On-line individual differences in statistical learning predict language processing

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli.

Visual statistical learning is related to natural language ability in adults: An ERP study

Statistical learning (SL) is believed to enable language acquisition by allowing individuals to learn regularities within linguistic input. However, neural evidence supporting a direct relationship between SL and language ability is scarce. We investigated whether there are associations between event-related potential (ERP) correlates of SL and language abilities while controlling for the general level of selective attention. Seventeen adults completed tests of visual SL, receptive vocabulary, grammatical ability, and sentence completion. Response times and ERPs showed that SL is related to receptive vocabulary and grammatical ability. ERPs indicated that the relationship between SL and grammatical ability was independent of attention while the association between SL and receptive vocabulary depended on attention. The implications of these dissociative relationships in terms of underlying mechanisms of SL and language are discussed. These results further elucidate the cognitive nature of the links between SL mechanisms and language abilities.

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages.

Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis

Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD.

Attentional effects on rule extraction and consolidation from speech

Incidental learning plays a crucial role in the initial phases of language acquisition. However the knowledge derived from implicit learning, which is based on prediction-based mechanisms, may become explicit. The role that attention plays in the formation of implicit and explicit knowledge of the learned material is unclear. In the present study, we investigated the role that attention plays in the acquisition of non-adjacent rule learning from speech. In addition, we also tested whether the amount of attention during learning changes the representation of the learned material after a 24h delay containing sleep. For that, we developed an experiment run on two consecutive days consisting on the exposure to an artificial language that contained non-adjacent dependencies (rules) between words whereas different conditions were established to manipulate the amount of attention given to the rules (target and non-target conditions). Furthermore, we used both indirect and direct measures of learning that are more sensitive to implicit and explicit knowledge, respectively. Whereas the indirect measures indicated that learning of the rules occurred regardless of attention, more explicit judgments after learning showed differences in the type of learning reached under the two attention conditions. 24 hours later, indirect measures showed no further improvements during additional language exposure and explicit judgments indicated that only the information more robustly learned in the previous day, was consolidated.

Reading span task performance, linguistic experience, and the processing of unexpected syntactic events

Accounts of individual differences in online language processing ability often focus on the explanatory utility of verbal working memory, as measured by reading span tasks. Although variability in reading span task performance likely reflects individual differences in multiple underlying traits, skills, and processes, accumulating evidence suggests that reading span scores also reflect variability in the linguistic experiences of an individual. Here, through an individual differences approach, we first demonstrate that reading span scores correlate significantly with measures of the amount of experience an individual has had with written language (gauged by measures that provide "proxy estimates" of print exposure). We then explore the relationship between reading span scores and online language processing ability. Individuals with higher reading spans demonstrated greater sensitivity to violations of statistical regularities found in natural language-as evinced by higher reading times (RTs) on the disambiguating region of garden-path sentences-relative to their lower span counterparts. This result held after statistically controlling for individual differences in a non-linguistic operation span task. Taken together, these results suggest that accounts of individual differences in sentence processing can benefit from a stronger focus on experiential factors, especially when considered in relation to variability in perceptual and learning abilities that influence the amount of benefit gleaned from such experience.

Individual Differences in Statistical Learning Predict Children's Comprehension of Syntax

Variability in children's language acquisition is likely due to a number of cognitive and social variables. The current study investigated whether individual differences in statistical learning (SL), which has been implicated in language acquisition, independently predicted 6- to 8-year-old's comprehension of syntax. Sixty-eight (N = 68) English-speaking children completed a test of comprehension of four syntactic structures, a test of SL utilizing nonlinguistic visual stimuli, and several additional control measures. The results revealed that SL independently predicted comprehension of two syntactic structures that show considerable variability in this age range: passives and object relative clauses. These data suggest that individual differences in children's capacity for SL are associated with the acquisition of the syntax of natural languages.

The language faculty that wasn't: a usage-based account of natural language recursion

In the generative tradition, the language faculty has been shrinking—perhaps to include only the mechanism of recursion. This paper argues that even this view of the language faculty is too expansive. We first argue that a language faculty is difficult to reconcile with evolutionary considerations. We then focus on recursion as a detailed case study, arguing that our ability to process recursive structure does not rely on recursion as a property of the grammar, but instead emerges gradually by piggybacking on domain-general sequence learning abilities. Evidence from genetics, comparative work on non-human primates, and cognitive neuroscience suggests that humans have evolved complex sequence learning skills, which were subsequently pressed into service to accommodate language. Constraints on sequence learning therefore have played an important role in shaping the cultural evolution of linguistic structure, including our limited abilities for processing recursive structure. Finally, we re-evaluate some of the key considerations that have often been taken to require the postulation of a language faculty.

The language faculty that wasn't: a usage-based account of natural language recursion

In the generative tradition, the language faculty has been shrinking-perhaps to include only the mechanism of recursion. This paper argues that even this view of the language faculty is too expansive. We first argue that a language faculty is difficult to reconcile with evolutionary considerations. We then focus on recursion as a detailed case study, arguing that our ability to process recursive structure does not rely on recursion as a property of the grammar, but instead emerges gradually by piggybacking on domain-general sequence learning abilities. Evidence from genetics, comparative work on non-human primates, and cognitive neuroscience suggests that humans have evolved complex sequence learning skills, which were subsequently pressed into service to accommodate language. Constraints on sequence learning therefore have played an important role in shaping the cultural evolution of linguistic structure, including our limited abilities for processing recursive structure. Finally, we re-evaluate some of the key considerations that have often been taken to require the postulation of a language faculty.

The Relationship Between Artificial and Second Language Learning

Artificial language learning (ALL) experiments have become an important tool in exploring principles of language and language learning. A persistent question in all of this work, however, is whether ALL engages the linguistic system and whether ALL studies are ecologically valid assessments of natural language ability. In the present study, we considered these questions by examining the relationship between performance in an ALL task and second language learning ability. Participants enrolled in a Spanish language class were evaluated using a number of different measures of Spanish ability and classroom performance, which was compared to IQ and a number of different measures of ALL performance. The results show that success in ALL experiments, particularly more complex artificial languages, correlates positively with indices of L2 learning even after controlling for IQ. These findings provide a key link between studies involving ALL and our understanding of second language learning in the classroom.

Can we improve structured sequence processing? Exploring the direct and indirect effects of computerized training using a mediational model

Recent research suggests that language acquisition may rely on domain-general learning abilities, such as structured sequence processing, which is the ability to extract, encode, and represent structured patterns in a temporal sequence. If structured sequence processing supports language, then it may be possible to improve language function by enhancing this foundational learning ability. The goal of the present study was to use a novel computerized training task as a means to better understand the relationship between structured sequence processing and language function. Participants first were assessed on pre-training tasks to provide baseline behavioral measures of structured sequence processing and language abilities. Participants were then quasi-randomly assigned to either a treatment group involving adaptive structured visuospatial sequence training, a treatment group involving adaptive non-structured visuospatial sequence training, or a control group. Following four days of sequence training, all participants were assessed with the same pre-training measures. Overall comparison of the post-training means revealed no group differences. However, in order to examine the potential relations between sequence training, structured sequence processing, and language ability, we used a mediation analysis that showed two competing effects. In the indirect effect, adaptive sequence training with structural regularities had a positive impact on structured sequence processing performance, which in turn had a positive impact on language processing. This finding not only identifies a potential novel intervention to treat language impairments but also may be the first demonstration that structured sequence processing can be improved and that this, in turn, has an impact on language processing. However, in the direct effect, adaptive sequence training with structural regularities had a direct negative impact on language processing. This unexpected finding suggests that adaptive training with structural regularities might potentially interfere with language processing. Taken together, these findings underscore the importance of pursuing designs that promote a better understanding of the mechanisms underlying training-related changes, so that regimens can be developed that help reduce these types of negative effects while simultaneously maximizing the benefits to outcome measures of interest.

Statistical learning as an individual ability: Theoretical perspectives and empirical evidence

Although the power of statistical learning (SL) in explaining a wide range of linguistic functions is gaining increasing support, relatively little research has focused on this theoretical construct from the perspective of individual differences. However, to be able to reliably link individual differences in a given ability such as language learning to individual differences in SL, three critical theoretical questions should be posed: Is SL a componential or unified ability? Is it nested within other general cognitive abilities? Is it a stable capacity of an individual? Following an initial mapping sentence outlining the possible dimensions of SL, we employed a battery of SL tasks in the visual and auditory modalities, using verbal and non-verbal stimuli, with adjacent and non-adjacent contingencies. SL tasks were administered along with general cognitive tasks in a within-subject design at two time points to explore our theoretical questions. We found that SL, as measured by some tasks, is a stable and reliable capacity of an individual. Moreover, we found SL to be independent of general cognitive abilities such as intelligence or working memory. However, SL is not a unified capacity, so that individual sensitivity to conditional probabilities is not uniform across modalities and stimuli.

Statistical Learning is Related to Early Literacy-Related Skills

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy-oral language, vocabulary knowledge, and phonological processing-within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed.

Domain generality versus modality specificity: the paradox of statistical learning

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

Relationship between perceptual learning in speech and statistical learning in younger and older adults

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly.

Shining light on infants' discovery of structure

Learning and discovery seem often to begin with noting patterns. Human infants are skilled at pattern detection, even patterns only definable at an abstract level, which is key to their acquisition of complex knowledge systems such as language and music. However, research examining infants' abstract rule learning has generated inconsistent results. We propose that apparent domain differences in infants' abstract rule learning may be the result of extraneous stimulus variation and discrepancies in the methodologies employed across studies probing this skill. We discuss how a behavioral methodology indexing infants' online learning would be valuable in furthering understanding of infants' (as well as adults') abstract rule learning and its neurophysiological concomitants. We outline current research aimed at developing such an index, and we propose future research, pairing such techniques with neurophysiological methods, aimed at shining more light on human skill at discovering structure.

Impaired statistical learning of non-adjacent dependencies in adolescents with specific language impairment

Being able to track dependencies between syntactic elements separated by other constituents is crucial for language acquisition and processing (e.g., in subject-noun/verb agreement). Although long assumed to require language-specific machinery, research on statistical learning has suggested that domain-general mechanisms may support the acquisition of non-adjacent dependencies. In this study, we investigated whether individuals with specific language impairment (SLI)-who have problems with long-distance dependencies in language-also have problems with statistical learning of non-adjacent relations. The results confirmed this hypothesis, indicating that statistical learning may subserve the acquisition and processing of long-distance dependencies in natural language.

Implicit structured sequence learning: an fMRI study of the structural mere-exposure effect

In this event-related fMRI study we investigated the effect of 5 days of implicit acquisition on preference classification by means of an artificial grammar learning (AGL) paradigm based on the structural mere-exposure effect and preference classification using a simple right-linear unification grammar. This allowed us to investigate implicit AGL in a proper learning design by including baseline measurements prior to grammar exposure. After 5 days of implicit acquisition, the fMRI results showed activations in a network of brain regions including the inferior frontal (centered on BA 44/45) and the medial prefrontal regions (centered on BA 8/32). Importantly, and central to this study, the inclusion of a naive preference fMRI baseline measurement allowed us to conclude that these fMRI findings were the intrinsic outcomes of the learning process itself and not a reflection of a preexisting functionality recruited during classification, independent of acquisition. Support for the implicit nature of the knowledge utilized during preference classification on day 5 come from the fact that the basal ganglia, associated with implicit procedural learning, were activated during classification, while the medial temporal lobe system, associated with explicit declarative memory, was consistently deactivated. Thus, preference classification in combination with structural mere-exposure can be used to investigate structural sequence processing (syntax) in unsupervised AGL paradigms with proper learning designs.

Memory mechanisms supporting syntactic comprehension

Efforts to characterize the memory system that supports sentence comprehension have historically drawn extensively on short-term memory as a source of mechanisms that might apply to sentences. The focus of these efforts has changed significantly in the past decade. As a result of changes in models of short-term working memory (ST-WM) and developments in models of sentence comprehension, the effort to relate entire components of an ST-WM system, such as those in the model developed by Baddeley (Nature Reviews Neuroscience 4: 829-839, 2003) to sentence comprehension has largely been replaced by an effort to relate more specific mechanisms found in modern models of ST-WM to memory processes that support one aspect of sentence comprehension--the assignment of syntactic structure (parsing) and its use in determining sentence meaning (interpretation) during sentence comprehension. In this article, we present the historical background to recent studies of the memory mechanisms that support parsing and interpretation and review recent research into this relation. We argue that the results of this research do not converge on a set of mechanisms derived from ST-WM that apply to parsing and interpretation. We argue that the memory mechanisms supporting parsing and interpretation have features that characterize another memory system that has been postulated to account for skilled performance-long-term working memory. We propose a model of the relation of different aspects of parsing and interpretation to ST-WM and long-term working memory.

Does it really matter? Separating the effects of musical training on syntax acquisition

The possible transfer of musical expertise to the acquisition of syntactical structures in first and second language has emerged recently as an intriguing topic in the research of cognitive processes. However, it is unlikely that the benefits of musical training extend equally to the acquisition of all syntactical structures. As cognitive transfer presumably requires overlapping processing components and brain regions involved in these processing components, one can surmise that transfer between musical ability and syntax acquisition would be limited to structural elements that are shared between the two. We propose that musical expertise transfers only to the processing of recursive long-distance dependencies inherent in hierarchical syntactic structures. In this study, we taught fifty-six participants with widely varying degrees of musical expertise the artificial language BROCANTO, which allows the direct comparison of long-distance and local dependencies. We found that the quantity of musical training (measured in accumulated hours of practice and instruction) explained unique variance in performance in the long-distance dependency condition only. These data suggest that musical training facilitates the acquisition specifically of hierarchical syntactic structures.

The neurobiology of syntax: beyond string sets

The human capacity to acquire language is an outstanding scientific challenge to understand. Somehow our language capacities arise from the way the human brain processes, develops and learns in interaction with its environment. To set the stage, we begin with a summary of what is known about the neural organization of language and what our artificial grammar learning (AGL) studies have revealed. We then review the Chomsky hierarchy in the context of the theory of computation and formal learning theory. Finally, we outline a neurobiological model of language acquisition and processing based on an adaptive, recurrent, spiking network architecture. This architecture implements an asynchronous, event-driven, parallel system for recursive processing. We conclude that the brain represents grammars (or more precisely, the parser/generator) in its connectivity, and its ability for syntax is based on neurobiological infrastructure for structured sequence processing. The acquisition of this ability is accounted for in an adaptive dynamical systems framework. Artificial language learning (ALL) paradigms might be used to study the acquisition process within such a framework, as well as the processing properties of the underlying neurobiological infrastructure. However, it is necessary to combine and constrain the interpretation of ALL results by theoretical models and empirical studies on natural language processing. Given that the faculty of language is captured by classical computational models to a significant extent, and that these can be embedded in dynamic network architectures, there is hope that significant progress can be made in understanding the neurobiology of the language faculty.

Visual Sequence Learning in Infancy: Domain-General and Domain-Specific Associations with Language

Research suggests that non-linguistic sequence learning abilities are an important contributor to language development (Conway, Bauernschmidt, Huang, & Pisoni, 2010). The current study investigated visual sequence learning as a possible predictor of vocabulary development in infants. Fifty-eight 8.5-month-old infants were presented with a three-location spatiotemporal sequence of multi-colored geometric shapes. Early language skills were assessed using the MacArthur-Bates CDI. Analyses of children's reaction times to the stimuli suggest that the extent to which infants demonstrated learning was significantly correlated with their vocabulary comprehension at the time of test and with their gestural comprehension abilities 5 months later. These findings suggest that visual sequence learning may have both domain-general and domain-specific associations with language learning.

Defining the genetic architecture of human developmental language impairment

Language is a uniquely human trait, which poses limitations on animal models for discovering biological substrates and pathways. Despite this challenge, rapidly developing biotechnology in the field of genomics has made human genetics studies a viable alternative route for defining the molecular neuroscience of human language. This is accomplished by studying families that transmit both normal and disordered language across generations. The language disorder reviewed here is specific language impairment (SLI), a developmental deficiency in language acquisition despite adequate opportunity, normal intelligence, and without any apparent neurological etiology. Here, we describe disease gene discovery paradigms as applied to SLI families and review the progress this field has made. After review the evidence that genetic factors influence SLI, we discuss methods and findings from scans of the human chromosomes, including the main replicated regions on chromosomes 13, 16 and 19 and two identified genes, ATP2C2 and CMIP that appear to account for the language variation on chromosome 16. Additional work has been done on candidate genes, i.e., genes chosen a priori and not through a genome scanning studies, including several studies of CNTNAP2 and some recent work implicating BDNF as a gene x gene interaction partner of genetic variation on chromosome 13 that influences language. These recent developments may allow for better use of post-mortem human brain samples functional studies and animal models for circumscribed language subcomponents. In the future, the identification of genetic variation associated with language phenotypes will provide the molecular pathways to understanding human language.

Interference between sentence processing and probabilistic implicit sequence learning

Background

During sentence processing we decode the sequential combination of words, phrases or sentences according to previously learned rules. The computational mechanisms and neural correlates of these rules are still much debated. Other key issue is whether sentence processing solely relies on language-specific mechanisms or is it also governed by domain-general principles.

Methodology/Principal Findings

In the present study, we investigated the relationship between sentence processing and implicit sequence learning in a dual-task paradigm in which the primary task was a non-linguistic task (Alternating Serial Reaction Time Task for measuring probabilistic implicit sequence learning), while the secondary task were a sentence comprehension task relying on syntactic processing. We used two control conditions: a non-linguistic one (math condition) and a linguistic task (word processing task). Here we show that the sentence processing interfered with the probabilistic implicit sequence learning task, while the other two tasks did not produce a similar effect.

Conclusions/Significance

Our findings suggest that operations during sentence processing utilize resources underlying non-domain-specific probabilistic procedural learning. Furthermore, it provides a bridge between two competitive frameworks of language processing. It appears that procedural and statistical models of language are not mutually exclusive, particularly for sentence processing. These results show that the implicit procedural system is engaged in sentence processing, but on a mechanism level, language might still be based on statistical computations.