Statistical Learning Task Research Articles

Multi-Pattern Visual Statistical Learning in Monolinguals and Bilinguals.

To date, the impact of bilingualism on statistical learning remains unclear. Here we test a novel visual statistical learning task that affords simultaneous learning of two types of regularities: co-occurrence regularities between pairs of elements and the co-occurrence of visual features that could define categories. We compared performance by English monolinguals, Spanish-Catalan bilinguals and Spanish-English bilinguals, as previous studies have suggested that bilinguals might be more open than monolinguals to the presence of multiple regularities, though no previous studies have tested the learning of multiple patterns within a single task. We demonstrated that both monolingual and bilingual participants could learn the co-occurrence probabilities and the features that define categories. To the best of our knowledge, this study is the first to demonstrate that learners can extract co-occurrence regularities along two dimensions in the visual modality. However, we did not detect significant differences in performance across groups. We close by discussing the implications for the growing literature on bilingualism and statistical learning.

Synesthetes perseverate in implicit learning: Evidence from a non-stationary statistical learning task.

Synesthetes automatically and consistently experience additional sensory or cognitive perceptions in response to particular environmental stimuli. Recent evidence suggests that the propensity to develop synesthesia is genetic while the particular associations experienced by a given synesthete are influenced by learning. Despite the potential role of implicit learning in the formation of synesthetic associations, there has been minimal investigation of synesthetes' implicit learning abilities. In this study, we examine linguistic-colour synesthetes' ability to implicitly learn from and adjust to non-stationary statistics in a domain unrelated to their particular form of synesthesia. Engaging participants in a computer game Whack-the-mole, we utilise the online measure of reaction time to assess the time course of learning. Participants are exposed to "worlds" of probabilities that, unbeknownst to them, undergo unannounced changes, creating unpredictable statistical shifts devoid of accompanying cues. The same small set of probability worlds are repeated throughout the experiment to investigate participants' ability to retain and learn from this repetitive probabilistic information. The reaction time data provide evidence that synesthetes require more information than nonsynesthetes to benefit from the non-stationary probability distributions. These findings demonstrate that linguistic-colour synesthetes' implicit learning abilities-in a domain far from their synesthetic experiences-differ from those of nonsynesthetes.

Individual differences in statistical learning predict children's reading ability in a semi-transparent orthography

Learning to read is a milestone in a child's life, and reading ability is a strong predictor of academic outcomes. Some studies have revealed that individual differences in the capacity for implicit statistical learning are linked with children's reading skills in English, which has a deep orthography, but we do not know whether the same relation is found in semi-transparent orthographies such as Norwegian. Additionally, the relative contribution of statistical learning alongside other cognitive measures known to be related to reading has not been examined. This study of sixty-five Norwegian school children (M = 10.3 years) found that performance on an independent statistical learning task predicted a small, but unique amount of variance in reading ability in Norwegian, even when a number of other reading-related cognitive skills were taken into account. This suggests that children's implicit learning of statistical regularities is associated with proficiency with written language in a semi-transparent orthography such as Norwegian.

Statistical learning as a predictor of attention bias modification outcome: A preliminary study among socially anxious patients

Attention bias modification (ABM) is a novel therapy designed to modulate attentional biases towards threat typically observed among anxious individuals. Bias modification is allegedly achieved via extraction of a statistical regularity embedded within the treatment task. However, no prior study examined prediction of ABM therapeutic response in relation to patients' capacity to extract statistical properties from the environment, a capacity known as “statistical learning”. Here, 30 treatment-seeking patients with social anxiety disorder completed a gold-standard statistical learning task at baseline and then received six sessions of ABM therapy. Results indicate that baseline statistical learning capacity predicts treatment outcome: the better patients' statistical learning capacity, the greater their reduction in clinician-rated and self-reported social anxiety symptoms. Restricted capacities for statistical learning could account for the moderate effect sizes of ABM therapy in clinical trials. Poor response may occur in patients who fail to extract the underlying contingency embedded in ABM.

Auditory perception is associated with implicit language learning and receptive language ability in autism spectrum disorder

BackgroundAutism spectrum disorder (ASD) is associated with language impairment as well as atypical auditory sensory processing. The current study investigated associations among auditory perception, implicit language learning and receptive language ability in youth with ASD. MethodsWe measured auditory event related potentials (ERP) during an artificial language statistical learning task in 76 youth with ASD and 27 neurotypical (NT) controls. Participants with ASD had a broad range of cognitive and language abilities. ResultsNT youth showed evidence of implicit learning via attenuated P1 amplitude in the left hemisphere. In contrast, among youth with ASD, implicit learning elicited bilateral attenuation that was increasingly evident with greater receptive language skill. ConclusionsEfficient early auditory perception reflects language learning and is a marker of language ability among youth with ASD. Atypical lateralization of word learning is evident in ASD across a broad range of receptive language abilities.

Statistical Learning Impairments as a Consequence of Stroke.

Statistical learning is the implicit learning of the contingencies between sequential stimuli, typically from mere exposure. It is present from infancy onward, and plays a role in functions from language learning to selective attention. Despite these observations, there are few data on whether statistical learning capacity changes with age or after brain injury. In order to examine how brain injury affects the ability to learn and update statistical representations, we had young control and healthy elder participants, as well as participants with either left or right brain injury, perform an auditory statistical learning task. Participants listened to two languages with made-up words that were defined by the transition probability between syllables. Following passive listening, learning was assessed with a two-alternative forced choice test for the most familiar word. As in previous studies, we found that young controls have a learning capacity limitation for statistical learning; a second language is less well learned than the first, and this statistical learning capacity limit is attenuated with age. Additionally, we found that brain damaged patients, whether with left or right hemispheric damage, showed impaired statistical learning. This impairment was not explained by aphasia or cognitive deficits. As statistical learning is a critical skill for daily life, a better appreciation of the nature of this impairment will improve our understanding of the cognitive effects of brain injury and could lead to new rehabilitation strategies.

Neuroimaging evidence for sensitivity to orthography-to-phonology conversion in native readers and foreign learners of Chinese

Abstract Through recent neuroimaging research into brain mechanisms of proficient reading and literacy acquisition in different languages, a common neural network supporting reading has been identified in native readers across various writing systems. However, whether the same or different brain regions are involved in learning to read a foreign language is largely unexplored. To investigate (1) neural correlates of literacy acquisition of Chinese in adults whose learning of this logographic language was relatively late, and (2) to examine which cognitive factors might be predictors of literacy acquisition that would modulate the computation demands on reading-related brain regions, native and non-native Chinese readers were recruited to participate in pronunciation and color verification tasks using Chinese pseudo-phonograms in fMRI while their sensitivity to extracting systematic regularity in nonverbal materials, as well as their IQ and working memory, was measured in a visual statistical learning (VSL) task. The results indicated that native participants activated a left lateralized reading network that is consistent with previous research on orthography-to-phonology conversion (OPC) of Chinese, while a similar but extensive network that also includes regions in the right hemisphere was engaged in the non-natives. Within this network, left inferior frontal sites were found to be crucial to the mapping of Chinese pseudo characters to potential sounds especially in non-natives. More important, only the VSL scores of native and non-native participants, but not their general cognitive abilities, were negatively correlated with the brain activities in the left inferior parietal and left inferior frontal regions, respectively, suggesting that the fundamental capacity of SL supports literacy acquisition through modulating computation demands on the brain regions associated with OPC processing, which is critical to Chinese character recognition.

Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation.

Low-rank matrix estimation arises in a number of statistical and machine learning tasks. In particular, the coefficient matrix is considered to have a low-rank structure in multivariate linear regression and multivariate quantile regression. In this paper, we propose a method called penalized matrix least squares approximation (PMLSA) toward a unified yet simple low-rank matrix estimate. Specifically, PMLSA can transform many different types of low-rank matrix estimation problems into their asymptotically equivalent least-squares forms, which can be efficiently solved by a popular matrix fast iterative shrinkage-thresholding algorithm. Furthermore, we derive analytic degrees of freedom for PMLSA, with which a Bayesian information criterion (BIC)-type criterion is developed to select the tuning parameters. The estimated rank based on the BIC-type criterion is verified to be asymptotically consistent with the true rank under mild conditions. Extensive experimental studies are performed to confirm our assertion.

Is Statistical Learning Ability Related to Reading Ability, and If So, Why?

ABSTRACTPrevious studies found a relationship between performance on statistical learning (SL) tasks and reading ability and developmental dyslexia. Thus, it has been suggested that the ability to implicitly learn patterns may be important for reading acquisition. Causal mechanisms behind this relationship are unclear: Although orthographic sensitivity to letter bigrams may emerge through SL and facilitate reading, there is no empirical support for this link. We test 84 adults on two SL tasks, reading tests, and a bigram sensitivity task. We test for correlations using Bayes factors. This serves to test the prediction that SL and reading ability are correlated and to explore sensitivity to bigram legality as a potential mediator. We find no correlations between SL tasks and reading ability, SL and bigram sensitivity, or between the SL tasks. We conclude that correlating SL with reading ability may not yield replicable results, partly due to low correlations between SL tasks.

Statistical Learning is Associated with Autism Symptoms and Verbal Abilities in Young Children with Autism.

Statistical learning-extracting regularities in the environment-may underlie complex social behavior. 124 children, 56 with autism and 68 typically developing, ages 2-8 years, completed a novel visual statistical learning task on an iPad. Averaged together, children with autism demonstrated less learning on the task compared to typically developing children. However, multivariate classification analyses characterized individual behavior patterns, and demonstrated a subset of children with autism had similar learning patterns to typically developing children and that subset of children had less severe autism symptoms. Therefore, statistically averaging data resulted in missing critical heterogeneity. Variability in statistical learning may help to understand differences in autism symptoms across individuals and could be used to tailor and inform treatment decisions.

Linguistic entrenchment: Prior knowledge impacts statistical learning performance

Statistical Learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying statistical regularities in the input. Recent findings, however, show clear differences in processing regularities across modalities and stimuli as well as low correlations between performance on visual and auditory tasks. Why does a presumably domain-general mechanism show distinct patterns of modality and stimulus specificity? Here we claim that the key to this puzzle lies in the prior knowledge brought upon by learners to the learning task. Specifically, we argue that learners’ already entrenched expectations about speech co-occurrences from their native language impacts what they learn from novel auditory verbal input. In contrast, learners are free of such entrenchment when processing sequences of visual material such as abstract shapes. We present evidence from three experiments supporting this hypothesis by showing that auditory-verbal tasks display distinct item-specific effects resulting in low correlations between test items. In contrast, non-verbal tasks – visual and auditory – show high correlations between items. Importantly, we also show that individual performance in visual and auditory SL tasks that do not implicate prior knowledge regarding co-occurrence of elements, is highly correlated. In a fourth experiment, we present further support for the entrenchment hypothesis by showing that the variance in performance between different stimuli in auditory-verbal statistical learning tasks can be traced back to their resemblance to participants' native language. We discuss the methodological and theoretical implications of these findings, focusing on models of domain generality/specificity of SL.

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.

The Necessity of the Hippocampus for Statistical Learning.

Converging evidence points to a role for the hippocampus in statistical learning, but open questions about its necessity remain. Evidence for necessity comes from Schapiro and colleagues who report that a single patient with damage to hippocampus and broader medial temporal lobe cortex was unable to discriminate new from old sequences in several statistical learning tasks. The aim of the current study was to replicate these methods in a larger group of patients who have either damage localized to hippocampus or broader medial temporal lobe damage, to ascertain the necessity of the hippocampus in statistical learning. Patients with hippocampal damage consistently showed less learning overall compared with healthy comparison participants, consistent with an emerging consensus for hippocampal contributions to statistical learning. Interestingly, lesion size did not reliably predict performance. However, patients with hippocampal damage were not uniformly at chance and demonstrated above-chance performance in some task variants. These results suggest that hippocampus is necessary for statistical learning levels achieved by most healthy comparison participants but significant hippocampal pathology alone does not abolish such learning.

Mass volume curves and anomaly ranking

This paper aims at formulating the issue of ranking multivariate unlabeled observations depending on their degree of abnormality as an unsupervised statistical learning task. In the 1-d situation, this problem is usually tackled by means of tail estimation techniques: univariate observations are viewed as all the more ‘abnormal’ as they are located far in the tail(s) of the underlying probability distribution. It would be desirable as well to dispose of a scalar valued ‘scoring’ function allowing for comparing the degree of abnormality of multivariate observations. Here we formulate the issue of scoring anomalies as a M-estimation problem by means of a novel functional performance criterion, referred to as the Mass Volume curve (MV curve in short), whose optimal elements are strictly increasing transforms of the density almost everywhere on the support of the density. We first study the statistical estimation of the MV curve of a given scoring function and we provide a strategy to build confidence regions using a smoothed bootstrap approach. Optimization of this functional criterion over the set of piecewise constant scoring functions is next tackled. This boils down to estimating a sequence of empirical minimum volume sets whose levels are chosen adaptively from the data, so as to adjust to the variations of the optimal MV curve, while controlling the bias of its approximation by a stepwise curve. Generalization bounds are then established for the difference in sup norm between the MV curve of the empirical scoring function thus obtained and the optimal MV curve.

Commentary: Musicians' Online Performance during Auditory and Visual Statistical Learning Tasks.

A commentary on: Musicians' Online Performance during Auditory and Visual Statistical Learning Tasks by Mandikal Vasuki, P. R., Sharma, M., Ibrahim, R. K., and Arciuli, J. (2017). Front. Hum. Neurosci. 11:114. doi: 10.3389/fnhum.2017.00114 Statistical learning (SL) is the extraction of the underlying statistical structure from sensory input (Frost et al., 2015). The extent to which this ability is domain-general (with a single central mechanism underpinning SL in any modality) or domain-specific (where the SL mechanism differs by modality) remains a central question in statistical learning (Frost et al., 2015), and two approaches have been adopted to tackle this. First is to examine the extent to which predominantly domain-specific skills such as language proficiency (Arciuli and von Koss Torkildsen, 2012) and musical expertise (Schon and Francois, 2011), and domain-general skills such as working memory and general IQ (Siegelman and Frost, 2015), correlate with SL ability. Second is to compare SL performance across modalities, or even examine cross-modal transfer (Durrant et al., 2016). Mandikal Vasuki et al. (2017) (and the sister paper: Mandikal Vasuki et al., 2016) make an important contribution by adopting both of these approaches. They compare auditory and visual SL using the Saffran triplet learning paradigm (Saffran et al., 1999) in musicians and non-musicians. The three key findings are that musicians are better than non-musicians at segmentation of auditory stimuli only, there is no correlation between auditory and visual performance, and that auditory performance is better overall. This last result could be due to privileged auditory processing of sequential stimuli (Conway et al., 2009), or it could just reflect differences in perceptual or memory capabilities across modalities. However, the fact that SL performance in one modality does not predict performance in another is hard to explain if a single mechanism underlying both is posited. Combined with the fact that overall better performance was found in musicians only in the auditory modality, a domain-specific SL mechanism seems to offer the most parsimonious explanation of this data.

PAC-Bayesian high dimensional bipartite ranking

This paper is devoted to the bipartite ranking problem, a classical statistical learning task, in a high dimensional setting. We propose a scoring and ranking strategy based on the PAC-Bayesian approach. We consider nonlinear additive scoring functions, and we derive non-asymptotic risk bounds under a sparsity assumption. In particular, oracle inequalities in probability holding under a margin condition assess the performance of our procedure, and prove its minimax optimality. An MCMC-flavored algorithm is proposed to implement our method, along with its behavior on synthetic and real-life datasets.

The developmental trajectory of children's auditory and visual statistical learning abilities: modality-based differences in the effect of age.

Infants, children and adults are capable of extracting recurring patterns from their environment through statistical learning (SL), an implicit learning mechanism that is considered to have an important role in language acquisition. Research over the past 20 years has shown that SL is present from very early infancy and found in a variety of tasks and across modalities (e.g., auditory, visual), raising questions on the domain generality of SL. However, while SL is well established for infants and adults, only little is known about its developmental trajectory during childhood, leaving two important questions unanswered: (1) Is SL an early-maturing capacity that is fully developed in infancy, or does it improve with age like other cognitive capacities (e.g., memory)? and (2) Will SL have similar developmental trajectories across modalities? Only few studies have looked at SL across development, with conflicting results: some find age-related improvements while others do not. Importantly, no study to date has examined auditory SL across childhood, nor compared it to visual SL to see if there are modality-based differences in the developmental trajectory of SL abilities. We addressed these issues by conducting a large-scale study of children's performance on matching auditory and visual SL tasks across a wide age range (5-12y). Results show modality-based differences in the development of SL abilities: while children's learning in the visual domain improved with age, learning in the auditory domain did not change in the tested age range. We examine these findings in light of previous studies and discuss their implications for modality-based differences in SL and for the role of auditory SL in language acquisition. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=3kg35hoF0pw.

Selective social learning in infancy: looking for mechanisms.

Although there is mounting evidence that selective social learning begins in infancy, the psychological mechanisms underlying this ability are currently a controversial issue. The purpose of this study is to investigate whether theory of mind abilities and statistical learning skills are related to infants' selective social learning. Seventy-seven 18-month-olds were first exposed to a reliable or an unreliable speaker and then completed a word learning task, two theory of mind tasks, and a statistical learning task. If domain-general abilities are linked to selective social learning, then infants who demonstrate superior performance on the statistical learning task should perform better on the selective learning task, that is, should be less likely to learn words from an unreliable speaker. Alternatively, if domain-specific abilities are involved, then superior performance on theory of mind tasks should be related to selective learning performance. Findings revealed that, as expected, infants were more likely to learn a novel word from a reliable speaker. Importantly, infants who passed a theory of mind task assessing knowledge attribution were significantly less likely to learn a novel word from an unreliable speaker compared to infants who failed this task. No such effect was observed for the other tasks. These results suggest that infants who possess superior social-cognitive abilities are more apt to reject an unreliable speaker as informant. A video abstract of this article can be viewed at: https://youtu.be/zuuCniHYzqo.

Dynamics of EEG functional connectivity during statistical learning

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning.

Infant statistical-learning ability is related to real-time language processing.

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests that one way statistical learning ability affects language proficiency is by facilitating real-time language processing. Here we tested whether 15-month-olds' ability to learn sequential statistical structure in artificial language materials is related to their ability to encode and interpret native-language speech. Specifically, we tested their ability to learn sequential structure among syllables (Experiment 1) and words (Experiment 2), as well as their ability to encode familiar English words in sentences. The results suggest that infants' ability to learn sequential structure among syllables is related to their lexical-processing efficiency, providing continuity with findings from children and adults, though effects were modest.