examine how children impose grammatical structure onto their perceived input, even to the extent of creating a new language when none is available. These investigations provide insight into the ways in which children extract, manipulate, and create the complex structures that exist within natural languages. Discovering the Units of LanguageBefore infants can begin to map words onto objects in the world, they must determine which sound sequences are words. To do so, infants must uncover at least some of the units that belong to their native language from a largely continuous stream of sounds in which words are seldom surrounded by pauses. Despite the difficulty of this reverse-engineering problem, infants successfully segment words from fluent speech from Ϸ7 months of age.How do infants learn the units of their native language so rapidly? One fruitful approach to answering this question has been to present infants with miniature artificial languages that embody specific aspects of natural language structure. Once an infant has been familiarized with a sample of this language, a new sample, or a sample from a different language, is presented to the infant. Subtle measures of surprise (e.g., duration of looking toward the new sounds) are then used to assess whether the infant perceives the new sample as more of the same, or something different. In this fashion, we can ask what the infant extracted from the artificial language, which can lead to insights regarding the learning mechanisms underlying the earliest stages of language acquisition (1).One important discovery using this technique has come from the work of Saffran and colleagues (2-5), who have examined the powerful role that statisti...

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Statistical learning of tone sequences by human infants and adults

Cited by 1,138 publications

References 42 publications

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

Cross-modal transfer of statistical information benefits from sleep

The acquisition of language by children

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