Statistical Language Learning Research Articles

Does bilingual experience influence statistical language learning?

Statistical language learning (SL) tasks measure different aspects of foreign language learning. Studies have used SL tasks to investigate whether bilingual experience confers advantages in acquiring additional languages through implicit processes. However, the results have been inconsistent, which may be related to bilingualism-related features (e.g., degree of dissimilarity between the specific language pair) and other variables such as specific processes that are targeted by the SL task. In the present study, we compared the performance of one Spanish monolingual and two bilingual (Spanish-Basque and Spanish-English) groups across three well-established SL tasks. Each task targeted a different aspect of foreign language learning; specifically, word segmentation, morphological rule generalization, and word-referent learning. In Experiment 1, we manipulated sub-lexical phonotactic patterns to vary the difficulty of three SL tasks, with the results showing no differences between the groups in word segmentation. In Experiment 2, we included non-adjacent dependencies to target affixal morphology rule learning, but again no group-related differences were found. In Experiment 3, we addressed word learning using an audio-visual SL task combining exclusive and multiple word-referent mappings, and found that bilinguals outperformed monolinguals, suggesting that bilingualism may exert influences on SL at the lexical level. This advantage might have been mediated by the high working memory demands required to perform the task. Summarizing, this study shows no evidence for a general bilingual advantage in SL, although bilinguals may outperform monolinguals under specific experimental conditions such as SL tasks that place high demands on working memory processes. In addition, the similar performance of Spanish-Basque and Spanish-English bilinguals across all three SL tasks suggests that the degree of dissimilarity between pairs of spoken languages does not modulate SL skills.

Statistical learning at a virtual cocktail party

Statistical learning – the ability to extract distributional regularities from input – is suggested to be key to language acquisition. Yet, evidence for the human capacity for statistical learning comes mainly from studies conducted in carefully controlled settings without auditory distraction. While such conditions permit careful examination of learning, they do not reflect the naturalistic language learning experience, which is replete with auditory distraction – including competing talkers. Here, we examine how statistical language learning proceeds in a virtual cocktail party environment, where the to-be-learned input is presented alongside a competing speech stream with its own distributional regularities. During exposure, participants in the Dual Talker group concurrently heard two novel languages, one produced by a female talker and one by a male talker, with each talker virtually positioned at opposite sides of the listener (left/right) using binaural acoustic manipulations. Selective attention was manipulated by instructing participants to attend to only one of the two talkers. At test, participants were asked to distinguish words from part-words for both the attended and the unattended languages. Results indicated that participants’ accuracy was significantly higher for trials from the attended vs. unattended language. Further, the performance of this Dual Talker group was no different compared to a control group who heard only one language from a single talker (Single Talker group). We thus conclude that statistical learning is modulated by selective attention, being relatively robust against the additional cognitive load provided by competing speech, emphasizing its efficiency in naturalistic language learning situations.

Effects of healthy aging and left hemisphere stroke on statistical language learning

ABSTRACT Spoken sentences are continuous streams of sound, without reliable acoustic cues to word boundaries. We have previously proposed that language learners identify words via an implicit statistical learning mechanism that computes transitional probabilities between syllables. Neuroimaging studies in healthy young adults associate this learning with left inferior frontal gyrus, left arcuate fasciculus, and bilateral striatum. Here, we test the effects of healthy aging and left hemisphere (LH) injury on statistical learning. Following 10-minute exposure to an artificial language, participants rated familiarity of Words, Part-words (sequences spanning word boundaries), and Non-words (unfamiliar sequences). Young controls (N = 14) showed robust learning, rating Words > Part-words > Non-words. Older controls (N = 28) showed this pattern to a weaker degree. Stroke survivors (N = 24) as a group showed no learning. A lesion comparison examining individual differences revealed that “non-learners” are more likely to have anterior lesions. Together, these findings demonstrate that word segmentation is sensitive to healthy aging and LH injury.

P80 Language learning in the adult brain: TMS-induced disruption of the left dorsolateral prefrontal cortex enhances neural entrainment indexes to statistical language learning

P80 Language learning in the adult brain: TMS-induced disruption of the left dorsolateral prefrontal cortex enhances neural entrainment indexes to statistical language learning

Statistical language learning in infancy.

Research to date suggests that infants exploit statistical regularities in linguistic input to identify and learn a range of linguistic structures, ranging from the sounds of language (e.g., native-language speech sounds, word boundaries in continuous speech) to aspects of grammatical structure (e.g., lexical categories like nouns and verbs, basic aspects of syntax). This article presents a brief review of the infant statistical language learning literature, and raises broader questions concerning why infants are sensitive to statistical regularities. In doing so, we consider the relationship between statistical learning, prediction, and uncertainty reduction in infancy.

Statistical Language Learning and Vagus Nerve Stimulation: A Pilot Study

Applying transcutaneous stimulation to the auricular branch of the vagus nerve (tVNS) has been shown to enhance associative learning in humans. The main goal of the project is to investigate the effect tVNS has on procedural learning, specifically implicit statistical language learning. The aim of the experiment reported in this paper was to determine which statistical language learning paradigms would be appropriate to use with tVNS. Since we would be looking at within-subject changes between two sessions (one session with, one session without stimulation), we tested the test-retest reliability of two statistical learning paradigms. We also tested the correlation between a explicit phonological memory task and the implicit statistical learning tasks to determine whether phonological memory was involved in the statistical learning tasks. Our results showed a high test-retest reliability for the word segmentation and adjacent dependencies statistical learning task. However, the second statistical learning task dealing with non-adjacent dependencies had low test-retest reliability, meaning it would not be appropriate for future studies incorporating tVNS. There was a high correlation between the phonological memory task and both statistical learning taks, indicating implicit statistical learning may recruit phonological memory.

Statistical language learning: computational, maturational, and linguistic constraints.

Our research on statistical language learning shows that infants, young children, and adults can compute, online and with remarkable speed, how consistently sounds co-occur, how frequently words occur in similar contexts, and the like, and can utilize these statistics to find candidate words in a speech stream, discover grammatical categories, and acquire simple syntactic structure in miniature languages. However, statistical learning is not merely learning the patterns presented in the input. When their input is inconsistent, children sharpen these statistics and produce a more systematic language than the one to which they are exposed. When input languages inconsistently violate tendencies that are widespread in human languages, learners shift these languages to be more aligned with language universals, and children do so much more than adults. These processes explain why children acquire language (and other patterns) more effectively than adults, and also may explain how systematic language structures emerge in communities where usages are varied and inconsistent. Most especially, they suggest that usage-based learning approaches must account for differences between adults and children in how usage properties are acquired, and must also account for substantial changes made by adult and child learners in how input usage properties are represented during learning.

Sleep confers a benefit for retention of statistical language learning in 6.5 month old infants

Infants show robust ability to track transitional probabilities within language and can use this information to extract words from continuous speech. The degree to which infants remember these words across a delay is unknown. Given well-established benefits of sleep on long-term memory retention in adults, we examine whether sleep similarly facilitates memory in 6.5month olds. Infants listened to an artificial language for 7minutes, followed by a period of sleep or wakefulness. After a time-matched delay for sleep and wakefulness dyads, we measured retention using the head-turn-preference procedure. Infants who slept retained memory for the extracted words that was prone to interference during the test. Infants who remained awake showed no retention. Within the nap group, retention correlated with three electrophysiological measures (1) absolute theta across the brain, (2) absolute alpha across the brain, and (3) greater fronto-central slow wave activity (SWA).

Implicit learning in aphasia: Evidence from serial reaction time and artificial grammar tasks

Implicit learning in aphasia: Evidence from serial reaction time and artificial grammar tasks

Isolated words enhance statistical language learning in infancy.

Infants are adept at tracking statistical regularities to identify word boundaries in pause-free speech. However, researchers have questioned the relevance of statistical learning mechanisms to language acquisition, since previous studies have used simplified artificial languages that ignore the variability of real language input. The experiments reported here embraced a key dimension of variability in infant-directed speech. English-learning infants (8-10 months) listened briefly to natural Italian speech that contained either fluent speech only or a combination of fluent speech and single-word utterances. Listening times revealed successful learning of the statistical properties of target words only when words appeared both in fluent speech and in isolation; brief exposure to fluent speech alone was not sufficient to facilitate detection of the words' statistical properties. This investigation suggests that statistical learning mechanisms actually benefit from variability in utterance length, and provides the first evidence that isolated words and longer utterances act in concert to support infant word segmentation.

Adult Language Learning After Minimal Exposure to an Unknown Natural Language

Despite the literature on the role of input in adult second‐language (L2) acquisition and on artificial and statistical language learning, surprisingly little is known about how adults break into a new language in the wild. This article reports on a series of behavioral and neuroimaging studies that examine what linguistic information adults can extract from naturalistic but controlled audiovisual input in an unknown and typologically distant L2 after minimal exposure (7–14 min) without instruction or training. We tested the stepwise development of segmental, phonotactic, and lexical knowledge in Dutch adults after minimal exposure to Mandarin Chinese and the role of item frequency, speech‐associated gestures, and word length at the earliest stages of learning. In an exploratory neural connectivity study we further examined the neural correlates of word recognition in a new language, identifying brain regions whose connectivity was related to performance both before and after learning. While emphasizing the complexity of the learning task, the results suggest that the adult learning mechanism is more powerful than is normally assumed when faced with small amounts of complex, continuous audiovisual language input.

A cognitive interactionist sentence parser with simple recurrent networks

Sentence parsing has a long history in the research fields of machine learning and natural language processing. The state-of-the-art technologies used to tackle this task include those based on statistical language learning. In the meantime, human sentence parsing has attracted massive research efforts for decades in the field of cognitive psychology. A range of behaviouristic experiments verify that the interactionist approach is a sensible and effective way to simulate the human parsing mechanism. This paper proposes a novel and effective sentence parser, the Cognitive Interactionist Parser (CIParser), which incorporates the cognitive interactionist approach with semantic information and simple recurrent networks to extend and enrich the technologies for sentence parsing. Considering the parsing efficiency, CIParser processes the semantic information of nouns and verbs in current stage. The performance of the Cognitive Interactionist Parser is evaluated using elaborately designed experiments using the noted SUSANNE Corpus. The experimental results demonstrate that the Cognitive Interactionist Parser surpasses two state-of-the-art statistical parsers in two classical measures, Precision and Recall, of Information Retrieval (IR).

Statistical learning and language acquisition.

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants' ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants' sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. WIREs Cogn Sci 2010 1 906-914 This article is categorized under: Linguistics > Language Acquisition Psychology > Language.

Statistical learning in a natural language by 8-month-old infants.

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants' ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition.

Statistical language learning in neonates revealed by event-related brain potentials

BackgroundStatistical learning is a candidate for one of the basic prerequisites underlying the expeditious acquisition of spoken language. Infants from 8 months of age exhibit this form of learning to segment fluent speech into distinct words. To test the statistical learning skills at birth, we recorded event-related brain responses of sleeping neonates while they were listening to a stream of syllables containing statistical cues to word boundaries.ResultsWe found evidence that sleeping neonates are able to automatically extract statistical properties of the speech input and thus detect the word boundaries in a continuous stream of syllables containing no morphological cues. Syllable-specific event-related brain responses found in two separate studies demonstrated that the neonatal brain treated the syllables differently according to their position within pseudowords.ConclusionThese results demonstrate that neonates can efficiently learn transitional probabilities or frequencies of co-occurrence between different syllables, enabling them to detect word boundaries and in this way isolate single words out of fluent natural speech. The ability to adopt statistical structures from speech may play a fundamental role as one of the earliest prerequisites of language acquisition.

Statistical Language Learning and Face Processing by Infants and Toddlers with Williams Syndrome

Statistical Language Learning and Face Processing by Infants and Toddlers with Williams Syndrome

The Role of Distributional Cues in the Acquisition of Verb Argument Structures

This paper investigates the role of input frequency in the acquisition of verb argument structures based on distributional information of a corpus of utterances derived from the English CHILDES database (MacWhinney 1993). It has been widely accepted that children successfully learn verb argument structures by innate language mechanisms, such as linking rules which connect verb meanings and its syntactic structures. In contrast, an approach to language acquisition called “statistical language learning” has currently claimed that children could succeed in acquiring syntactic structures in the absence of innate language mechanisms, making use of distributional properties of the input. In this paper, I evaluate the feasibility of the statistical learning in acquiring verb argument structures, based on distributional information about locative verbs in parental input. The naturalistic data allow us to investigate to what extent the statistical learning approach can and cannot help children succeed in learning the syntax of locative verbs. Based on the results of English database analysis, I show that there is rich statistical information for learning the syntactic possibilities of locative verbs in parental input, despite some limitations in the statistical learning approach. (Sangji University)

Constraints on Statistical Language Learning

How do learners discover the structure in linguistic input? One set of cues which learners might use to acquire phrase structure are the dependencies, or predictive relationships, which link elements within phrases. In order to determine whether learners can use this statistical information, adults and children were exposed to artificial languages that either contained or violated the kinds of dependencies that characterize natural languages. Additional experiments contrasted the acquisition of these linguistic systems with the same grammars implemented as non-linguistic input (sequences of nonlinguistic sounds or shapes). Predictive relationships yielded better learning for sequentially presented auditory stimuli, and for simultaneously presented visual stimuli, but no such advantage was found for sequentially presented visual stimuli. Learning outcomes were not affected by the degree to which the input contained linguistic content. These findings suggest that constraints on learning mechanisms that mirror the structure of natural languages are not tailored solely for language learning. Implications for theories of language acquisition and perceptual learning are discussed.

Statistical Language Learning.

Statistical Language Learning.