Sequential expectations: the role of prediction-based learning in language

The Role of Feedback in the Statistical Learning of Language-Like Regularities

In language learning, learners engage with their environment, incorporating cues from different sources. However, in lab-based experiments, using artificial languages, many of the cues and features that are part of real-world language learning are stripped away. In three experiments, we investigated the role of positive, negative, and mixed feedback on the gradual learning of language-like statistical regularities within an active guessing game paradigm. In Experiment 1, participants received deterministic feedback (100%), whereas probabilistic feedback (i.e., 75% or 50%) was introduced in Experiment 2. Finally, Experiment 3 explored the impact of mixed probabilistic feedback (33% positive, 33% negative, 33% no feedback). The results showed that cross-situational learning of words was observed without feedback, but participants were able to learn structural regularities of the miniature language only when feedback was provided. Interestingly, the presence of positive feedback was particularly helpful for the learner, promoting more in-depth learning of the artificial language.

Online measurement of learning temporal statistical structure in categorization tasks

The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive behavioral design to characterize the VSL trajectory during ongoing task performance. In sequential categorization tasks, we assessed two previously identified VSL markers: priming of the second predictable image in a pair manifested by a reduced reaction time (RT) and greater accuracy, and the anticipatory effect on the first image revealed by a longer RT. First, in Experiment 1A, we used an adapted paradigm and replicated these VSL markers; however, they appeared to be confounded by motor learning. Next, in Experiment 1B, we confirmed the confounding influence of motor learning. To assess VSL without motor learning, in Experiment 2 we (1) simplified the categorization task, (2) raised the number of subjects and image repetitions, and (3) increased the number of single unpaired images. Using linear mixed-effect modeling and estimated marginal means of linear trends, we found that the RT curves differed significantly between predictable paired and control single images. Further, the VSL curve fitted a logarithmic model, suggesting a rapid learning process. These results suggest that our paradigm in Experiment 2 seems to be a viable online tool to monitor the behavioral correlates of unsupervised implicit VSL.

Do Children Use Multi-Word Information in Real-Time Sentence Comprehension?

Meaning in language emerges from multiple words, and children are sensitive to multi-word frequency from infancy. While children successfully use cues from single words to generate linguistic predictions, it is less clear whether and how they use multi-word sequences to guide real-time language processing and whether they form predictions on the basis of multi-word information or pairwise associations. We address these questions in two visual-world eye-tracking experiments with 5- to 8-year-old children. In Experiment 1, we asked whether children generate more robust predictions for the sentence-final object of highly frequent sequences (e.g., "Throw the ball"), compared to less frequent sequences (e.g., "Throw the book"). We further examined if gaze patterns reflect event knowledge or phrasal frequency by comparing the processing of phrases that have the same event structure but differ in multi-word content (e.g., "Brush your teeth" vs. "Brush her teeth"). In the second study, we employed a training paradigm to ask if children are capable of generating predictio.ns from novel multi-word associations while controlling for the overall frequency of the sequences. While the results of Experiment 1 suggested that children primarily relied on event associations to generate real-time predictions, those of Experiment 2 showed that the same children were able to use recurring novel multi-word sequences to generate real-time linguistic predictions. Together, these findings suggest that children can draw on multi-word information to generate linguistic predictions, in a context-dependent fashion, and highlight the need to account for the influence of multi-word sequences in models of language processing.

The Value of Statistical Learning to Cognitive Network Science

To study the human mind is to consider the nature of associations-how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar of statistical learning (SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

Language statistical learning responds to reinforcement learning principles rooted in the striatum

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

Statistical learning is the ability to extract regularities from the environment; in the domain of language, this ability is fundamental in the learning of words and structural rules. This study uses a combination of computational modelling and functional MRI to reveal a fundamental link between online language statistical learning and reinforcement learning at the algorithmic and implementational levels.

Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style

It is still a matter of debate whether visual aids improve learning of music. In a multisession study, we investigated the neural signatures of novel music sequence learning with or without aids (auditory-only: AO, audiovisual: AV). During three training sessions on three separate days, participants (nonmusicians) reproduced (note by note on a keyboard) melodic sequences generated by an artificial musical grammar. The AV group (n = 20) had each note color-coded on screen, whereas the AO group (n = 20) had no color indication. We evaluated learning of the statistical regularities of the novel music grammar before and after training by presenting melodies ending on correct or incorrect notes and by asking participants to judge the correctness and surprisal of the final note, while EEG was recorded. We found that participants successfully learned the new grammar. Although the AV group, as compared to the AO group, reproduced longer sequences during training, there was no significant difference in learning between groups. At the neural level, after training, the AO group showed a larger N100 response to low-probability compared with high-probability notes, suggesting an increased neural sensitivity to statistical properties of the grammar; this effect was not observed in the AV group. Our findings indicate that visual aids might improve sequence reproduction while not necessarily promoting better learning, indicating a potential dissociation between sequence reproduction and learning. We suggest that the difficulty induced by auditory-only input during music training might enhance cognitive engagement, thereby improving neural sensitivity to the underlying statistical properties of the learned material.

Baboons (Papio papio) Process a Context-Free but Not a Context-Sensitive Grammar

Language processing involves the ability to master supra-regular grammars, that go beyond the level of complexity of regular grammars. This ability has been hypothesized to be a uniquely human capacity. Our study probed baboons' capacity to learn two supra-regular grammars of different levels of complexity: a context-free grammar generating sequences following a mirror structure (e.g., AB | BA, ABC | CBA) and a context-sensitive grammar generating sequences following a repeat structure (e.g., AB | AB, ABC | ABC), the latter requiring greater computational power to be processed. Fourteen baboons were tested in a prediction task, requiring them to track a moving target on a touchscreen. In distinct experiments, sequences of target locations followed one of the above two grammars, with rare violations. Baboons showed slower response times when violations occurred in mirror sequences, but did not react to violations in repeat sequences, suggesting that they learned the context-free (mirror) but not the context-sensitive (repeat) grammar. By contrast, humans tested with the same task learned both grammars. These data suggest a difference in sensitivity in baboons between a context-free and a context-sensitive grammar.

Exploring and Exploiting Uncertainty: Statistical Learning Ability Affects How We Learn to Process Language Along Multiple Dimensions of Experience

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self-paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition.

Non-adjacent dependency learning in infancy, and its link to language development

To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infants' capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infants'  statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infants' vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infants' segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size.

On Empirical Methodology, Constraints, and Hierarchy in Artificial Grammar Learning

This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long-distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns.

The Role of Statistical Learning in Understanding and Treating Spoken Language Outcomes in Deaf Children With Cochlear Implants

Purpose

Statistical learning-the ability to learn patterns in environmental input-is increasingly recognized as a foundational mechanism necessary for the successful acquisition of spoken language. Spoken language is a complex, serially presented signal that contains embedded statistical relations among linguistic units, such as phonemes, morphemes, and words, which represent the phonotactic and syntactic rules of language. In this review article, we first review recent work that demonstrates that, in typical language development, individuals who display better nonlinguistic statistical learning abilities also show better performance on different measures of language. We next review research findings that suggest that children who are deaf and use cochlear implants may have difficulties learning sequential input patterns, possibly due to auditory and/or linguistic deprivation early in development, and that the children who show better sequence learning abilities also display improved spoken language outcomes. Finally, we present recent findings suggesting that it may be possible to improve core statistical learning abilities with specialized training and interventions and that such improvements can potentially impact and facilitate the acquisition and processing of spoken language.

Method

We conducted a literature search through various online databases including PsychINFO and PubMed, as well as including relevant review articles gleaned from the reference sections of other review articles used in this review. Search terms included various combinations of the following: sequential learning, sequence learning, statistical learning, sequence processing, procedural learning, procedural memory, implicit learning, language, computerized training, working memory training, statistical learning training, deaf, deafness, hearing impairment, hearing impaired, DHH, hard of hearing, cochlear implant(s), hearing aid(s), and auditory deprivation. To keep this review concise and clear, we limited inclusion to the foundational and most recent (2005-2018) relevant studies that explicitly included research or theoretical perspectives on statistical or sequential learning. We here summarize and synthesize the most recent and relevant literature to understanding and treating language delays in children using cochlear implants through the lens of statistical learning.

Conclusions

We suggest that understanding how statistical learning contributes to spoken language development is important for understanding some of the difficulties that children who are deaf and use cochlear implants might face and argue that it may be beneficial to develop novel language interventions that focus specifically on improving core foundational statistical learning skills.

Statistical learning as a window into developmental disabilities

Until recently, most behavioral studies of children with intellectual and developmental disabilities (IDD) have used standardized assessments as a means to probe etiology and to characterize phenotypes. Over the past decade, however, tasks originally developed to investigate learning processes in typical development have been brought to bear on developmental processes in children with IDD.

This brief review will focus on one learning process in particular—statistical learning—and will provide an overview of what has been learned thus far from studies using statistical learning tasks with different groups of children with IDD conditions. While a full picture is not yet available, results to date suggest that studies of learning are both feasible and informative about learning processes that may differ across diagnostic groups, particularly as they relate to language acquisition.

More generally, studies focused on learning processes may be highly informative about different developmental trajectories both across groups and within groups of children.

Procedural-Memory, Working-Memory, and Declarative-Memory Skills Are Each Associated With Dimensional Integration in Sound-Category Learning

This paper investigates relationships between procedural-memory, declarative-memory, and working-memory skills and adult native English speakers' novel sound-category learning. Participants completed a sound-categorization task that required integrating two dimensions: one native (vowel quality), one non-native (pitch). Similar information-integration category structures in the visual and auditory domains have been shown to be best learned implicitly (e.g., Maddox et al., 2006). Thus, we predicted that individuals with greater procedural-memory capacity would better learn sound categories, because procedural memory appears to support implicit learning of new information and integration of dimensions. Seventy undergraduates were tested across two experiments. Procedural memory was assessed using a linguistic adaptation of the serial-reaction-time task (Misyak et al., 2010a,b). Declarative memory was assessed using the logical-memory subtest of the Wechsler Memory Scale-4th edition (WMS-IV; Wechsler, 2009). Working memory was assessed using an auditory version of the reading-span task (Kane et al., 2004). Experiment 1 revealed contributions of only declarative memory to dimensional integration, which might indicate not enough time or motivation to shift over to a procedural/integrative strategy. Experiment 2 gave twice the speech-sound training, distributed over 2 days, and also attempted to train at the category boundary. As predicted, effects of declarative memory were removed and effects of procedural memory emerged, but, unexpectedly, new effects of working memory surfaced. The results may be compatible with a multiple-systems account in which declarative and working memory facilitate transfer of control to the procedural system.

Atypical predictive processing during visual statistical learning in children with developmental dyslexia: an event-related potential study

Previous research suggests that individuals with developmental dyslexia perform below typical readers on non-linguistic cognitive tasks involving the learning and encoding of statistical-sequential patterns. However, the neural mechanisms underlying such a deficit have not been well examined. The aim of the present study was to investigate the event-related potential (ERP) correlates of sequence processing in a sample of children diagnosed with dyslexia using a non-linguistic visual statistical learning paradigm. Whereas the response time data suggested that both typical and atypical readers learned the statistical patterns embedded in the task, the ERP data suggested otherwise. Specifically, ERPs of the typically developing children (n = 12) showed a P300-like response indicative of learning, whereas the children diagnosed with a reading disorder (n = 8) showed no such ERP effects. These results may be due to intact implicit motor learning in the children with dyslexia but delayed attention-dependent predictive processing. These findings are consistent with other evidence suggesting that differences in statistical learning ability might underlie some of the reading deficits observed in developmental dyslexia.

Evolutionarily conserved neural signatures involved in sequencing predictions and their relevance for language

Predicting the occurrence of future events from prior ones is vital for animal perception and cognition. Although how such sequence learning (a form of relational knowledge) relates to particular operations in language remains controversial, recent evidence shows that sequence learning is disrupted in frontal lobe damage associated with aphasia. Also, neural sequencing predictions at different temporal scales resemble those involved in language operations occurring at similar scales. Furthermore, comparative work in humans and monkeys highlights evolutionarily conserved frontal substrates and predictive oscillatory signatures in the temporal lobe processing learned sequences of speech signals. Altogether this evidence supports a relational knowledge hypothesis of language evolution, proposing that language processes in humans are functionally integrated with an ancestral neural system for predictive sequence learning.

Non-adjacent Dependencies Processing in Human and Non-human Primates

Human and non-human primates share the ability to extract adjacent dependencies and, under certain conditions, non-adjacent dependencies (i.e., predictive relationships between elements that are separated by one or several intervening elements in a sequence). In this study, we explore the online extraction dynamics of non-adjacent dependencies in humans and baboons using a serial reaction time task. Participants had to produce three-target sequences containing deterministic relationships between the first and last target locations. In Experiment 1, participants from the two species could extract these non-adjacent dependencies, but humans required less exposure than baboons. In Experiment 2, the data show for the first time in a non-human primate species the successful generalization of sequential non-adjacent dependencies over novel intervening items. These findings provide new evidence to further constrain current theories about the nature and the evolutionary origins of the learning mechanisms allowing the extraction of non-adjacent dependencies.

Implicit Statistical Learning: A Tale of Two Literatures

Implicit learning and statistical learning are two contemporary approaches to the long-standing question in psychology and cognitive science of how organisms pick up on patterned regularities in their environment. Although both approaches focus on the learner's ability to use distributional properties to discover patterns in the input, the relevant research has largely been published in separate literatures and with surprisingly little cross-pollination between them. This has resulted in apparently opposing perspectives on the computations involved in learning, pitting chunk-based learning against probabilistic learning. In this paper, I trace the nearly century-long historical pedigree of the two approaches to learning and argue for their integration under the heading of "implicit statistical learning." Building on basic insights from the memory literature, I sketch a framework for statistically based chunking that aims to provide a unified basis for understanding implicit statistical learning.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory-picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test-retest reliability (r = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process.

Towards a theory of individual differences in statistical learning

In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.

Effect of pattern awareness on the behavioral and neurophysiological correlates of visual statistical learning

Statistical learning is the ability to extract predictive patterns from structured input. A common assumption is that statistical learning is a type of implicit learning that does not result in explicit awareness of learned patterns. However, there is also some evidence that statistical learning may involve explicit processing to some extent. The purpose of this study was to examine the effect of pattern awareness on behavioral and neurophysiological correlates of visual statistical learning. Participants completed a visual learning task while behavioral responses and event-related potentials were recorded. Following the completion of the task, awareness of statistical patterns was assessed through a questionnaire scored by three independent raters. Behavioral findings indicated learning only for participants exhibiting high pattern awareness levels. Neurophysiological data indicated that only the high-pattern awareness group showed expected P300 event-related potential learning effects, although there was also some indication that the low awareness groups showed a sustained mid- to late-latency negativity. Linear mixed-model analyses confirmed that only the high awareness group showed neurophysiological indications of learning. Finally, source estimation results revealed left hemispheric activation was associated with statistical learning extending from frontal to occipital and parietal regions. Further analyses suggested that left insula, left parahippocampal, and right precentral regions showed different levels of activation based on pattern awareness. To conclude, we found that pattern awareness, a dimension associated with explicit processing, strongly influences the behavioral and neurophysiological correlates of visual statistical learning.

Sequential learning in individuals with agrammatic aphasia: evidence from artificial grammar learning

We examined sequential learning in individuals with agrammatic aphasia (n = 12) and healthy age-matched participants (n = 12) using an artificial grammar. Artificial grammar acquisition, 24-hour retention, and the potential benefits of additional training were examined by administering an artificial grammar judgment test (1) immediately following auditory exposure-based training, (2) one day after training, and (3) after a second training session on the second day. An untrained control group (n = 12 healthy age-matched participants) completed the tests on the same time schedule. The trained healthy and aphasic groups showed greater sensitivity to the detection of grammatical items than the control group. No significant correlations between sequential learning and language abilities were observed among the aphasic participants. The results suggest that individuals with agrammatic aphasia show sequential learning, but the underlying processes involved in this learning may be different than for healthy adults.

Auditory access, language access, and implicit sequence learning in deaf children

Developmental psychology plays a central role in shaping evidence-based best practices for prelingually deaf children. The Auditory Scaffolding Hypothesis (Conway et al., 2009) asserts that a lack of auditory stimulation in deaf children leads to impoverished implicit sequence learning abilities, measured via an artificial grammar learning (AGL) task. However, prior research is confounded by a lack of both auditory and language input. The current study examines implicit learning in deaf children who were (Deaf native signers) or were not (oral cochlear implant users) exposed to language from birth, and in hearing children, using both AGL and Serial Reaction Time (SRT) tasks. Neither deaf nor hearing children across the three groups show evidence of implicit learning on the AGL task, but all three groups show robust implicit learning on the SRT task. These findings argue against the Auditory Scaffolding Hypothesis, and suggest that implicit sequence learning may be resilient to both auditory and language deprivation, within the tested limits. A video abstract of this article can be viewed at: https://youtu.be/EeqfQqlVHLI [Correction added on 07 August 2017, after first online publication: The video abstract link was added.].

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.

Musicians' Online Performance during Auditory and Visual Statistical Learning Tasks

Musicians' brains are considered to be a functional model of neuroplasticity due to the structural and functional changes associated with long-term musical training. In this study, we examined implicit extraction of statistical regularities from a continuous stream of stimuli-statistical learning (SL). We investigated whether long-term musical training is associated with better extraction of statistical cues in an auditory SL (aSL) task and a visual SL (vSL) task-both using the embedded triplet paradigm. Online measures, characterized by event related potentials (ERPs), were recorded during a familiarization phase while participants were exposed to a continuous stream of individually presented pure tones in the aSL task or individually presented cartoon figures in the vSL task. Unbeknown to participants, the stream was composed of triplets. Musicians showed advantages when compared to non-musicians in the online measure (early N1 and N400 triplet onset effects) during the aSL task. However, there were no differences between musicians and non-musicians for the vSL task. Results from the current study show that musical training is associated with enhancements in extraction of statistical cues only in the auditory domain.

The real-time prediction and inhibition of linguistic outcomes: Effects of language and literacy skill

Recent studies have found considerable individual variation in language comprehenders' predictive behaviors, as revealed by their anticipatory eye movements during language comprehension. The current study investigated the relationship between these predictive behaviors and the language and literacy skills of a diverse, community-based sample of young adults. We found that rapid automatized naming (RAN) was a key determinant of comprehenders' prediction ability (e.g., as reflected in predictive eye movements to a white cake on hearing "The boy will eat the white…"). Simultaneously, comprehension-based measures predicted participants' ability to inhibit eye movements to objects that shared features with predictable referents but were implausible completions (e.g., as reflected in eye movements to a white but inedible white car). These findings suggest that the excitatory and inhibitory mechanisms that support prediction during language processing are closely linked with specific cognitive abilities that support literacy. We show that a self-organizing cognitive architecture captures this pattern of results.

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.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels.

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.

Cross-Domain Statistical-Sequential Dependencies Are Difficult to Learn

Recent studies have demonstrated participants' ability to learn cross-modal associations during statistical learning tasks. However, these studies are all similar in that the cross-modal associations to be learned occur simultaneously, rather than sequentially. In addition, the majority of these studies focused on learning across sensory modalities but not across perceptual categories. To test both cross-modal and cross-categorical learning of sequential dependencies, we used an artificial grammar learning task consisting of a serial stream of auditory and/or visual stimuli containing both within- and cross-domain dependencies. Experiment 1 examined within-modal and cross-modal learning across two sensory modalities (audition and vision). Experiment 2 investigated within-categorical and cross-categorical learning across two perceptual categories within the same sensory modality (e.g., shape and color; tones and non-words). Our results indicated that individuals demonstrated learning of the within-modal and within-categorical but not the cross-modal or cross-categorical dependencies. These results stand in contrast to the previous demonstrations of cross-modal statistical learning, and highlight the presence of modality constraints that limit the effectiveness of learning in a multimodal environment.

Implicit learning and implicit treatment outcomes in individuals with aphasia

BACKGROUND

Implicit learning is a process of learning that occurs outside of conscious awareness and may be involved in implicit, exposure-based language training. However, research shows that implicit learning abilities are variable among individuals with aphasia, and it remains unknown whether individuals who show basic implicit learning abilities also benefit from implicit language training.

AIMS

The aims of this series of experiments were to test implicit learning in individuals with agrammatic aphasia, examine the effects of a novel implicit language treatment, and investigate whether individuals with aphasia who show implicit learning ability also benefit from implicit treatment focused on passive sentence comprehension.

METHODS & PROCEDURES

Nine participants with chronic agrammatic aphasia and 21 neurologically intact participants completed a visuomotor serial reaction time test of implicit learning (Experiment 1). The participants with aphasia also completed a short-term novel implicit sentence comprehension treatment (Experiment 2) that consisted of five sessions of repeated exposure to grammatically correct passive sentences and matching photographs. Sentence comprehension was tested in multiple baseline sessions and on each day of training using a sentence-picture matching task. The relation between participants' learning patterns across experiments was also examined.

OUTCOMES & RESULTS

Individuals with agrammatic aphasia as well as neurologically intact adults demonstrated significant implicit sequence learning in the serial reaction time task. However, the participants with aphasia did not show concomitant improvement in sentence comprehension as a result of the implicit treatment protocol.

CONCLUSIONS

This study suggests that individuals with agrammatic aphasia demonstrate implicit learning ability; however, this ability does not necessarily promote successful outcomes in treatment that is based solely on implicit training methods.

Where are the cookies? Two- and three-year-olds use number-marked verbs to anticipate upcoming nouns

We tested toddlers' and adults' predictive use of English subject-verb agreement. Participants saw pairs of pictures differing in number and kind (e.g., one apple, two cookies), and heard sentences with a target noun naming one of the pictures. The target noun was the subject of a preceding agreeing verb in informative trials (e.g., Wherearethe good cookies?), but not in uninformative trials (Do you see the good cookies?). In Experiment 1, 3-year-olds and adults were faster and more likely to shift their gaze from distractor to target upon hearing an informative agreeing verb. In Experiment 2, 2.5-year-olds were faster to shift their gaze from distractor to target in response to the noun in informative trials, and were more likely to be fixating the target already at noun onset. Thus, toddlers used agreeing verbs to predict number features of an upcoming noun. These data provide strong new evidence for the broad scope of predictive processing in online language comprehension.

Phonological memory and word learning deficits in children with specific language impairment: A role for perceptual context?

BACKGROUND

Sensitivity to perceptual context (anchoring) has been suggested to contribute to the development of both oral- and written-language skills, but studies of this idea in children have been rare.

AIMS

To determine whether deficient anchoring contributes to the phonological memory and word learning deficits of children with specific language impairment (SLI).

METHODS AND PROCEDURES

84 preschool children with and without SLI participated in the study. Anchoring to repeated items was evaluated in two tasks - a phonological memory task and a pseudo-word learning task.

OUTCOMES AND RESULTS

Compared to children with typical development, children with SLI had poorer phonological memory spans and learned fewer words during the word learning task. In both tasks the poorer performance of children with SLI reflected a smaller effect of anchoring that was manifested in a smaller effect of item repetition on performance. Furthermore, across the entire sample anchoring was significantly correlated with performance in vocabulary and grammar tasks.

CONCLUSIONS AND IMPLICATIONS

These findings are consistent with the hypothesis that anchoring contributes to language skills and that children with SLI have impaired anchoring, although further studies are required to determine the role of anchoring in language development.

Neural systems involved in processing novel linguistic constructions and their visual referents

In language, abstract phrasal patterns provide an important source of meaning, but little is known about whether or how such constructions are used to predict upcoming visual scenes. Findings from two fMRI studies indicate that initial exposure to a novel construction allows its semantics to be used for such predictions. Specifically, greater activity in the ventral striatum, a region sensitive to prediction errors, was linked to worse overall comprehension of a novel construction. Moreover, activity in occipital cortex was attenuated when a visual event could be inferred from a learned construction, which may reflect predictive coding of the event. These effects disappeared when predictions were unlikely: that is, when phrases provided no additional information about visual events. These findings support the idea that learners create and evaluate predictions about new instances during comprehension of novel linguistic constructions.

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.

Learning to predict is spared in mild cognitive impairment due to Alzheimer's disease

Learning the statistics of the environment is critical for predicting upcoming events. However, little is known about how we translate previous knowledge about scene regularities to sensory predictions. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are known to have spared implicit but impaired explicit recognition memory are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards oriented gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. Further, we show that executive cognitive control may account for individual variability in predictive learning. That is, we observed significant positive correlations of performance in attentional and working memory tasks with post-training performance in the prediction task. Taken together, these results suggest a mediating role of circuits involved in cognitive control (i.e. frontal circuits) that may support the ability for predictive learning in MCI-AD.

Learning temporal statistics for sensory predictions in mild cognitive impairment

Training is known to improve performance in a variety of perceptual and cognitive skills. However, there is accumulating evidence that mere exposure (i.e. without supervised training) to regularities (i.e. patterns that co-occur in the environment) facilitates our ability to learn contingencies that allow us to interpret the current scene and make predictions about future events. Recent neuroimaging studies have implicated fronto-striatal and medial temporal lobe brain regions in the learning of spatial and temporal statistics. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are characterized by hippocampal dysfunction are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards orientated gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. However, our fMRI results demonstrate that MCI-AD patients recruit an alternate circuit to hippocampus to succeed in learning of predictive structures. In particular, we observed stronger learning-dependent activations for structured sequences in frontal, subcortical and cerebellar regions for patients compared to age-matched controls. Thus, our findings suggest a cortico-striatal-cerebellar network that may mediate the ability for predictive learning despite hippocampal dysfunction in MCI-AD.

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.

Four central questions about prediction in language processing

The notion that prediction is a fundamental principle of human information processing has been en vogue over recent years. The investigation of language processing may be particularly illuminating for testing this claim. Linguists traditionally have argued prediction plays only a minor role during language understanding because of the vast possibilities available to the language user as each word is encountered. In the present review I consider four central questions of anticipatory language processing: Why (i.e. what is the function of prediction in language processing)? What (i.e. what are the cues used to predict up-coming linguistic information and what type of representations are predicted)? How (what mechanisms are involved in predictive language processing and what is the role of possible mediating factors such as working memory)? When (i.e. do individuals always predict up-coming input during language processing)? I propose that prediction occurs via a set of diverse PACS (production-, association-, combinatorial-, and simulation-based prediction) mechanisms which are minimally required for a comprehensive account of predictive language processing. Models of anticipatory language processing must be revised to take multiple mechanisms, mediating factors, and situational context into account. Finally, I conjecture that the evidence considered here is consistent with the notion that prediction is an important aspect but not a fundamental principle of language processing. This article is part of a Special Issue entitled SI: Prediction and Attention.

Exploring the neurodevelopment of visual statistical learning using event-related brain potentials

Implicit statistical learning (ISL) allows for the learning of environmental patterns and is thought to be important for many aspects of perception, cognition, and language development. However, very little is known about the development of the underlying neural mechanisms that support ISL. To explore the neurodevelopment of ISL, we investigated the event-related potential (ERP) correlates of learning in adults, older children (aged 9-12), and younger children (aged 6-9) using a novel predictor-target paradigm. In this task, which was a modification of the standard oddball paradigm, participants were instructed to view a serial input stream of visual stimuli and to respond with a button press when a particular target appeared. Unbeknownst to the participants, covert statistical probabilities were embedded in the task such that the target was predicted to varying degrees by different predictor stimuli. The results were similar across all three age groups: a P300 component that was elicited by the high predictor stimulus after sufficient exposure to the statistical probabilities. These neurophysiological findings provide evidence for developmental invariance in ISL, with adult-like competence reached by at least age 6.