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{{Short description|Branch of developmental psycholinguistics}}
{{linguistics}}
'''Statistical language acquisition''', a branch of [[developmental psychology
==Philosophy==
Fundamental to the study of statistical language acquisition is the centuries-old debate between [[rationalism]] (or its modern manifestation in the psycholinguistic community, [[psychological nativism
In modern times, this debate has largely surrounded
▲Fundamental to the study of statistical language acquisition is the centuries-old debate between [[rationalism]] (or its modern manifestation in the psycholinguistic community, [[psychological nativism | nativism]]) and [[empiricism]], with researchers in this field falling strongly in support of the latter category. Nativism is the position that humans are born with innate [[___domain specificity | ___domain-specific]] knowledge, especially inborn capacities for language learning. Ranging from seventeenth century rationalist philosophers such as [[Descartes]], [[Spinoza]], and [[Leibniz]] to contemporary philosophers such as [[Richard Montague]] and linguists such as [[Noam Chomsky]], nativists posit an innate learning mechanism with the specific function of language acquisition.<ref name = "philo">Russell, J. (2004). What is Language Development?: Rationalist, Empiricist, and Pragmatist Approaches to the Acquisition of Syntax. Oxford University Press.</ref>
Standing in stark contrast to this position is empiricism, the [[epistemology
▲In modern times, this debate has largely surrounded Chomsky’s support of a [[Universal Grammar]], properties that all natural languages must have, through the controversial postulation of a [[Language Acquisition Device]] (LAD), an instinctive mental ‘organ’ responsible for language learning which searches all possible language alternatives and chooses the parameters that best match the learner’s environmental linguistic input. Much of Chomky’s theory is founded on the [[poverty of the stimulus]] (POTS) argument, the assertion that a child’s linguistic data is so limited and corrupted that learning language from this data alone is impossible. As an example, many proponents of POTS claim that because children are never exposed to negative evidence, that is, information about what phrases are ungrammatical, the language structure they learn would not resemble that of correct speech without a language-specific learning mechanism.<ref>Chomsky, N. (1965). Aspects of the Theory of Syntax. Cambridge, MA: MIT Press.</ref>
Chomsky is very critical of this empirical theory of language acquisition. He has said, "It's true there's been a lot of work on trying to apply statistical models to various linguistic problems. I think there have been some successes, but a lot of failures." He claims the idea of using statistical methods to acquire language is simply a mimicry of the process, rather than a true understanding of how language is acquired.<ref>{{Cite web | url=http://norvig.com/chomsky.html | title=On Chomsky and the Two Cultures of Statistical Learning}}</ref>
▲Standing in stark contrast to this position is empiricism, the [[epistemology | epistemological]] theory that all knowledge comes from sensory experience. This school of thought often characterizes the nascent mind as a [[tabula rasa]], or blank slate, and can in many ways be associated with the nurture perspective of the "[[nature versus nurture | nature vs. nurture debate]]”. This viewpoint has a long historical tradition that parallels that of rationalism, beginning with [[17th century | seventeenth century]] empiricist philosophers such as [[John Locke | Locke]], [[Francis Bacon | Bacon]], [[Hobbes]], and, in the following century, [[David Hume | Hume]]. The basic tenet of empiricism is that information in the environment is structured enough that its patterns are both detectable and extractable by ___domain-general learning mechanisms.<ref name = "philo"/> In terms of [[language acquisition]], these patterns can be either linguistic or social in nature.
==Experimental
===Headturn Preference Procedure (HPP)===
One of the most used experimental [[paradigm
▲One of the most used experimental [[paradigm | paradigms]] in investigations of infants’ capacities for statistical language acquisition is the Headturn Preference Procedure (HPP), developed by [[Stanford University | Stanford]] psychologist [[Anne Fernald]] in 1985 to study infants’ preferences for prototypical [[baby talk | child-directed speech]] over normal adult speech.<ref>Fernald, A. (1985). Four-Month-Old Infants Prefer to Listen to Motherese ”. Infant Behavior and Development, 181-195.</ref> In the classic HPP paradigm, infants are allowed to freely turn their heads and are seated between two speakers with mounted lights. The light of either the right or left speaker then flashes as that speaker provides some type of audial or linguistic input stimulus to the infant. Reliable orientation to a given side is taken to be an indication of a preference for the input associated with that side’s speaker. This paradigm has since become increasingly important in the study of [[Speech perception#Infant speech perception | infant speech perception]], especially for input at levels higher than [[syllable]] chunks, though with some modifications, including using the listening times instead of the side preference as the relevant dependent measure.<ref name= "review">Swingley, D. (2009). Contributions of infant word learning to language development. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 364(1536), 3617-32. doi:10.1098/rstb.2009.0107</ref>
===Conditioned Headturn Procedure===
Similar to HPP, the Conditioned Headturn Procedure also makes use of an
===Anticipatory eye movement===
▲Similar to HPP, the Conditioned Headturn Procedure also makes use of an infant’s differential preference for a given side as an indication of a preference for, or more often a familiarity with, the input or speech associated with that side. Used in studies of [[prosody (linguistics)| prosodic]] boundary markers by Gout et. al (2004)<ref name = "review"/> and later by Werker in her classic studies of [[categorical perception]] of [[first language | native-language]] [[phoneme | phonemes]]<ref name = "werker">Werker, J. F., & Lalonde, C. E. (1988). Cross-Language Speech Perception : Initial Capabilities and Developmental Change. Developmental Psychology, 24(5), 672-683.</ref>, infants are [[classical conditioning | conditioned]] by some attractive image or display to look in one of two directions every time a certain input is heard, a whole word in Gout’s case and a single phonemic syllable in Werker’s. After the conditioning, new or more complex input is then presented to the infant, and their ability to detect the earlier target word or distinguish the input of the two trials is observed by whether they turn their head in expectation of the conditioned display or not.
While HPP and the Conditioned Headturn Procedure allow for observations of behavioral responses to stimuli and after the fact inferences about what the
===
[[
As such, artificial language experiments are typically conducted to explore what the relevant linguistic variables are, what sources of information infants are able to use and when, and how researchers can go about modeling the [[learning]] and acquisition process.<ref name = "review"/> [[Richard N. Aslin
▲While HPP and the Conditioned Headturn Procedure allow for observations of behavioral responses to stimuli and after the fact inferences about what the subject’s expectations must have been to motivate this behavior, the Anticipatory Eye Movement paradigm allows researchers to directly observe a subject’s expectations before the event occurs. By [[eye tracking | tracking]] subjects’ [[eye movement (sensory) | eye movements]] researchers have been able to investigate infant [[decision-making]] and the ways in which infants encode and act on [[probabilistic logic | probabilistic knowledge]] to make predictions about their environments.<ref>Davis, S. J., Newport, E. L., & Aslin, R. N. (2009). Probability-matching in 10-month-old infants. Methods.</ref> This paradigm also offers the advantage of comparing differences in eye movement behavior across a wider range of ages than others.
===Audio and audiovisual recordings===
Statistical learning has been shown to play a large role in language acquisition, but social interaction appears to be a necessary component of learning as well. In one study, infants presented with audio or audiovisual recordings of Mandarin speakers failed to distinguish the phonemes of the language.<ref name = "crackingcode">{{cite journal | last1 = Kuhl | first1 = Patricia K | year = 2004 | title = Early language acquisition: cracking the speech code | journal = Nature Reviews Neuroscience | volume = 5 | issue = 11| pages = 831–843 | doi = 10.1038/nrn1533 | pmid=15496861| s2cid = 205500033 }}</ref><ref name = "social learning">{{cite journal | last1 = Kuhl | first1 = Patricia K | year = 2007 | title = Is speech learning "gated" by the social brain | url = http://ilabs.washington.edu/kuhl/pdf/Kuhl_2007.pdf | journal = Developmental Science | volume = 10 | issue = 1| pages = 11–120 | doi = 10.1111/j.1467-7687.2007.00572.x | pmid = 17181708 }}</ref> This implies that simply hearing the sounds is not sufficient for language learning; social interaction cues the infant to take statistics. Particular interactions geared towards infants is known as "child-directed" language because it is more repetitive and associative, which makes it easier to learn. These "child directed" interactions could also be the reason why it is easier to learn a language as a child rather than an adult.
===Bilinguals===
▲[[artificial language | Artificial languages]], that is, small-scale languages that typically have an extremely limited [[vocabulary]] and simplified [[grammar]] rules, are a commonly used paradigm for [[psycholinguistics | psycholinguistic]] researchers. Artificial languages allow researchers to isolate variables of interest and wield a greater degree of control over the input the subject will receive. Unfortunately, the overly simplified nature of these languages and the absence of a number of phenomena common to all human natural languages such as [[rhythm]], [[pitch (music) | pitch]] changes, and sequential regularities raise questions of [[external validity]] for any findings obtained using this paradigm, even after attempts have been made to increase the [[complexity]] and richness of the languages used.<ref name = "soundtomeaning">Hay, J. F., Pelucchi, B., Estes, K. G., & Saffran, J. R. (2011). Linking sounds to meanings: Infant statistical learning in a natural language. Cognitive psychology, 63(2), 93-106. doi:10.1016/j.cogpsych.2011.06.002</ref>
Studies of bilingual infants, such as a study Bijeljac-Babic, et al., on French-learning infants, have offered insight to the role of prosody in language acquisition.<ref name = "bijeljac-babic">{{cite journal | last1 = Bibeljac-Babic | first1 = Ranka | last2 = Serres | first2 = Josette | last3 = Höhle | first3 = Barbara | last4 = Nazzi | first4 = Thierry | year = 2012 | title = Effect of Bilingualism on Lexical Stress Pattern Discrimination in French-Learning Infants | journal = PLOS ONE | volume = 7| issue = 2| page = e30843| doi = 10.1371/journal.pone.0030843 | pmid = 22363500 | pmc = 3281880 | bibcode = 2012PLoSO...730843B | doi-access = free }}</ref> The Bijeljac-Babic study found that language dominance influences "sensitivity to prosodic contrasts." Although this was not a study on statistical learning, its findings on prosodic pattern recognition might have implications for statistical learning.
It is possible that the kinds of language experience and knowledge gained through the statistical learning of the first language influences one's acquisition of a second language. Some research points to the possibility that the difficulty of learning a second language may be derived from the structural patterns and language cues that one has already picked up from his or her acquisition of first language. In that sense, the knowledge of and skills to process the first language from statistical acquisition may act as a complicating factor when one tries to learn a new language with different sentence structures, grammatical rules, and speech patterns.{{citation needed|date=February 2014}}
▲As such, artificial language experiments are typically conducted to explore what the relevant linguistic variables are, what sources of information infants are able to use and when, and how researchers can go about modeling the [[learning]] and acquisition process.<ref name = "review"/> [[Richard N. Aslin | Aslin]] and [[Elissa L. Newport | Newport]], for example, have used artificial languages to explore what features of linguistic input make certain [[pattern | patterns ]] salient and easily detectable by infants, allowing them to easily contrast the detection of syllable repetition with that of word-final syllables and make conclusions about the conditions under which either feature is recognized as important.<ref name = "general">Aslin, R. N., & Newport, E. L. (2012). Statistical Learning : From Acquiring Specific Items to Forming General Rules. Distribution. doi:10.1177/0963721412436806</ref>
==Important
===Phonetic
The first step in developing knowledge of a system as complex as natural language is learning to distinguish the important language-specific classes of sounds, called phonemes, that distinguish meaning between words. [[University of British Columbia
It is now commonly accepted that children use some form of perceptual [[distributional hypothesis
▲The first step in developing knowledge of a system as complex as natural language is learning to distinguish the important language-specific classes of sounds, called phonemes, that distinguish meaning between words. [[University of British Columbia | UBC]] psychologist Janet Werker, since her influential series of experiments in the 1980s, has been one of the most prominent figures in the effort to understand the process by which human babies develop these phonological distinctions. While adults who speak different languages are unable to distinguish meaningful sound differences in other languages that do not delineate different meanings in their own, babies are born with the ability to universally distinguish all speech sounds. Werker’s work has shown that while infants at six to eight months are still able to perceive the difference between certain [[Hindi]] and [[English language | English]] [[consonant | consonants]], they have completely lost this ability by 11 to 13 months.<ref name = "werker"/>
▲It is now commonly accepted that children use some form of perceptual [[distributional hypothesis | distributional learning]], by which categories are discovered by clumping similar instances of an input stimulus, to form phonetic categories early in life.<ref name = "review"/> Interestingly, developing children have been found to be effective judges of linguistic authority, screening the input they model their language on by shifting their [[attention]] less to speakers who mispronounce words.<ref name = "review" />
===Parsing===
[[Parsing]] is the process by which a continuous speech stream is segmented into its [[:wikt:discrete|discrete]] meaningful units, e.g. [[sentence (linguistics)
An important concept in understanding these results is that of [[markov chain
▲[[Parsing]] is the process by which a continuous speech stream is segmented into its [[discrete]] meaningful units, e.g. [[sentence (linguistics) | sentences]], [[word | words]], and syllables. [[Jenny Saffran | Saffran]] (1996) represents a singularly seminal study in this line of research. Infants were presented with two minutes of continuous speech of an artificial language from a computerized voice to remove any interference from [[extraneous variable | extraneous variables]] such as prosody or [[intonation (linguistics) | intonation]]. After this presentation, infants were able to distinguish words from nonwords, as measured by longer looking times in the second case.<ref name = "saffran">*Saffran, J. R., Aslin, R. N., & Newport, E. L. (2012). Statistical Learning by 8-Month-Old Infants. Advancement Of Science, 274(5294), 1926-1928.</ref>
The development of syllable-ordering biases is an important step along the way to full language development. The ability to categorize syllables and group together frequently [[co-occurrence
▲An important concept in understanding these results is that of [[markov chain | transitional probability]], the [[likelihood]] of an element, in this case a syllable, following or preceding another element. In this experiment, syllables that went together in words has a much higher transitional probability than did syllables at [[Word#Word boundaries | word boundaries]] that just happened to be adjacent.<ref name = "review"/><ref name = "soundtomeaning"/><ref name = "saffran"/> Incredibly, infants, after a short two minute presentation, were able to keep track of these [[statistics]] and recognize high [[probability]] words. Further research has since replicated these results with natural languages unfamiliar to infants, indicating that learning infants also keep track of the direction (forward or backward) of the transitional probabilities.<ref name = "soundtomeaning"/>
▲The development of syllable-ordering biases is an important step along the way to full language development. The ability to categorize syllables and group together frequently [[co-occurrence | co-occurring]] sequences may be critical in the development of a ''protolexicon'', a set of common language-specific word templates based on characteristic patterns in the words an infant hears. The development of this protolexicon may in turn allow for the recognition of new types of patterns, e.g. the high frequency of word-initially [[stress (linguistics) | stressed]] consonants in English, which would allow infants to further parse words by recognizing common prosodic phrasings as autonomous linguistic units, restarting the dynamic cycle of word and language learning.<ref name = "review"/>
The question of how novice language-users are capable of associating learned [[name
Researchers have shown that this problem is intimately linked with the ability to parse language, and that those words that are easy to segment due to their high transitional probabilities are also easier to [[conceptual metaphor
▲===Referent-Label Associations===
The developmentally earliest understanding of word to referent associations have been reported at six months old, with infants comprehending the words
▲The question of how novice language-users are capable of associating learned [[name | labels]] with the appropriate [[referent]], the person or object in the environment which the label names, has been at the heart of [[philosophy | philosophical]] considerations of [[philosophy of language | language]] and [[meaning (philosophy of language) | meaning]] from [[Plato]] to [[Willard Van Orman Quine | Quine]] to [[Douglas Hofstadter | Hofstadter]].<ref>Bornstein, M.H., & Lamb, M.E. (Eds.). (2011). Developmental Science: An Advanced Textbook. New York, NY: Psychology Press</ref> This problem, that of finding some solid relationship between word and object, of finding a word’s [[meaning (linguistics) | meaning]] without succumbing to an infinite recursion of dictionary look-up, is known as the [[symbol grounding | symbol grounding problem]].<ref>Harnad, S. (1990). The Symbol Grounding Problem. Physica D: Nonlinear Phenomena, 42, 335-346.</ref>
It is important to note that there is a distinction, often confounded in acquisition research, between mapping a label to a specific [[type-token distinction
▲Researchers have shown that this problem is intimately linked with the ability to parse language, and that those words that are easy to segment due to their high transitional probabilities are also easier to [[conceptual metaphor | map]] to an appropriate referent.<ref name = "soundtomeaning"/> This serves as further evidence of the developmental progression of language acquisition, with children requiring an understanding of the sound distributions of natural languages to form phonetic categories, parse words based on these categories, and then use these parses to map them to objects as labels.
The ability to appropriately generalize to whole classes of yet unseen words, coupled with the abilities to parse continuous speech and keep track of word-ordering regularities, may be the critical skills necessary to develop proficiency with and knowledge of syntax and grammar.<ref name = "review"/>▼
▲The developmentally earliest understanding of word to referent associations have been reported at six months old, with infants comprehending the words ‘[[mother | mommy]]’ and ‘[[father | daddy}}’ or their familial or cultural equivalents. Further studies have shown that infants quickly develop in this capacity and by seven months are capable of learning associations between moving images and [[nonsense]] words and syllables.<ref name = "review"/>
===Differences in autistic populations===
▲It is important to note that there is a distinction, often confounded in acquisition research, between mapping a label to a specific [[type-token distinction | instance]] or individual and mapping a label to an entire [[type-token distinction | class]] of objects. This latter process is sometimes referred to as [[generalization]] or rule learning. Research has shown that if input is encoded in terms of perceptually salient dimensions rather than specific details and if patterns in the input indicate that a number of objects are named interchangeably in the same context, a language learner will be much more likely to generalize that name to every instance with the relevant features. This tendency is heavily dependent on the consistency of context clues and the degree to which word contexts overlap in the input.<ref name = "general"/> These differences are furthermore linked to the well-known patterns of [[Organizing Knowledge Cognitively#Concepts | under]] and [[Organizing Knowledge Cognitively#Concepts | overgeneralization]] in infant [[vocabulary development | word learning]].
According to recent research, there is no neural evidence of statistical language learning in children with [[Autistic spectrum disorder|autism spectrum disorders]].{{cn|date=January 2025}} When exposed to a continuous stream of artificial speech, children without autism displayed less cortical activity in the [[Frontal cortex|dorsolateral frontal cortices]] (specifically the [[middle frontal gyrus]]) as cues for word boundaries increased. However activity in these networks remained unchanged in autistic children, regardless of the verbal cues provided. This evidence, highlighting the importance of proper Frontal Lobe brain function is in support of the "Executive Functions" Theory, used to explain some of the biologically related causes of Autistic language deficits. With impaired working memory, decision making, planning, and goal setting, which are vital functions of the Frontal Lobe, Autistic children are at loss when it comes to socializing and communication (Ozonoff, et al., 2004). Additionally, researchers have found that the level of communicative impairment in autistic children was inversely correlated with signal increases in these same regions during exposure to artificial languages. Based on this evidence, researchers have concluded that children with autism spectrum disorders don't have the neural architecture to identify word boundaries in continuous speech. Early word segmentation skills have been shown to predict later language development, which could explain why language delay is a hallmark feature of autism spectrum disorders.<ref>{{cite journal | last1 = Scott-Van Zeeland | first1 = A. A. | last2 = McNealy | first2 = K. | last3 = Wang | first3 = A. T. | last4 = Sigman | first4 = M. | last5 = Bookheimer | first5 = S. Y. | last6 = Dapretto | first6 = M. | year = 2010 | title = No neural evidence of statistical learning during exposure to artificial languages in children with autism spectrum disorders | journal = Biological Psychiatry | volume = 68 | issue = 4| pages = 345–351 | doi=10.1016/j.biopsych.2010.01.011| pmid = 20303070 | pmc = 3229830 }}</ref>
=== Statistical language learning across situations ===
▲The ability to appropriately generalize to whole classes of yet unseen words, coupled with the abilities to parse continuous speech and keep track of word-ordering regularities, may be the critical skills necessary to develop proficiency with and knowledge of syntax and grammar.<ref name = "review"/>
Language learning takes place in different contexts, with both the infant and the caregiver engaging in social interactions. Recent research have investigated how infants and adults use cross-situational statistics in order to learn about not only the meanings of words but also the constraints within a context. For example, Smith and his colleagues proposed that infants learn language by acquiring a bias to label objects to similar objects that come from categories that are well-defined. Important to this view is the idea that the constraints that assist learning of words are not independent of the input itself or the infant's experience. Rather, constraints come about as infants learn about the ways that the words are used and begin to pay attention to certain characteristics of objects that have been used in the past to represent the words.
Inductive learning problem can occur as words are oftentimes used in ambiguous situations in which there are more than one possible referents available. This can lead to confusion for the infants as they may not be able to distinguish which words should be extended to label objects being referenced to. Smith and Yu proposed that a way to make a distinction in such ambiguous situations is to track the word-referent pairings over multiple scenes. For instance, an infant who hears a word in the presence of object A and object B will be unsure of whether the word is the referent of object A or object B. However, if the infant then hears the label again in the presence of object B and object C, the infant can conclude that object B is the referent of the label because object B consistently pairs with the label across different situations.
==Computational Models==▼
[[
===Associative
[[artificial neural network
A precursor to this approach, and one of the first model types to account for the dimension of time in linguistic comprehension and production was [[Jeffrey Elman
▲[[artificial neural network | Associative neural network]] models of language acquisition are one of the oldest types of [[cognitive model]], using [[neural network | distributed representations]] and changes in the weights of the connections between the nodes that make up these representations to simulate learning in a manner reminiscent of the [[neuroplasticity | plasticity]]-based [[neuron | neuronal]] reorganization that forms the basis of human learning and [[memory]].<ref>Seidenberg, M. S., & Mcclelland, J. L. (1989). A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review, 96(4), 523-568.</ref> Associative models represent a break with [[GOFAI | classical cognitive]] models, characterized by discrete and [[physical symbol system | context-free symbols]], in favor of a [[dynamical system | dynamical systems]] approach to language better capable of handling [[time | temporal]] considerations.<ref name = "lexorg">Li, P. (2009). Lexical organization and competition in first and second languages: computational and neural mechanisms. Cognitive science, 33(4), 629-64. doi:10.1111/j.1551-6709.2009.01028.x</ref>
Early successes such as these paved the way for dynamical systems research into linguistic acquisition, answering many questions about early linguistic development but leaving many others unanswered, such as how these statistically acquired [[lexeme
▲A precursor to this approach, and one of the first model types to account for the dimension of time in linguistic comprehension and production was [[Jeffrey Elman | Elman]]’s [[Simple recurrent network#Elman networks and Jordan networks | simple recurrent network]] (SRN). By making use of a [[feedback]] network to represent the system’s past states, SRNs were able in a word-prediction task to [[cluster]] input into self-organized [[grammatical category | grammatical categories]] based solely on statistical co-occurrence patterns.<ref name = "lexorg"/><ref name = "srn">Elman, J. L. (1975). Language as a dynamical system. Most.</ref>
SOMs have been helpful to researchers in identifying and investigating the constraints and variables of interest in a number of acquisition processes, and in exploring the consequences of these findings on linguistic and cognitive theories. By identifying [[working memory]] as an important constraint both for language learners and for current computational models, researchers have been able to show that manipulation of this variable allows for [[bootstrapping (linguistics)
▲Early successes such as these paved the way for dynamical systems research into linguistic acquisition, answering many questions about early linguistic development but leaving many others unanswered, such as how these statistically acquired [[lexeme | lexemes]] are [[representation (psychology) | represented]].<ref name = "lexorg"/> Of particular importance in recent research has been the effort to understand the dynamic interaction of learning (e.g. language-based) and learner (e.g. speaker-based) variables in lexical organization and [[competition model | competition]] in [[multilingualism | bilinguals]].<ref name = "zins"/> In the ceaseless effort to move toward more psychologically realistic models, many researchers have turned to a subset of associative models, [[Self-Organizing Map | Self-Organizing Maps]] (SOMs), as established, cognitively plausible models of language development.<ref>Kohonen, T. (n.d.). The Self-Organizing Map.</ref><ref>Zhao, X., Li, P., & Kohonen, T. (2011). Contextual self-organizing map: software for constructing semantic representations. Behavior research methods, 43(1), 77-88. doi:10.3758/s13428-010-0042-z</ref>
▲SOMs have been helpful to researchers in identifying and investigating the constraints and variables of interest in a number of acquisition processes, and in exploring the consequences of these findings on linguistic and cognitive theories. By identifying [[working memory]] as an important constraint both for language learners and for current computational models, researchers have been able to show that manipulation of this variable allows for [[bootstrapping (linguistics) | syntactic bootstrapping]], drawing not just categorical but actual content meaning from words’ positional co-occurrence in sentences.<ref>Li, P., Burgess, C., & Lund, K. (2000). The Acquisition of Word Meaning through Global Lexical Co-occurrences. Young Children.</ref>
Some recent [[statistical model
Models that make use of these probabilistic methods have been able to merge the previously [[dichotomy
▲===Probabilistic Models===
While these results seem to be robust, studies concerning these
▲Some recent [[statistical model | models]] of language acquisition have centered around methods of [[Bayesian Inference]] to account for infants’ abilities to appropriately parse streams of speech and acquire word meanings. Models of this type rely heavily on the notion of [[conditional probability]] (the probability of A given B), in line with findings concerning infants’ use of transitional probabilities of words and syllables to learn words.<ref name = "saffran"/>
===C/V hypothesis===
▲Models that make use of these probabilistic methods have been able to merge the previously [[dichotomy | dichotomous]] language acquisition perspectives of [[social interactionist theory | social theories]] that emphasize the importance of learning speaker intentions and statistical and [[relational frame theory | associative theories]] that rely on cross-situational contexts into a single joint-inference problem. This approach has led to important results in explaining acquisition phenomena such as [[mutual exclusivity (psychology) | mutual exclusivity]], one-trial learning or [[fast mapping]], and the use of [[intention | social intentions]].<ref>Frank, M. C., Goodman, N. D., & Tenenbaum, J. B. (2009). Using Speakers ’ Referential Intentions to Model Early Cross-Situational Word Learning. Psychological Science, 1-8.</ref>
Along the lines of probabilistic frequencies, the C/V hypothesis basically states all language hearers use consonantal frequencies to distinguish between words (lexical distinctions) in continuous speech strings, in comparison to vowels. Vowels are more pertinent to rhythmic identification. Several follow-up studies revealed this finding, as they showed that vowels are processed independently of their local statistical distribution.<ref>{{Cite web |url=http://www.sissa.it/cns/Books/Linguistic%20Contstraints_in%20Rebuschat%20%26%20Williams.pdf |title=Archived copy |access-date=2022-02-17 |archive-date=2016-03-04 |archive-url=https://web.archive.org/web/20160304043734/http://www.sissa.it/cns/Books/Linguistic%20Contstraints_in%20Rebuschat%20%26%20Williams.pdf |url-status=dead }}</ref>
Other research has shown that the consonant-vowel ratio doesn't influence the sizes of lexicons when comparing distinct languages. In the case of languages with a higher consonant ratio, children may depend more on consonant neighbors than rhyme or vowel frequency.<ref>Lambertsen, Claus; [[Janna Oetting|Oetting, Janna]]; Barlow, Jessica. Journal of Speech, Language & Hearing Research. Oct2012, Vol. 55 Issue 5, p1265-1273.</ref>
=== Algorithms for language acquisition ===
▲While these results seem to be robust, studies concerning these models’ abilities to handle more complex situations such as multiple referent to single label mapping, multiple label to single referent mapping, and bilingual language acquisition in comparison to associative models’ successes in these areas have yet to be explored. Hope remains, though, that these model types may be merged to provide a comprehensive account of language acquisition.<ref>Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition : exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8), 357-364. Elsevier Ltd. doi:10.1016/j.tics.2010.05.004</ref>
Some models of language acquisition have been based on [[adaptive parsing]]<ref name="Lehman2012">{{cite book|author=Jill Fain Lehman|title=Adaptive Parsing: Self-Extending Natural Language Interfaces|url=https://books.google.com/books?id=tU_tBwAAQBAJ&q=%22language+acquisition%22|date=6 December 2012|publisher=Springer Science & Business Media|isbn=978-1-4615-3622-2}}</ref> and [[grammar induction]] algorithms.<ref>Chater, Nick, and Christopher D. Manning. "[http://www.lscp.net/persons/dupoux/teaching/QUINZAINE_RENTREE_CogMaster_2006-07/Bloc4_proba/pdf/Chater2006.pdf Probabilistic models of language processing and acquisition]." Trends in cognitive sciences 10.7 (2006): 335-344.</ref>
==References==
{{reflist}}
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{{DEFAULTSORT:Statistical Language Acquisition}}
[[Category:Language acquisition]]
[[Category:Applied linguistics]]
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