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Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a “suite” of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

One important aspect of human cognition involves the learning of structured information encountered in our environment, a phenomenon known as statistical learning. A growing body of research suggests that learning to read print is partially guided by learning the statistical contingencies existing between the letters within a word, and also between the letters and sounds to which the letters refer. Research also suggests that impairments to statistical learning ability may at least partially explain the difficulties experienced by individuals diagnosed with dyslexia. However, the findings regarding impaired learning are not consistent, perhaps partly due to the varied use of methodologies across studies – such as differences in the learning paradigms, stimuli used, and the way that learning is assessed – as well as differences in participant samples such as age and extent of the learning disorder. In this review, we attempt to examine the purported link between statistical learning and dyslexia by assessing a set of the most recent and relevant studies in both adults and children. Based on this review, we conclude that although there is some evidence for a statistical learning impairment in adults with dyslexia, the evidence for an impairment in children is much weaker. We discuss several suggestive trends that emerge from our examination of the research, such as issues related to task heterogeneity, possible age effects, the role of publication bias, and other suggestions for future research such as the use of neural measures and a need to better understand how statistical learning changes across typical development. We conclude that no current theoretical framework of dyslexia fully captures the extant research findings on statistical learning.

This study investigated the role of sequential processing in spoken language outcomes for children who are deaf or hard of hearing (DHH), ages 5;3–11;4, by comparing them to children with typical hearing (TH), ages 6;3–9;7, on sequential learning and memory tasks involving easily nameable and difficult-to-name visual stimuli. Children who are DHH performed more poorly on easily nameable sequencing tasks, which positively predicted receptive vocabulary scores. Results suggest sequential learning and memory may underlie delayed language skills of many children who are DHH. Implications for language development in children who are DHH are discussed.

The expression of motion shows strong crosslinguistic variability; however, less is known about speakers' expectancies for lexicalizations of motion at the neural level. We examined event-related brain potentials (ERPs) in native English or Spanish speakers while they read grammatical sentences describing animations involving manner and path components of motion that did or did not violate language-specific patterns of expression. ERPs demonstrated different expectancies between speakers: Spanish speakers showed higher expectancies for motion verbs to encode path and English speakers showed higher expectancies for motion verbs to encode manner followed by a secondary path expression. Interestingly, grammatical but infrequent motion expressions (manner verbs in Spanish, path verbs and secondary manner expressions in English) elicited semantic P600 rather than the expected N400 effects-with or without post-N400 positivities-that are typically associated with semantic processing. Overall, our findings provide the first empirical evidence for the effect of crosslinguistic variation in processing motion event descriptions at the neural level.

Objective:Right cerebellar-left frontal (RC-LF) white matter integrity (WMI) has been associated with working memory. However, prior studies have employed measures of working memory that include processing speed and attention. We examined the relationships between the RC-LF WMI and processing speed, attention, and working memory to clarify the relationship of RC-LF WMI with a specific cognitive function. Right superior longitudinal fasciculus II (SLF II) WMI and visual attention were included as a negative control tract and task to demonstrate a double dissociation.Methods:Adult survivors of childhood brain tumors [n = 29, age: M = 22 years (SD = 5), 45% female] and demographically matched controls were recruited (n = 29). Tests of auditory attention span, working memory, and visual attention served as cognitive measures. Participants completed a 3-T MRI diffusion-weighted imaging scan. Fractional anisotropy (FA) and radial diffusivity (RD) served as WMI measures. Partial correlations between WMI and cognitive scores included controlling for type of treatment.Results:A correlational double dissociation was found. RC-LF WMI was associated with auditory attention (FA: r = .42, p = .03; RD: r = −.50, p = .01) and was not associated with visual attention (FA: r = −.11, p = .59; RD: r = −.11, p = .57). SLF II FA WMI was associated with visual attention (FA: r = .44, p = .02; RD: r = −.17, p = .40) and was not associated with auditory attention (FA: r = .24, p = .22; RD: r = −.10, p = .62).Conclusions:The results show that RC-LF WMI is associated with auditory attention span rather than working memory per se and provides evidence for a specificity based on the correlational double dissociation.

Statistical learning plays an important role in the acquisition of spoken and written language. It has been proposed that impaired or atypical statistical learning may be linked with language difficulties in developmental disabilities. However, research on statistical learning in individuals with developmental disabilities such as autism spectrum disorder, dyslexia, and specific language impairment, and in individuals with cochlear implants, has produced divergent findings. It is unclear whether, and to what extent, statistical learning is impaired or atypical in each of these developmental disabilities. We suggest that these disparate findings point to several critical issues that must be addressed before we can evaluate the role of statistical learning in atypical child development. While the issues we outline are interrelated, we propose four key points relating to (a) the nature of statistical learning, (b) the myriad of ways in which statistical learning can be measured, (c) our lack of understanding regarding the developmental trajectory of statistical learning, and (d) the role of individual differences. We close by making suggestions that we believe will be helpful in moving the field forward and creating new synergies among researchers, clinicians, and educators to better support language learners.

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman, 1993; Newport, 1990). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of simple recursive grammars: right-branching and center-embedding, with recursive embedded clauses in fixed positions and fixed length. This effect was replicated in Experiment 2 (N=100). In Experiment 3 and 4, we used a more complex center-embedded grammar with recursive loops in variable positions, producing strings of variable length. When participants were presented an incremental ordering of training stimuli, as in natural language, they were better able to generalize their knowledge of simple units to more complex units when the training input “grew” according to structural complexity, compared to when it “grew” according to string length. Overall,the results suggest that starting small confers an advantage for learning complex center-embedded structures when the input is organized according to structural complexity.

Humans and nonhuman primates can learn about the organization of stimuli in the environment using implicit sequential pattern learning capabilities. However, most previous artificial grammar learning studies with nonhuman primates have involved relatively simple grammars and short input sequences. The goal in the current experiments was to assess the learning capabilities of monkeys on an artificial grammar-learning task that was more complex than most others previously used with nonhumans. Three experiments were conducted using a joystick-based, symmetrical-response serial reaction time task in which two monkeys were exposed to grammar-generated sequences at sequence lengths of four in Experiment 1, six in Experiment 2, and eight in Experiment 3. Over time, the monkeys came to respond faster to the sequences generated from the artificial grammar compared to random versions. In a subsequent generalization phase, subjects generalized their knowledge to novel sequences, responding significantly faster to novel instances of sequences produced using the familiar grammar compared to those constructed using an unfamiliar grammar. These results reveal that rhesus monkeys can learn and generalize the statistical structure inherent in an artificial grammar that is as complex as some used with humans, for sequences up to eight items long. These findings are discussed in relation to whether or not rhesus macaques and other primate species possess implicit sequence learning abilities that are similar to those that humans draw upon to learn natural language grammar.

The auditory scaffolding hypothesis states that early experience with sound underpins the development of domain-general sequence processing abilities, supported by studies observing impaired sequence processing in deaf or hard-of-hearing (DHH) children. To test this hypothesis, we administered a sequence processing task to 77 DHH children who use American Sign Language (ASL) and 23 hearing monolingual children aged 7–12 years and found no performance difference between them after controlling for age and nonverbal intelligence. Additionally, neither spoken language comprehension scores nor hearing loss levels predicted sequence processing scores in the DHH group, whereas ASL comprehension scores did. Our results do not indicate sequence processing deficits in DHH children and do not support the auditory scaffolding hypothesis; instead, these findings suggest that factors related to experience with and/or proficiency in an accessible language during development may be more important determinants of sequence processing abilities.

Language is acquired in part through statistical learning abilities that encode environmental regularities. Language development is also heavily influenced by social environmental factors such as socioeconomic status. However, it is unknown to what extent statistical learning interacts with SES to affect language outcomes. We measured event-related potentials in 26 children aged 8–12 while they performed a visual statistical learning task. Regression analyses indicated that children’s learning performance moderated the relationship between socioeconomic status and both syntactic and vocabulary language comprehension scores. For children demonstrating high learning, socioeconomic status had a weaker effect on language compared to children showing low learning. These results suggest that high statistical learning ability can provide a buffer against the disadvantages associated with being raised in a lower socioeconomic status household.

Sequential Learning by Touch, Vision, and Audition Christopher M. Conway (cmc82@cornell.edu) Morten H. Christiansen (mhc27@cornell.edu) Department of Psychology Cornell University Ithaca, NY 14853, USA Abstract We investigated the extent to which touch, vision, and audition are similar in the ways they mediate the processing of statistical regularities within sequential input. While previous research has examined statistical/sequential learning in the visual and auditory domains, few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored in such research. Our data reveal commonalities between the ways in which these three modalities afford the learning of sequential information. However, the data also suggest that in terms of sequential learning, audition is superior to the other two senses. We discuss these findings in terms of whether statistical/sequential learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones. Introduction The acquisition of statistical/sequential information from the environment appears to be involved in many learning situations, ranging from speech segmentation (Saffran, Newport, & Aslin, 1996), to learning orthographic regularities of written words (Pacton, Perruchet, Fayol, & Cleeremans, 2001) to processing visual scenes (Fiser & Aslin, 2002). However, previous research, focusing exclusively on visual and auditory domains, has failed to investigate whether such learning can occur via touch. Perhaps more importantly, few studies have attempted directly to compare sequential learning as it occurs across the various sensory modalities. There are important reasons to pursue such avenues of study. First, a common assumption is that statistical/sequential learning is a broad, domain- general ability (e.g., Kirkham, Slemmer, & Johnson, 2002). But in order to adequately assess this hypothesis, systematic experimentation across the modalities is necessary. If differences exist between sequential learning in the various senses, it may reflect the operation of multiple mechanisms, rather than a single process. Second, in regards to the touch modality in particular, prior research has generally focused on low- level perception; discovering that the sense of touch can accommodate complex sequential learning may have important implications for tactile communication systems. This paper describes three experiments conducted with the aim to assess sequential learning in three sensory modalities: touch, vision, and audition. Experiment 1 provides the first direct evidence for a fairly complex tactile sequential learning capability. Experiment 2 provides a visual analogue of Experiment 1 and suggests commonalities between visual and tactile sequential learning. Finally, Experiment 3 assesses the auditory domain, revealing an auditory advantage for sequential processing. We conclude by discussing these results in relation to basic issues of cognitive and neural organization—namely, to what extent sequential learning consists of a single or multiple mechanisms. Sequential Learning We define sequential learning as an ability to encode and represent the order of discrete elements occurring in a sequence (Conway & Christiansen, 2001). Importantly, we consider a crucial aspect of sequential learning to be the acquisition of statistical regularities occurring among sequence elements. Artificial grammar learning (AGL; Reber, 1967) is a widely used paradigm for studying such sequential learning 1 . AGL experiments typically use finite-state grammars to generate the stimuli; in such grammars, a transition from one state to the next produces an element of the sequence. For example, in the grammar of Figure 1, the path begins at the left-most node, labeled S1. The next transition can lead to either S 2 or S3. Every time a number is encountered in the transition between states, it is added as the next element of the sequence, producing a sequence corresponding to the rules of the grammar. For example, by passing through the nodes S1, S2, S2, S4, S3, S5, the “legal” sequence 4-1-3-5-2 is generated. During a training phase, participants typically are exposed to a subset of legal sequences—often under the guise of a “memory experiment” or some other such task—with the intent that they will incidentally encode structural aspects of the stimuli. Next, they are tested on whether they can classify novel sequences as In the typical AGL task, the stimulus elements are presented simultaneously (e.g., letter strings)—rather than sequentially (i.e., one element at a time). We consider even the former case to be a sequential learning task because scanning strings of letters generally occurs in a left-to-right, sequential manner. However, our aim here is to create a truly sequential learning environment using temporally-distributed input.

The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., “A-B”) versus nonadjacent (e.g., “A-X-B”) dependencies have different cognitive demands, but the neural correlates accompanying such processing are currently underspecified. We developed a sequential learning task in which sequences of printed nonsense syllables containing both adjacent and nonadjacent dependencies were presented. After incidentally learning these grammatical sequences, twenty-one healthy adults (age M = 22.1, 12 females) made familiarity judgments about novel grammatical sequences and ungrammatical sequences containing violations of the adjacent or nonadjacent structure while in a 3T MRI scanner. Violations of adjacent dependencies were associated with increased BOLD activation in both posterior (lateral occipital and angular gyrus) as well as frontal regions (e.g., medial frontal gyrus, inferior frontal gyrus). Initial results indicated no regions showing significant BOLD activations for the violations of nonadjacent dependencies. However, when using a less stringent cluster threshold, exploratory analyses revealed that violations of nonadjacent dependencies were associated with increased activation in subcallosal cortex, paracingulate cortex, and anterior cingulate cortex (ACC). Finally, when directly comparing the adjacent condition to the nonadjacent condition, we found significantly greater levels of activation for the right superior lateral occipital cortex (BA 19) for the adjacent relative to nonadjacent condition. In sum, the detection of violations of adjacent and nonadjacent dependencies appear to involve distinct neural networks, with perceptual brain regions mediating the processing of adjacent but not nonadjacent dependencies. These results are consistent with recent proposals that statistical-sequential learning is not a unified construct but depends on the interaction of multiple neurocognitive mechanisms acting together.

Purpose. To determine whether the effect of atypical start times and/or deadlines for tasks are due to cognitive load, or may be due to underlying temporal dissonance. Methodology. This paper develops a theoretical argument for an alternative explanation of a phenomenon observed in management research based upon a review and thought experiments about the phenomenon. Results. In several studies, it has been found that “atypical” start times and deadlines – that is, start or end times that do not occur on the quarter-hour – result in lower performance (Labianca, Moon, & Watt, 2005; Sterling, Lopez-Kidwell, Labianca, & Moon, 2013). In the Labianca et al. work, this difference is attributed to increased cognitive load, due to the way that people are taught to tell time using analogue clocks. In the Sterling et al. work, this is extended to embrace the “punctuated equilibrium” group development model (Gersick, 1988, 1989). However, the contribution of cognitive load is assumed, and not measured in these works. Further, in general, analogue methods of presenting data improve performance rather than degrade it; since quarter-hour increments in analogue clock hands correspond to 90 degree angles, the determination of appropriate deadline and transition times should actually be easier. However, if we consider the ideas of temporal focus (Shipp, Edwards, & Lambert, 2009) and deadline awareness (a component of temporal urgency) (Conte, Landy, & Mathieu, 1995; Landy, Rastegary, Thayer, & Colvin, 1991), we see that different individuals may map non-prototypical times onto different prototypical times, based upon whether they tend to be past-focused or future-focused and how deadline aware they are. When people with differing characteristics are on a team, the result is that the team may experience a form of temporal dissonance (Conway & Limayem, 2011). The theoretical contribution. This theory will lead to a better understanding of both why task start times and deadlines matter. More importantly, it leads to more refined conclusions about how to ameliorate the problem when necessary. Practical implications The practical implications are that the appropriate interventions may differ based upon whether the cause for the performance differences noted is due to cognitive load or due to temporal dissonance. For instance, always setting a task to start and/or end at a prototypical time may waste time when the team does not experience temporal dissonance. Also, reducing the cognitive load may not improve performance if the issue is temporal dissonance. Keywords: Time, time management, temporal dissonance Paper type: Theoretical paper.

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.

Purpose The authors investigated the ability of deaf children with cochlear implants (CIs) to use sentence context to facilitate the perception of spoken words. Method Deaf children with CIs ( n = 24) and an age-matched group of children with normal hearing ( n = 31) were presented with lexically controlled sentences and were asked to repeat each sentence in its entirety. Performance was analyzed at each of 3 word positions of each sentence (first, second, and third key word). Results Whereas the children with normal hearing showed robust effects of contextual facilitation—improved speech perception for the final words in a sentence—the deaf children with CIs on average showed no such facilitation. Regression analyses indicated that for the deaf children with CIs, Forward Digit Span scores significantly predicted accuracy scores for all 3 positions, whereas performance on the Stroop Color and Word Test, Children's Version (Golden, Freshwater, & Golden, 2003) predicted how much contextual facilitation was observed at the final word. Conclusions The pattern of results suggests that some deaf children with CIs do not use sentence context to improve spoken word recognition. The inability to use sentence context may be due to possible interactions between language experience and cognitive factors that affect the ability to successfully integrate temporal–sequential information in spoken language.

Research problem: The purpose of the study is to understand how affective reactions to color impact learning attitudes and outcomes in a computer-mediated learning environment. Research question: How do color differences change affective processes and outcomes in computer-mediated communication? Literature review: Several previous studies exploring particular characteristics and learning in computer-mediated environments influenced the review of the literature. The literature on color psychology indicates that color preferences and affective reactions to color can influence behaviors and attitudes. The literature on goal achievement motivation posits that affective dispositions influence goal orientation, motivation, and individual outcomes. The literature on affect infers that affective reactions are responses to events, and these reactions influence attitudes and behaviors. The current study draws on these prior studies to examine affective reaction to color and learning outcomes in a computer-mediated learning environment. Methodology: We conducted a quasiexperimental study with 79 participants, who listened to a visual presentation lecture with either blue or yellow background and then completed a survey on their affective reactions, learning attitudes, and outcomes. Results and discussion: The results of our study indicate that color is not neutral and may influence learning attitudes and outcomes and, hence, the color of computer technology interface design can influence learning outcomes. Practitioners and academics must take people's affective reactions to color into account in designs and studies of visual information presentations. The sample size and the focus on two color hues (yellow and blue) may have some limitations on the conclusions and generalizability of this study. Future studies should examine more color hues and color saturation to further our understanding of affective reactions to colors and consequent impact on attitudes and behavioral outcomes.

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.

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.

Research suggests that nonlinguistic sequence learning abilities are an important contributor to language development (Conway, Bauernschmidt, Huang, & Pisoni, 2010). The current study investigated visual sequence learning (VSL) 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 multicolored 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 VSL may have both domain-general and domain-specific associations with language learning.

Implicit statistical learning (ISL) is exclusive to neither a particular sensory modality nor a single domain of processing. Even so, differences in perceptual processing may substantially affect learning across modalities. In three experiments, statistically equivalent auditory and visual familiarizations were presented under different timing conditions that either facilitated or disrupted temporal processing (fast or slow presentation rates). We find an interaction of rate and modality of presentation: At fast rates, auditory ISL was superior to visual. However, at slow presentation rates, the opposite pattern of results was found: Visual ISL was superior to auditory. Thus, we find that changes to presentation rate differentially affect ISL across sensory modalities. Additional experiments confirmed that this modality-specific effect was not due to cross-modal interference or attentional manipulations. These findings suggest that ISL is rooted in modality-specific, perceptually based processes.

Deaf children with cochlear implants (CIs) represent an intriguing opportunity to study neurocognitive plasticity and reorganization when sound is introduced following a period of auditory deprivation early in development. Although it is common to consider deafness as affecting hearing alone, it may be the case that auditory deprivation leads to more global changes in neurocognitive function. In this paper, we investigate implicit sequence learning abilities in deaf children with CIs using a novel task that measured learning through improvement to immediate serial recall for statistically consistent visual sequences. The results demonstrated two key findings. First, the deaf children with CIs showed disturbances in their visual sequence learning abilities relative to the typically developing normal-hearing children. Second, sequence learning was significantly correlated with a standardized measure of language outcome in the CI children. These findings suggest that a period of auditory deprivation has secondary effects related to general sequencing deficits, and that disturbances in sequence learning may at least partially explain why some deaf children still struggle with language following cochlear implantation.

Across a wide range of tasks, vision appears to process input best when it is spatially rather than temporally distributed, whereas audition is the opposite. Here we explored whether such modality constraints also affect implicit statistical learning in an artificial grammar learning task. Participants were exposed to statistically governed input sequences and then tested on their ability to classify novel items. We explored three types of presentation formats*visual input distributed spatially, visual input distributed temporally, auditory input distributed temporally*and two rates of presentation: moderate (4 elements/second) and fast (8 elements/second). Overall, learning abilities were best for visual-spatial and auditory input. Additionally, at the faster presentation rate, performance declined only for the visual-temporal condition. Finally, auditory learning was mediated by increased sensitivity to the endings of input sequences, whereas vision was most sensitive to the beginnings of sequences. These results suggest that statistical learning for sequential and spatial patterns proceeds differently across the visual and auditory modalities.

Current technology enables schools to provide remote or virtual computing labs. Virtual computing labs can be implemented in multiple ways ranging from remote access to banks of dedicated workstations to sophisticated access to large-scale servers hosting virtualized workstations. This paper reports on the implementation of a specific lab using remote access to dedicated workstations and supporting students enrolled in degree programs in management. Such a lab can be implemented at relatively low cost by reallocating resources dedicated to existing physical labs.

The ability to learn and exploit environmental regularities is important for many aspects of skill learning, of which language may be a prime example. Much of such learning proceeds in an implicit fashion, that is, it occurs unintentionally and automatically and results in knowledge that is difficult to verbalize explicitly. An important research goal is to ascertain the underlying neurocognitive mechanisms of implicit learning abilities and understand its contribution to perception, language, and cognition more generally. In this article, we review recent work that investigates the extent to which implicit learning of sequential structure is mediated by stimulus-specific versus domain-general learning mechanisms. Although much of previous implicit learning research has emphasized its domain-general aspect, here we highlight behavioral work suggesting a modality-specific locus. Even so, our data also reveal that individual variability in implicit sequence learning skill correlates with performance on a task requiring sensitivity to the sequential context of spoken language, suggesting that implicit sequence learning to some extent is domain-general. Taking into consideration this behavioral work, in conjunction with recent imaging studies, we argue that implicit sequence learning and language processing are both complex, dynamic processes that partially share the same underlying neurocognitive mechanisms, specifically those that rely on the encoding and representation of phonological sequences.

Spoken language consists of a complex, sequentially arrayed signal that contains patterns that can be described in terms of statistical relations among language units. Previous research has suggested that a domain-general ability to learn structured sequential patterns may underlie language acquisition. To test this prediction, we examined the extent to which implicit sequence learning of probabilistically structured patterns in hearing adults is correlated with a spoken sentence perception task under degraded listening conditions. Performance on the sentence perception task was found to be correlated with implicit sequence learning, but only when the sequences were composed of stimuli that were easy to encode verbally. Implicit learning of phonological sequences thus appears to underlie spoken language processing and may indicate a hitherto unexplored cognitive factor that may account for the enormous variability in language outcomes in deaf children with cochlear implants. The present findings highlight the importance of investigating individual differences in specific cognitive abilities as a way to understand and explain language in deaf learners and, in particular, variability in language outcomes following cochlear implantation.

Acquiring spoken language involves implicitly learning the statistical relations among language units. In this chapter, we review recent behavioral and neurophysiological findings from our research group that illuminate the relation of this type of ‘implicit statistical learning’ (ISL) to language acquisition. First, we review evidence illustrating that ISL mechanisms enable the learner to predict upcoming language units. Second, we show modality constraints affecting the operation of ISL mechanisms, with auditory and visual learning biased to learn different types of patterns. Third, we demonstrate that under certain developmental conditions typical ISL abilities are altered, which can detrimentally affect subsequent language acquisition. These findings highlight the experience-dependent nature of ISL and its relation to typical and atypical language acquisition.

The ability to acquire spoken language depends in part on a sensitivity to the sequential regularities contained within linguistic input. In this chapter, the authors propose that language learning operates via two distinct sequence-learning processes: probabilistic sequence learning, which supports the acquisition of syntax and other structured linguistic patterns, and repetition sequence learning, which supports word learning. First, the authors review work from their lab and others illustrating that performance on tasks that require participants to learn non-linguistic sequential patterns is empirically associated with different measures of language processing. Second, they present recent work from their lab specifically highlighting the role played by probabilistic sequence learning for acquiring syntax in a sample of deaf and hard-of-hearing children. Finally, the authors demonstrate that the learning of repeating sequences is related to vocabulary development in these children. These findings suggest that there may be at least two relatively distinct domain-general sequential processing skills, with each supporting a different aspect of language acquisition.

We used event-related potentials (ERPs) to investigate the time course and distribution of brain activity while adults performed (a) a sequential learning task involving complex structured sequences, and (b) a language processing task. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic processing, was found for structural incongruencies in both sequential learning as well as natural language, and with similar topographical distributions. Additionally, a left anterior negativity (LAN) was observed for language but not for sequential learning. These results are interpreted as an indication that the P600 provides an index of violations and the cost of integration of expectations for upcoming material when processing complex sequential structure. We conclude that the same neural mechanisms may be recruited for both syntactic processing of linguistic stimuli and sequential learning of structured sequence patterns more generally.

Previous studies have suggested that both automatic and intentional processes contribute to the learning of grammar and fragment knowledge in artificial grammar learning (AGL) tasks. To explore the relative contribution of automatic and intentional processes to knowledge gained in AGL, we utilized dual-task methodology to dissociate automatic and intentional grammar- and fragment-based knowledge in AGL at both acquisition and at test. Both experiments used a balanced chunk strength grammar to assure an equal proportion of fragment cues (i.e., chunks) in grammatical and nongrammatical test items. In Experiment 1, participants engaged in a working memory dual-task either during acquisition, test, or both acquisition and test. The results showed that participants performing the dual-task during acquisition learned the artificial grammar as well as the single-task group, presumably by relying on automatic learning mechanisms. A working memory dual-task at test resulted in attenuated grammar performance, suggesting a role for intentional processes for the expression of grammatical learning at test. Experiment 2 explored the importance of perceptual cues by changing letters between the acquisition and test phase; unlike Experiment 1, there was no significant learning of grammatical information for participants under dual-task conditions in Experiment 2, suggesting that intentional processing is necessary for successful acquisition and expression of grammar-based knowledge under transfer conditions. In sum, it appears that some aspects of learning in AGL are indeed relatively automatic, although the expression of grammatical information and the learning of grammatical patterns when perceptual similarity is eliminated both appear to require explicit resources.

Deaf children with cochlear implants (CIs) represent an intriguing opportunity to study neurocognitive plasticity and reorganization when sound is introduced following a period of auditory deprivation early in development. Although it is common to consider deafness as affecting hearing alone, it may be the case that auditory deprivation leads to more global changes in neurocognitive function. In this paper, we investigate implicit sequence learning abilities in deaf children with CIs using a novel task that measured learning through improvement to immediate serial recall for statistically-consistent visual sequences. The results demonstrated two key findings. First, the deaf children with CIs showed disturbances in their visual sequence learning abilities relative to the typically-developing normal-hearing children. Second, sequence learning was significantly correlated with a standardized measure of language outcome in the CI children. These findings suggest that a period of auditory deprivation has secondary effects related to general sequencing deficits, and that disturbances in sequence learning may at least partially explain why some deaf children still struggle with language following cochlear implantation.

The bulk of clinical research on cochlear implants (CIs) has been intellectually isolated from the mainstream of current research and theory in neuroscience, cognitive psychology and developmental neuropsychology. As a consequence, the major clinical research issues have been narrowly focused on speech and language outcomes and efficacy of cochlear implantation as a medical treatment for profound hearing loss. Little basic or clinical research in the past has investigated the underlying neurobiological and neurocognitive bases of the individual differences and variability in the effectiveness of CIs. Many of the deaf children with CIs may have comorbid disturbances and/or delays in several basic neurocognitive processes that subserve information processing systems used in spoken language processing. In this chapter, we report new findings on executive function and implicit visual sequence learning in deaf children with CIs. The results of these experiments suggest that differences in neural reorganization of multiple brain systems resulting from a period of profound deafness and language delay may be responsible for the enormous variability observed in speech and language outcome measures following implantation.

A large body of clinical research over the last decade demonstrates that cochlear implants work and provide significant speech and language benefits to profoundly deaf adults and prelingually deaf children. The most challenging research problem today is that cochlear implants do not work equally well for everyone who has a profound hearing loss and cochlear implants frequently do not provide much benefit at all under highly degraded listening conditions. Some individuals do extremely well on traditional audiologic outcome measures with their cochlear implants when tested under benign listening conditions in the clinic and research laboratory while others have much more difficulty. However, all patients with cochlear implants uniformly have difficulty in a number of challenging perceptual domains such as: listening in noise, talking on the telephone, localizing sounds, recognizing familiar voices and different dialects, identifying environmental sounds and listening to music. The enormous variability in outcome and benefit following implantation is not surprising because none of the current generation of cochlear implants successfully restores normal hearing or supports robust speech perception and spoken language processing across all of these difficult and highly variable listening conditions. The traditional outcome measures of audiologic benefit were never designed to assess, understand or explain individual differences in speech perception and spoken language processing. In this chapter, we summarize recent findings that suggest several promising new directions for understanding and explaining variability in outcome and benefit after implantation. These results have implications for the design of new cochlear implants as well as the development of radically new approaches to intervention, training and habilitation following implantation.

When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.

After having been plagued for centuries by unfounded speculations, the study of language evolution is now emerging as an area of legitimate scientific inquiry. Early conjectures about the origin and evolution of language suffered from a severe lack of empirical evidence to help rein in proposed theories. This led to outlandish claims such as the idea that Chinese was the original ur-language of humankind, surviving the biblical flood because of Noah and his family (Webb, 1669, cited in Aitchison, 1998). Or, the suggestion that humans have learned how to sing and speak from the birds in the same way as they would have learned how to weave from spiders (Burnett, 1773, cited in Aitchison, 1998). Given this state of the art, it was perhaps not surprising that the influential Societe Linguistique de Paris in 1866 imposed a ban on papers discussing issues related to language origin and evolution, and effectively excluded such theorizing from the scientific discourse.

Sequential learning plays a role in a variety of common tasks, such as human language processing, animal communication, and the learning of action sequences. In this article, we investigate sequential learning in non-human primates from a comparative perspective, focusing on three areas: the learning of arbitrary, fixed sequences; statistical learning; and the learning of hierarchical structure. Although primates exhibit many similarities to humans in their performance on sequence learning tasks, there are also important differences. Crucially, non-human primates appear to be limited in their ability to learn and represent the hierarchical structure of sequences. We consider the evolutionary implications of these differences and suggest that limitations in sequential learning may help explain why non-human primates lack human-like language.

Cognitive developmental disorders cannot be properly understood without due attention to the developmental process, and we commend the authors’simulations in this regard. We note the contribution of these simulations to the nascent field of connectionist modeling of developmental disorders and outline a set of criteria for assessing individual models in the hope of furthering future modeling efforts.