Linking sounds to meanings: Infant statistical learning in a natural language

The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of sequences presented in fluent speech and infants’ subsequent use of those sequences as labels for novel objects. Notably, the materials were drawn from a natural language unfamiliar to the infants (Italian). The results of three experiments suggest that there is a close relationship between the statistics of the speech stream and subsequent mapping of labels to referents. Mapping was facilitated when the labels contained high transitional probabilities in the forward and/or backward direction (Experiment 1). When no transitional probability information was available (Experiment 2), or when the internal transitional probabilities of the labels were low in both directions (Experiment 3), infants failed to link the labels to their referents. Word learning appears to be strongly influenced by infants’ prior experience with the distribution of sounds that make up words in natural languages.     

The neural correlates of statistical learning in a word segmentation task: An fMRI study

Functional magnetic resonance imaging (fMRI) was used to assess neural activation as participants learned to segment continuous streams of speech containing syllable sequences varying in their transitional probabilities. Speech streams were presented in four runs, each followed by a behavioral test to measure the extent of learning over time. Behavioral performance indicated that participants could discriminate statistically coherent sequences (words) from less coherent sequences (partwords). Individual rates of learning, defined as the difference in ratings for words and partwords, were used as predictors of neural activation to ask which brain areas showed activity associated with these measures. Results showed significant activity in the pars opercularis and pars triangularis regions of the left inferior frontal gyrus (LIFG). The relationship between these findings and prior work on the neural basis of statistical learning is discussed, and parallels to the frontal/subcortical network involved in other forms of implicit sequence learning are considered.     

Speech segmentation by statistical learning is supported by domain-general processes within working memory

The purpose of this study was to examine the extent to which working memory resources are recruited during statistical learning (SL). Participants were asked to identify novel words in an artificial speech stream where the transitional probabilities between syllables provided the only segmentation cue. Experiments 1 and 2 demonstrated that segmentation performance improved when the speech rate was slowed down, suggesting that SL is supported by some form of active processing or maintenance mechanism that operates more effectively under slower presentation rates. In Experiment 3 we investigated the nature of this mechanism by asking participants to perform a two-back task while listening to the speech stream. Half of the participants performed a two-back rhyme task designed to engage phonological processing, whereas the other half performed a comparable two-back task on un-nameable visual shapes. It was hypothesized that if SL is dependent only upon domain-specific processes (i.e., phonological rehearsal), the rhyme task should impair speech segmentation performance more than the shape task. However, the two loads were equally disruptive to learning, as they both eradicated the benefit provided by the slow rate. These results suggest that SL is supported by working-memory processes that rely on domain-general resources.     

The link between statistical segmentation and word learning in adults

Many studies have shown that listeners can segment words from running speech based on conditional probabilities of syllable transitions, suggesting that this statistical learning could be a foundational component of language learning. However, few studies have shown a direct link between statistical segmentation and word learning. We examined this possible link in adults by following a statistical segmentation exposure phase with an artificial lexicon learning phase. Participants were able to learn all novel object-label pairings, but pairings were learned faster when labels contained high probability (word-like) or non-occurring syllable transitions from the statistical segmentation phase than when they contained low probability (boundary-straddling) syllable transitions. This suggests that, for adults, labels inconsistent with expectations based on statistical learning are harder to learn than consistent or neutral labels. In contrast, a previous study found that infants learn consistent labels, but not inconsistent or neutral labels.     

The role of perceptual salience during the segmentation of connected speech

Statistical learning allows listeners to track transitional probabilities among syllable sequences and use these probabilities for subsequent speech segmentation. Recent studies have shown that other sources of information, such as rhythmic cues, can modulate the dependencies extracted via statistical computation. In this study, we explored how syllables made salient by a pitch rise affect the segmentation of trisyllabic words from an artificial speech stream by native speakers of three different languages (Spanish, English, and French). Results showed that, whereas performance of French participants did not significantly vary across stress positions (likely due to language-specific rhythmic characteristics), the segmentation performance of Spanish and English listeners was unaltered when syllables in word-initial and word-final positions were salient, but it dropped to chance level when salience was on the medial syllable. We argue that pitch rise in word-medial syllables draws attentional resources away from word boundaries, thus decreasing segmentation effectiveness.     

Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies.     

统计学习的认知神经机制及其与语言的关系

统计学习是指个体在连续刺激流中发现转移概率等统计规律的过程, 在Saffran等(1996)的经典婴儿语音切分研究中首次被提出。大量研究证实了统计学习的普遍存在, 近期学界开始关注统计学习的特异性及其对认知的影响, 尤其是从学习过程及其特异性两个方面阐述统计学习的认知神经机制并揭示其和语言的交互作用。未来应从脑和行为的多模态数据视角, 丰富统计学习结果的行为和神经指标, 考察不同类型统计学习过程的动态神经活动模式, 建立统计学习行为和脑的关联, 深化对统计学习认知神经机制的认识, 在统计学习与语言交互作用的基础上, 从成人二语学习切入结合音乐统计学习训练探讨促进语言学习的统计学习干预手段。     

Infants’ statistical word segmentation in an artificial language is linked to both parental speech input and reported production abilities

Individual variability in infant's language processing is partly explained by environmental factors, like the quantity of parental speech input, as well as by infant‐specific factors, like speech production. Here, we explore how these factors affect infant word segmentation. We used an artificial language to ensure that only statistical regularities (like transitional probabilities between syllables) could cue word boundaries, and then asked how the quantity of parental speech input and infants’ babbling repertoire predict infants’ abilities to use these statistical cues. We replicated prior reports showing that 8‐month‐old infants use statistical cues to segment words, with a preference for part‐words over words (a novelty effect). Crucially, 8‐month‐olds with larger novelty effects had received more speech input at 4 months and had greater production abilities at 8 months. These findings establish for the first time that the ability to extract statistical information from speech correlates with individual factors in infancy, like early speech experience and language production. Implications of these findings for understanding individual variability in early language acquisition are discussed.     

Acquiring variation in an artificial language: Children and adults are sensitive to socially conditioned linguistic variation

Languages exhibit sociolinguistic variation, such that adult native speakers condition the usage of linguistic variants on social context, gender, and ethnicity, among other cues. While the existence of this kind of socially conditioned variation is well-established, less is known about how it is acquired. Studies of naturalistic language use by children provide various examples where children’s production of sociolinguistic variants appears to be conditioned on similar factors to adults’ production, but it is difficult to determine whether this reflects knowledge of sociolinguistic conditioning or systematic differences in the input to children from different social groups. Furthermore, artificial language learning experiments have shown that children have a tendency to eliminate variation, a process which could potentially work against their acquisition of sociolinguistic variation. The current study used a semi-artificial language learning paradigm to investigate learning of the sociolinguistic cue of speaker identity in 6-year-olds and adults. Participants were trained and tested on an artificial language where nouns were obligatorily followed by one of two meaningless particles and were produced by one of two speakers (one male, one female). Particle usage was conditioned deterministically on speaker identity (Experiment 1), probabilistically (Experiment 2), or not at all (Experiment 3). Participants were given tests of production and comprehension. In Experiments 1 and 2, both children and adults successfully acquired the speaker identity cue, although the effect was stronger for adults and in Experiment 1. In addition, in all three experiments, there was evidence of regularization in participants’ productions, although the type of regularization differed with age: children showed regularization by boosting the frequency of one particle at the expense of the other, while adults regularized by conditioning particle usage on lexical items. Overall, results demonstrate that children and adults are sensitive to speaker identity cues, an ability which is fundamental to tracking sociolinguistic variation, and that children’s well-established tendency to regularize does not prevent them from learning sociolinguistically conditioned variation.     

Statistical language learning: mechanisms and constraints

What types of mechanisms underlie the acquisition of human language? Recent evidence suggests that learners, including infants, can use statistical properties of linguistic input to discover structure, including sound patterns, words, and the beginnings of grammar. These abilities appear to be both powerful and constrained, such that some statistical patterns are more readily detected and used than others. Implications for the structure of human languages are discussed.     

What artificial grammar learning reveals about the neurobiology of syntax

In this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-linear unification grammar in an implicit artificial grammar learning paradigm in 32 healthy Dutch university students (natural language FMRI data were already acquired for these participants). We predicted that artificial syntax processing would engage the left inferior frontal region (BA 44/45) and that this activation would overlap with syntax-related variability observed in the natural language experiment. The main findings of this study show that the left inferior frontal region centered on BA 44/45 is active during artificial syntax processing of well-formed (grammatical) sequence independent of local subsequence familiarity. The same region is engaged to a greater extent when a syntactic violation is present and structural unification becomes difficult or impossible. The effects related to artificial syntax in the left inferior frontal region (BA 44/45) were essentially identical when we masked these with activity related to natural syntax in the same subjects. Finally, the medial temporal lobe was deactivated during this operation, consistent with the view that implicit processing does not rely on declarative memory mechanisms that engage the medial temporal lobe. In the context of recent FMRI findings, we raise the question whether Broca’s region (or subregions) is specifically related to syntactic movement operations or the processing of hierarchically nested non-adjacent dependencies in the discussion section. We conclude that this is not the case. Instead, we argue that the left inferior frontal region is a generic on-line sequence processor that unifies information from various sources in an incremental and recursive manner, independent of whether there are any processing requirements related to syntactic movement or hierarchically nested structures. In addition, we argue that the Chomsky hierarchy is not directly relevant for neurobiological systems.     

Acquiring and processing verb argument structure: distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.     

口语加工中的词语切分线索

词是语言的基本结构单位, 对词语进行切分是语言加工的重要步骤。口语语流中的切分线索来自于语音、语义和语法三个方面。语音线索包括概率信息、音位配列规则和韵律信息, 韵律信息中还包括词重音、时长和音高等内容, 这些线索的使用在接触语言的早期阶段就逐渐被个体所掌握, 而且在不同的语言背景下有一定的特异性。语法和语义线索属于较高级的线索机制, 主要作用于词语切分过程的后期。后续研究应从语言的毕生发展和语言的特异性两个方面考察口语语言加工中的词语切分线索。     

The acquisition of allophonic rules: statistical learning with linguistic constraints

Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.     

An interaction between prosody and statistics in the segmentation of fluent speech

Sensitivity to prosodic cues might be used to constrain lexical search. Indeed, the prosodic organization of speech is such that words are invariably aligned with phrasal prosodic edges, providing a cue to segmentation. In this paper we devise an experimental paradigm that allows us to investigate the interaction between statistical and prosodic cues to extract words from a speech stream. We provide evidence that statistics over the syllables are computed independently of prosody. However, we also show that trisyllabic sequences with high transition probabilities that straddle two prosodic constituents appear not to be recognized. Taken together, our findings suggest that prosody acts as a filter, suppressing possible word-like sequences that span prosodic constituents.     

婴儿指示性手势与其语言学习的关系

指示性手势是指明空间中某个物体、处所或事件的手部动作,在婴儿1岁时出现,与婴儿的语言学习存在着较强的相关,然而目前尚不清楚为什么会存在此相关。可能存在两种内在机制:(1)指示性手势影响婴儿的学习环境——引发了适时的语言输入;(2)影响学习者本身——帮助婴儿创造有效的学习状态并示意给他人。未来的研究需要探讨这两种机制如何交互作用,以及如何将指示性手势作为诊断或干预工具,用于识别和帮助有语言障碍风险的个体。     

The acquisition of morphological knowledge investigated through artificial language learning

Affix knowledge plays an important role in visual word recognition, but little is known about how it is acquired. The authors present a new method of investigating the acquisition of affixes in which participants are trained on novel affixes presented in novel word contexts (e.g., sleepnept). Experiment 1 investigated the role of semantic information on affix acquisition by comparing a form-learning condition with a condition in which participants also received definitions for each novel word. Experiment 2 investigated the role of long-term consolidation on affix acquisition by comparing knowledge of learned affixes two days and nearly two months after training. Results demonstrated that episodic knowledge of affixes can be acquired shortly after a single training session using either form or semantic learning, but suggested that the development of lexicalized representations of affixes requires the provision of semantic information during learning as well as a substantial period of offline consolidation.     

On the learnability of frequent and infrequent word orders: An artificial language learning study

Most languages have a basic or “canonical” word order, which determines the relative positions of the subject (S), the verb (V), and the object (O) in a typical declarative sentence. The frequency of occurrence of the six possible word orders among world languages is not distributed uniformly. While SVO and SOV represent around 85% of world languages, orders like VSO (9%) or OSV (0.5%) are much less frequent or extremely rare. One possible explanation for this asymmetry is that biological and cognitive constraints for structured sequence processing make some word orders easier to be processed than others. Therefore, the high frequency of these word orders would be related to their higher learnability. The aim of the present study was to compare the learnability of different word orders between groups of adult subjects. Four artificial languages with different word orders were trained: two frequent (SVO, SOV) and two infrequent (VSO, OSV). In a test stage, subjects were asked to discriminate between new correct sentences and syntax or semantic violations. Higher performance rates and faster responses were observed for more frequent word orders. The results support the hypothesis that more frequent word orders are more easily learned.     

The prevalence of synaesthesia depends on early language learning

According to one theory, synaesthesia develops, or is preserved, because it helps children learn. If so, it should be more common among adults who faced greater childhood learning challenges. In the largest survey of synaesthesia to date, the incidence of synaesthesia was compared among native speakers of languages with transparent (easier) and opaque (more difficult) orthographies. Contrary to our prediction, native speakers of Czech (transparent) were more likely to be synaesthetes than native speakers of English (opaque). However, exploratory analyses suggested that this was because more Czechs learned non-native second languages, which was strongly associated with synaesthesia, consistent with the learning hypothesis. Furthermore, the incidence of synaesthesia among speakers of opaque languages was double that among speakers of transparent languages other than Czech, also consistent with the learning hypothesis. These findings contribute to an emerging understanding of synaesthetic development as a complex and lengthy process with multiple causal influences.     

The curse of knowledge: first language knowledge impairs adult learners' use of novel statistics for word segmentation

We investigated whether adult learners' knowledge of phonotactic restrictions on word forms from their first language impacts their ability to use statistical information to segment words in a novel language. Adults were exposed to a speech stream where English phonotactics and phoneme co-occurrence information conflicted. A control where these did not conflict was also run. Participants chose between words defined by novel statistics and words that are phonotactically possible in English, but had much lower phoneme contingencies. Control participants selected words defined by statistics while experimental participants did not. This result held up with increases in exposure and when segmentation was aided by telling participants a word prior to exposure. It was not the case that participants simply preferred English-sounding words, however, when the stimuli contained very short pauses, participants were able to learn the novel words despite the fact that they violated English phonotactics. Results suggest that prior linguistic knowledge can interfere with learners' abilities to segment words from running speech using purely statistical cues at initial exposure.