Testing the limits of statistical learning for word segmentation

Johnson, Elizabeth; Tyler, Michael D.

doi:10.1111/j.1467-7687.2009.00886.x

Cited by 131 publications

(157 citation statements)

References 38 publications

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“…A second reason for including this task was that the test for this measure is much more extensive (that is, there are more test items) than the test for the word segmentation tasks. If having reliable SL measures is (at least in part) a function of having an assessment of performance with a number of items, we expect that the grammar learning task should produce reasonably strong test-retest reliability (but see [63], for evidence that suggests that this may not be the case).…”

Section: Methodsmentioning

confidence: 99%

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

et al. 2016

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The ability to adapt to statistical structure (often referred to as "statistical learning") has been proposed to play a major role in the acquisition and use of natural languages. Several recent studies have explored the relationship between individual differences in statistical learning and language outcomes. These studies have produced mixed results, with some studies finding a significant relationship between statistical learning and language outcomes, and others finding weak or null results. Furthermore, the few studies that have used multiple measures of statistical learning have reported that they are not correlated (e.g., [1]). The current study assesses the reliability of various measures of auditory statistical segmentation, and their consistency over time. That is, do the generally low correlations observed between measures of statistical learning stem from task demands, the psychometric properties of the measures, or the fact that statistical learning may be a highly fragmented construct? Our results confirm previous reports that individual measures of statistical learning tend not to correlate with each other, and suggest that the somewhat weak reliability of the measures may be an important factor in the low correlations. Our data also suggest that aggregating performance across tasks may be an avenue for improving the reliability of the measures.

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Section: Methodsmentioning

confidence: 99%

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

et al. 2016

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“…The younger group would also not have had a vocabulary of any noteworthy size. The beginnings of successful word segmentation and recognition are seen at approximately 6 mo of age (27)(28)(29), but our younger-adopted group left Korea by age 5 mo at the latest; again, the evidence speaks against any serious role for word knowledge at that time.…”

Section: Discussionmentioning

confidence: 73%

“…However, recent studies have pushed the threshold of word recognition to an earlier time point: segmentation of running speech (27,28) and recognition of words referring to familiar people and concepts (29,30) have been demonstrated in infants of 6 mo or younger, and neural precursors of word recognition have been observed even at 3 mo (31). These findings cast doubt on any dependence of word recognition on phoneme repertoire possession given that, as has been repeatedly shown, a mature native phoneme inventory bringing reduced sensitivity to foreign contrasts is not in place by 6 mo.…”

Section: Significancementioning

confidence: 99%

Early phonology revealed by international adoptees' birth language retention

Choi

Broersma

Cutler

2017

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

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Until at least 6 mo of age, infants show good discrimination for familiar phonetic contrasts (i.e., those heard in the environmental language) and contrasts that are unfamiliar. Adult-like discrimination (significantly worse for nonnative than for native contrasts) appears only later, by 9-10 mo. This has been interpreted as indicating that infants have no knowledge of phonology until vocabulary development begins, after 6 mo of age. Recently, however, word recognition has been observed before age 6 mo, apparently decoupling the vocabulary and phonology acquisition processes. Here we show that phonological acquisition is also in progress before 6 mo of age. The evidence comes from retention of birth-language knowledge in international adoptees. In the largest ever such study, we recruited 29 adult Dutch speakers who had been adopted from Korea when young and had no conscious knowledge of Korean language at all. Half were adopted at age 3-5 mo (before nativespecific discrimination develops) and half at 17 mo or older (after word learning has begun). In a short intensive training program, we observe that adoptees (compared with 29 matched controls) more rapidly learn tripartite Korean consonant distinctions without counterparts in their later-acquired Dutch, suggesting that the adoptees retained phonological knowledge about the Korean distinction. The advantage is equivalent for the younger-adopted and the olderadopted groups, and both groups not only acquire the tripartite distinction for the trained consonants but also generalize it to untrained consonants. Although infants younger than 6 mo can still discriminate unfamiliar phonetic distinctions, this finding indicates that native-language phonological knowledge is nonetheless being acquired at that age.language acquisition | adoption | phonology | language memory T o talk to others, and to understand what others say, we need to know the phonological structure of the language we are using. Phonology embraces, for instance, the single-sound contrasts that distinguish one word from another, or the sequence constraints that apply to sounds; so if we are speaking English, we need to distinguish rye from lie, and if we hear [m] followed immediately by [b], we know that they belong to different syllables, as in somebody. Neither assumption holds for all languages. Phonological knowledge is highly language-specific.It is also abstract: not so much knowledge of the sounds and words in the language, but knowledge about those sounds and words, and the rules that govern them. Such knowledge is constantly called into play in adult talking and listening. When talkers must repeat others' utterances, they repeat the sounds that are said, but do so by using their own accent, not by imitating the accent in which the sounds are said (1). If talkers are exposed to novel phoneme sequence constraints in perception, they quickly apply them in production (2). In slips of the tongue, transposed elements accommodate to the new rather than to the originally intended phonetic context, su...

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“…As cognitive science continues to evolve, artificial intelligent approaches such as statistical learning and network science are becoming increasingly attractive and relevant to researchers in cognitive science and artificial intelligence. Recent work [1][2][3][4][5][6] demonstrates that statistical learning is one of the most deeply explored phenomena in the field of cognitive science. Noticeably, the increasing utilization of network science with statistical learning [7][8][9] makes network-based approaches a robust tool in cognitive science and computational linguistics [10,11].…”

Section: Introductionmentioning

confidence: 99%

iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

Chang

2017

Advances in Artificial Intelligence

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One of the main challenges in artificial intelligence or computational linguistics is understanding the meaning of a word or concept. We argue that the connotation of the term "understanding," or the meaning of the word "meaning," is merely a word mapping game due to unavoidable circular definitions. These circular definitions arise when an individual defines a concept, the concepts in its definition, and so on, eventually forming a personalized network of concepts, which we call an iWordNet. Such an iWordNet serves as an external representation of an individual's knowledge and state of mind at the time of the network construction. As a result, "understanding" and knowledge can be regarded as a calculable statistical property of iWordNet topology. We will discuss the construction and analysis of the iWordNet, as well as the proposed "Path of Understanding" in an iWordNet that characterizes an individual's understanding of a complex concept such as a written passage. In our pilot study of 20 subjects we used a regression model to demonstrate that the topological properties of an individual's iWordNet are related to his IQ score, a relationship that suggests iWordNets as a potential new methodology to studying cognitive science and artificial intelligence.

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Testing the limits of statistical learning for word segmentation

Cited by 131 publications

References 38 publications

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

Early phonology revealed by international adoptees' birth language retention

iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

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