mathematics

Statistical learning in language acquisition

Statistical learning in language acquisition is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Statistical learning in language acquisition rather than just read about it. In short: Statistical learning is the ability for humans and other animals to extract statistical regularities from the world around them to learn about the environment. Although statistical learning is now thought to be a generalized learning mechanism, the phenomenon was first identified in human infant language acquisition.

Key takeaways

Statistical learning in language acquisition belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Statistical learning in language acquisition to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Statistical learning in language acquisition from memory before moving on to harder problems.

Reference excerpt

Statistical learning is the ability for humans and other animals to extract statistical regularities from the world around them to learn about the environment. Although statistical learning is now thought to be a generalized learning mechanism, the phenomenon was first identified in human infant language acquisition. The earliest evidence for these statistical learning abilities comes from a study by Jenny Saffran, Richard Aslin, and Elissa Newport, in which 8-month-old infants were presented with nonsense streams of monotone speech. Each stream was composed of four three-syllable "pseudowords" that were repeated randomly. After exposure to the speech streams for two minutes, infants reacted differently to hearing "pseudowords" as opposed to "nonwords" from the speech stream, where nonwords were composed of the same syllables that the infants had been exposed to, but in a different order. This suggests that infants are able to learn statistical relationships between syllables even with very limited exposure to a language. That is, infants learn which syllables are always paired together and which ones only occur together relatively rarely, suggesting that they are parts of two different units. This method of learning is thought to be one way that children learn which groups of syllables form individual words. Since the initial discovery of the role of statistical learning in lexical acquisition, the same mechanism has been proposed for elements of phonological acquisition, and syntactical acquisition, as well as in non-linguistic domains. Further research has also indicated that statistical learning is likely a domain-general and even species-general learning mechanism, occurring for visual as well as auditory information, and in both primates and non-primates.

Lexical acquisition The role of statistical learning in language acquisition has been particularly well documented in the area of lexical acquisition. One important contribution to infants' understanding of segmenting words from a continuous stream of speech is their ability to recognize statistical regularities of the speech heard in their environments. Although many factors play an important role, this specific mechanism is powerful and can operate over a short time scale.

Original findings

It is a well-established finding that, unlike written language, spoken language does not have any clear boundaries between words; spoken language is a continuous stream of sound rather than individual words with silences between them. This lack of segmentation between linguistic units presents a problem for young children learning language, who must be able to pick out individual units from the continuous speech streams that they hear. One proposed method of how children are able to solve this problem is that they are attentive to the statistical regularities of the world around them. For example, in the phrase "pretty baby", children are more likely to hear the sounds pre and ty heard together during the entirety of the lexical input around them than they are to hear the sounds ty and ba together. In an artificial grammar learning study with adult participants, Saffran, Newport, and Aslin found that participants were able to locate word boundaries based only on transitional probabilities, suggesting that adults are capable of using statistical regularities in a language-learning task. This is a robust finding that has been widely replicated. To determine if young children have these same abilities Saffran Aslin and Newport exposed 8-month-old infants to an artificial grammar. The grammar was composed of four words, each composed of three nonsense syllables. During the experiment, infants heard a continuous speech stream of these words. The speech was presented in a monotone with no cues (such as pauses, intonation, etc.) to word boundaries other than the statistical probabilities. Within a word, the transitional probability of two syllable pairs was 1.0: in the word bidaku, for example, the probability of hearing the syllable da immediately after the syllable bi was 100%. Between words, however, the transitional probability of hearing a syllable pair was much lower: After any given word (e.g., bidaku) was presented, one of three words could follow (in this case, padoti, golabu, or tupiro), so the likelihood of hearing any given syllable after ku was only 33%. To determine if infants were picking up on the statistical information, each infant was presented with multiple presentations of either a word from the artificial grammar or a nonword made up of the same syllables but presented in a random order. Infants who were presented with nonwords during the test phase listened significantly longer to these words than infants who were presented with words from the artificial grammar, showing a novelty preference for these new nonwords. However, the implementation of the test could also be due to infants learning serial-order information and not to actually learning transitional probabilities between words. That is, at test, infants heard strings such as dapiku and tilado that were never presented during learning; they could simply have learned that the syllable ku never followed the syllable pi. To look more closely at this issue, Saffran Aslin and Newport conducted another study in which infants underwent the same training with the artificial grammar but then were presented with either words or part-words rather than words or nonwords. The part-words were syllable sequences composed of the last syllable from one word and the first two syllables from another (such as kupado). Because the part-words had been heard during the time when children were listening to the artificial grammar, preferential listening to these part-words would indicate that children were learning not only serial-order information, but also the statistical likelihood of hearing particular syllable sequences. Again, infants showed greater listening times to the novel (part-) words, indicating that 8-month-old infants were able to extract these statistical regularities from a continuous speech stream.

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Worked examples

Example 1 — a first encounter with Statistical learning in language acquisition

Start with the simplest possible case. Write down what Statistical learning in language acquisition claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Statistical learning in language acquisition before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Statistical learning in language acquisition ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Statistical learning in language acquisition

In research: Statistical learning in language acquisition appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Statistical learning in language acquisition in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Statistical learning in language acquisition is common in secondary-school and first-year university syllabi. It links to neighbouring topics Language acquisition, so understanding it makes those chapters shorter.
In everyday life: Look for Statistical learning in language acquisition outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Statistical learning in language acquisition in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Statistical learning in language acquisition means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Statistical learning in language acquisition out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Statistical learning in language acquisition in simple terms?

Why does Statistical learning in language acquisition matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Statistical learning in language acquisition?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Statistical learning in language acquisition.