Implicit Statistical Learning Research Articles

Evidence for a competitive relationship between executive functions and statistical learning

The ability of the brain to extract patterns from the environment and predict future events, known as statistical learning, has been proposed to interact in a competitive manner with prefrontal lobe-related networks and their characteristic cognitive or executive functions. However, it remains unclear whether these cognitive functions also possess a competitive relationship with implicit statistical learning across individuals and at the level of latent executive function components. In order to address this currently unknown aspect, we investigated, in two independent experiments (NStudy1 = 186, NStudy2 = 157), the relationship between implicit statistical learning, measured by the Alternating Serial Reaction Time task, and executive functions, measured by multiple neuropsychological tests. In both studies, a modest, but consistent negative correlation between implicit statistical learning and most executive function measures was observed. Factor analysis further revealed that a factor representing verbal fluency and complex working memory seemed to drive these negative correlations. Thus, the antagonistic relationship between implicit statistical learning and executive functions might specifically be mediated by the updating component of executive functions or/and long-term memory access.

Investigating the relationship between the speed of automatization and linguistic abilities: data collection during the COVID-19 pandemic

Abstract Our research explores the relationship between cognition and language. The focus of this paper is to discuss how we embarked upon remote data collection with children during the COVID-19 pandemic. In this study we investigate cognitive processes of non-verbal intelligence, working memory, implicit statistical learning, and speed of automatization (measured with the multiple-trial Tower of Hanoi puzzle). Here we focus primarily on the speed of automatization, partly because of theoretical interest, and because it is more difficult to adapt to an online format due to the motor component of the task. We established a hybrid method of data collection where the researcher was present online to guide children through a battery of language and cognitive tasks. We used a videoconferencing platform, a digital visualiser, and a physical puzzle which we posted to each child prior to commencing the research sessions. We also designed an online version of the puzzle with support from the Getting Data project. We discuss the methodology of our study and the lessons learned during remote data collection.

Cue predictiveness and uncertainty determine cue representation during visual statistical learning.

This study investigated how humans process probabilistic-associated information when encountering varying levels of uncertainty during implicit visual statistical learning. A novel probabilistic cueing validation paradigm was developed to probe the representation of cues with high (75% probability), medium (50%), low (25%), or zero levels of predictiveness in response to preceding targets that appeared with high (75%), medium (50%), or low (25%) transitional probabilities (TPs). Experiments 1 and 2 demonstrated a significant negative association between cue probe identification accuracy and cue predictiveness when these cues appeared after high-TP but not medium-TP or low-TP targets, establishing exploration-like cue processing triggered by lower-uncertainty rather than high-uncertainty inputs. Experiment 3 ruled out the confounding factor of probe repetition and extended this finding by demonstrating (1) enhanced representation of low-predictive and zero-predictive but not high-predictive cues across blocks after high-TP targets and (2) enhanced representation of high-predictive but not low-predictive and zero-predictive cues across blocks after low-TP targets for learners who exhibited above-chance awareness of cue-target transition. These results suggest that during implicit statistical learning, input characteristics alter cue-processing mechanisms, such that exploration-like and exploitation-like mechanisms are triggered by lower-uncertainty and higher-uncertainty cue-target sequences, respectively.

The relation between implicit statistical learning and proactivity as revealed by EEG

Environmental events often occur on a probabilistic basis but can sometimes be predicted based on specific cues and thus approached proactively. Incidental statistical learning enables the acquisition of knowledge about probabilistic cue-target contingencies. However, the neural mechanisms of statistical learning about contingencies (SLC), the required conditions for successful learning, and the role of implicit processes in the resultant proactive behavior are still debated. We examined changes in behavior and cortical activity during an SLC task in which subjects responded to visual targets. Unbeknown to them, there were three types of target cues associated with high-, low-, and zero target probabilities. About half of the subjects spontaneously gained explicit knowledge about the contingencies (contingency-aware group), and only they showed evidence of proactivity: shortened response times to predictable targets and enhanced event-related brain responses (cue-evoked P300 and contingent negative variation, CNV) to high probability cues. The behavioral and brain responses were strictly associated on a single-trial basis. Source reconstruction of the brain responses revealed activation of fronto-parietal brain regions associated with cognitive control, particularly the anterior cingulate cortex and precuneus. We also found neural correlates of SLC in the contingency-unaware group, but these were restricted to post-target latencies and visual association areas. Our results document a qualitative difference between explicit and implicit learning processes and suggest that in certain conditions, proactivity may require explicit knowledge about contingencies.

Neural correlates of statistical learning in developmental dyslexia: An electroencephalography study

The human brain extracts statistical regularities from the surrounding environment in a process called statistical learning. Behavioural evidence suggests that developmental dyslexia affects statistical learning. However, surprisingly few studies have assessed how developmental dyslexia affects the neural processing underlying this type of learning. We used electroencephalography to explore the neural correlates of an important aspect of statistical learning – sensitivity to transitional probabilities – in individuals with developmental dyslexia. Adults diagnosed with developmental dyslexia (n = 17) and controls (n = 19) were exposed to a continuous stream of sound triplets. Every so often, a triplet ending had a low transitional probability given the triplet’s first two sounds (“statistical deviants”). Furthermore, every so often a triplet ending was presented from a deviant location (“acoustic deviants”). We examined mismatch negativity elicited by statistical deviants (sMMN), and MMN elicited by location deviants (i.e., acoustic changes). Acoustic deviants elicited a MMN which was larger in the control group than in the developmental dyslexia group. Statistical deviants elicited a small, yet significant, sMMN in the control group, but not in the developmental dyslexia group. However, the difference between the groups was not significant. Our findings indicate that the neural mechanisms underlying pre-attentive acoustic change detection and implicit statistical auditory learning are both affected in developmental dyslexia.

Cognitive Development as a Piece of the Language Learning Puzzle

Why do children learn language more easily than adults do? This puzzle has fascinated cognitive and language scientists for decades. In the present letter, we approach the language learning puzzle from a cognitive perspective that is inspired by evidence from the perceptual and motor learning literature. Neuroscientific studies show that two memory systems in the brain are involved in human learning: an early implicit procedural memory system and a late-developing cognitive or declarative memory system. We argue that higher cognitive development constrains implicit statistical learning processes that are essential for learning patterns and regularities in languages, that is, the adult cognitive architecture has a cost. This is supported by experimental evidence showing that acquisition of implicit linguistic knowledge is enhanced under cognitive depletion in adults. More research is needed to test the cognitive cost hypothesis as it could partly solve the language learning puzzle.

Age-related differences in the statistical learning of target selection and distractor suppression.

In recent years, the use of implicit mechanisms based on statistical learning (SL) has emerged as a strong factor in biasing visuospatial attention, so that target selection is improved at frequently attended locations and distractor filtering is facilitated at frequently suppressed locations. Although these mechanisms have been consistently described in younger adults, similar evidence in healthy aging is scarce. Therefore, we studied the learning and persistence of SL of target selection and distractor suppression in younger and older adults in visual search tasks where the frequency of target (Experiment 1) or distractor (Experiment 2) was biased across spatial locations. The results show that SL of target selection was preserved in the older adults so, similar to their younger counterparts, they showed a strong and persistent advantage in target selection at locations more frequently attended. However, unlike young adults, they did not benefit from implicit SL of distractor suppression, so that distractor interference was maintained throughout the experiment independently of the contingencies associated with distractor locations. Taken together, these results provide novel evidence of distinct developmental patterns for SL of task-relevant and task-irrelevant visual information, likely reflecting differences in the implementation of proactive suppression attentional mechanisms between younger and older adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Implicit Statistical Learning in L2 Sentence Processing: Individual Cognitive Differences.

This study investigates whether statistical learning ability, conceptualized as a cognitive ability to learn regularities implicitly, is a good predictor for L2 learners' online language processing performance. Native-English-speaking adults, as a control group, and native-Korean-speaking adult L2 learners of English participated. They completed: (a) an artificial grammar learning task containing nonadjacent dependencies in sequences of non-words, to test statistical learning ability; and (b) a self-paced English reading task containing relative clauses (RC) in which the "filler" and the "gap" formed a long-distance dependency, to test language processing. Both tasks' stimuli were presented element-by-element to mimic the incremental nature of online language processing. The results for the L1 group show that higher accuracy scores on the artificial grammar learning task did not predict higher sentence comprehension scores. The results for the L2 group, however, show a marginally significant correlation between accuracy scores on the artificial grammar learning task and sentence comprehension scores. For both groups, the reading time difference between grammatical and ungrammatical items in the artificial grammar learning task did predict the speed of reading times for items with RCs with a long-distance dependency in the sentence processing task: Larger differences in RTs in the artificial grammar task correlated with slower reading at the critical region of English RCs. These findings suggest a similar mechanism for online first and second language processing of core syntactic phenomena and for statistical learning ability that involves implicitly tracking distributional relations across elements.

Concurrent visual sequence learning

Many researchers in the field of implicit statistical learning agree that there does not exist one general implicit learning mechanism, but rather, that implicit learning takes place in highly specialized encapsulated modules. However, the exact representational content of these modules is still under debate. While there is ample evidence for a distinction between modalities (e.g., visual, auditory perception), the representational content of the modules might even be distinguished by features within the same modalities (e.g., location, color, and shape within the visual modality). In implicit sequence learning, there is evidence for the latter hypothesis, as a stimulus-color sequence can be learned concurrently with a stimulus-location sequence. Our aim was to test whether this also holds true for non-spatial features within the visual modality. This has been shown in artificial grammar learning, but not yet in implicit sequence learning. Hence, in Experiment 1, we replicated an artificial grammar learning experiment of Conway and Christiansen (2006) in which participants were supposed to learn color and shape grammars concurrently. In Experiment 2, we investigated concurrent learning of sequences with an implicit sequence learning paradigm: the serial reaction time task. Here, we found evidence for concurrent learning of two sequences, a color and shape sequence. Overall, the findings converge to the assumption that implicit learning might be based on features.

What sticks after statistical learning: The persistence of implicit versus explicit memory traces

Statistical learning is a powerful mechanism that extracts even subtle regularities from our information-dense worlds. Recent theories argue that statistical learning can occur through multiple mechanisms—both the conventionally assumed automatic process that precipitates unconscious learning, and an attention-dependent process that brings regularities into conscious awareness. While this view has gained popularity, there are few empirical dissociations of the hypothesized implicit and explicit forms of statistical learning. Here we provide strong evidence for this dissociation in two ways. First, we show in healthy adults (N = 60) that implicit and explicit traces have divergent consolidation trajectories, with implicit knowledge of structure strengthened over a 24-h period, while precise explicit representations tend to decay. Second, we demonstrate that repeated testing strengthens the retention of explicit representations but that implicit statistical learning is uninfluenced by testing. Together these dissociations provide much needed support for the reconceptualization of statistical learning as a multi-component construct.

Manipulating the Rapid Consolidation Periods in a Learning Task Affects General Skills More than Statistical Learning and Changes the Dynamics of Learning.

Memory consolidation processes have traditionally been investigated from the perspective of hours or days. However, recent developments in memory research have shown that memory consolidation processes could occur even within seconds, possibly because of the neural replay of just practiced memory traces during short breaks. Here, we investigate this rapid form of consolidation during statistical learning. We aim to answer (1) whether this rapid consolidation occurs in implicit statistical learning and general skill learning, and (2) whether the duration of rest periods affects these two learning types differently. Human participants performed a widely used statistical learning task-the alternating serial reaction time (ASRT) task-that enables us to measure implicit statistical and general skill learning separately. The ASRT task consisted of 25 learning blocks with a rest period between the blocks. In a between-subjects design, the length of the rest periods was fixed at 15 or 30 s, or the participants could control the length themselves. We found that the duration of rest periods does not affect the amount of statistical knowledge acquired but does change the dynamics of learning. Shorter rest periods led to better learning during the learning blocks, whereas longer rest periods promoted learning also in the between-block rest periods, possibly because of the higher amount of replay. Moreover, we found weaker general skill learning in the self-paced group than in the fixed rest period groups. These results suggest that distinct learning processes are differently affected by the duration of short rest periods.

Frequency-based neural discrimination in fast periodic visual stimulation

Humans capitalize on statistical cues to discriminate fundamental units of information within complex streams of sensory input. We sought neural evidence for this phenomenon by combining fast periodic visual stimulation (FPVS) and EEG recordings. Skilled readers were exposed to sequences of linguistic items with decreasing familiarity, presented at a fast rate and periodically interleaved with oddballs. Crucially, each sequence comprised stimuli of the same category, and the only distinction between base and oddball items was the frequency of occurrence of individual tokens within a stream. Frequency-domain analyses revealed robust neural responses at the oddball presentation rate in all conditions, reflecting the discrimination between two locally-emerged groups of items purely informed by token frequency. Results provide evidence for a fundamental frequency-tuned mechanism that operates under high temporal constraints and could underpin category bootstrapping. Concurrently, they showcase the potential of FPVS for providing a direct neural measure of implicit statistical learning.

Effects of healthy aging and left hemisphere stroke on statistical language learning

ABSTRACT Spoken sentences are continuous streams of sound, without reliable acoustic cues to word boundaries. We have previously proposed that language learners identify words via an implicit statistical learning mechanism that computes transitional probabilities between syllables. Neuroimaging studies in healthy young adults associate this learning with left inferior frontal gyrus, left arcuate fasciculus, and bilateral striatum. Here, we test the effects of healthy aging and left hemisphere (LH) injury on statistical learning. Following 10-minute exposure to an artificial language, participants rated familiarity of Words, Part-words (sequences spanning word boundaries), and Non-words (unfamiliar sequences). Young controls (N = 14) showed robust learning, rating Words > Part-words > Non-words. Older controls (N = 28) showed this pattern to a weaker degree. Stroke survivors (N = 24) as a group showed no learning. A lesion comparison examining individual differences revealed that “non-learners” are more likely to have anterior lesions. Together, these findings demonstrate that word segmentation is sensitive to healthy aging and LH injury.

Unlocking adults’ implicit statistical learning by cognitive depletion

Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.

Language production and implicit statistical learning in typical development and children with acquired language disorders: an exploratory study

ABSTRACT Statistical properties of language provide important cues for language learning and may be processed by domain-general cognitive systems. We investigated the relationship between implicit statistical learning (the unconscious detection of statistical regularities in input) and language production. Twenty typically developing (TD) children and nine children with acquired language disorders (ALD) (aged 6–18 years) took part in a Boston Cookie Theft picture description task. Using a computerized analysis, we investigated statistical properties, such as usage frequency of words and collocation strength of word combinations. Participants also completed a non-linguistic serial reaction time (SRT) task, which tested non-verbal, implicit statistical learning in the visual-motor modality. We determined age effects, and compared language production and SRT performance between both groups. Older TD children produced more connected language, more words, less frequent function words, more rare or novel combinations, and showed better statistical learning. Children with ALD produced less connected language, more weakly collocated combinations, displayed less lexical diversity and showed poorer statistical learning. Post-hoc analyses found correlations between statistical learning and statistical properties of spoken language. Given the rarity and heterogeneity of children with ALD, group size was small and the study should be considered exploratory. However, we note that results are compatible with the view that language production draws on statistical learning and that impairment of statistical learning can be related to language disorders.

Implicit Statistical Learning in Naturalistic and Instructed Morphosyntactic Attainment: An Aptitude‐Treatment Interaction Design

AbstractWe investigated the potential influence of implicit learning mechanisms on L2 morphosyntactic attainment by examining the relationship between age of onset (AoO), two cognitive abilities hypothesized to underlie implicit learning (phonological short‐term memory and implicit statistical learning), and performance on an auditory grammaticality judgment test (GJT). Participants were 71 Polish‐English long‐term bilinguals with a wide range of AoOs (1–35 years) who differed in their context of learning and use (immersed vs. instructed). In immersed learners, we observed a growing dissociation between performance on grammatical and ungrammatical sentences as AoO was delayed. This effect was attenuated in those with better phonological short‐term memory and statistical learning abilities and is consistent with a decline in the ability to learn from implicit negative evidence. In instructed learners, GJT performance was subject to additive effects of AoO and grammaticality and was not associated with either cognitive predictor, suggesting that implicit learning mechanisms were not involved.

Strategic Distractor Suppression Improves Selective Control in Human Vision.

Our visual environment is complicated, and our cognitive capacity is limited. As a result, we must strategically ignore some stimuli to prioritize others. Common sense suggests that foreknowledge of distractor characteristics, like location or color, might help us ignore these objects. But empirical studies have provided mixed evidence, often showing that knowing about a distractor before it appears counterintuitively leads to its attentional selection. What has looked like strategic distractor suppression in the past is now commonly explained as a product of prior experience and implicit statistical learning, and the long-standing notion the distractor suppression is reflected in α band oscillatory brain activity has been challenged by results appearing to link α to target resolution. Can we strategically, proactively suppress distractors? And, if so, does this involve α? Here, we use the concurrent recording of human EEG and eye movements in optimized experimental designs to identify behavior and brain activity associated with proactive distractor suppression. Results from three experiments show that knowing about distractors before they appear causes a reduction in electrophysiological indices of covert attentional selection of these objects and a reduction in the overt deployment of the eyes to the location of the objects. This control is established before the distractor appears and is predicted by the power of cue-elicited α activity over the visual cortex. Foreknowledge of distractor characteristics therefore leads to improved selective control, and α oscillations in visual cortex reflect the implementation of this strategic, proactive mechanism.SIGNIFICANCE STATEMENT To behave adaptively and achieve goals we often need to ignore visual distraction. Is it easier to ignore distracting objects when we know more about them? We recorded eye movements and electrical brain activity to determine whether foreknowledge of distractor characteristics can be used to limit processing of these objects. Results show that knowing the location or color of a distractor stops us from attentionally selecting it. A neural signature of this inhibition emerges in oscillatory alpha band brain activity, and when this signal is strong, selective processing of the distractor decreases. Knowing about the characteristics of task-irrelevant distractors therefore increases our ability to focus on task-relevant information, in this way gating information processing in the brain.

Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge

Humans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected only in the structured condition, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in areas around the left pre-central gyrus as well as right temporo-frontal areas significantly predicted behavioral performance. Sensor level results confirmed this relationship between neural entrainment and subsequent explicit knowledge. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two aspects are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on factors such as sufficient exposure time and attention.

THE CONTRIBUTIONS OF IMPLICIT-STATISTICAL LEARNING APTITUDE TO IMPLICIT SECOND-LANGUAGE KNOWLEDGE

AbstractThis study addresses the role of domain-general mechanisms in second-language learning and knowledge using an individual differences approach. We examine the predictive validity of implicit-statistical learning aptitude for implicit second-language knowledge. Participants (n = 131) completed a battery of four aptitude measures and nine grammar tests. Structural equation modeling revealed that only the alternating serial reaction time task (a measure of implicit-statistical learning aptitude) significantly predicted learners’ performance on timed, accuracy-based language tests, but not their performance on reaction-time measures. These results inform ongoing debates about the nature of implicit knowledge in SLA: they lend support to the validity of timed, accuracy-based language tests as measures of implicit knowledge. Auditory and visual statistical learning were correlated with medium strength, while the remaining implicit-statistical learning aptitude measures were not correlated, highlighting the multicomponential nature of implicit-statistical learning aptitude and the corresponding need for a multitest approach to assess its different facets.

Task-induced brain functional connectivity as a representation of schema for mediating unsupervised and supervised learning dynamics in language acquisition.

IntroductionBased on the schema theory advanced by Rumelhart and Norman, we shed light on the individual variability in brain dynamics induced by hybridization of learning methodologies, particularly alternating unsupervised learning and supervised learning in language acquisition. The concept of “schema” implies a latent knowledge structure that a learner holds and updates as intrinsic to his or her cognitive space for guiding the processing of newly arriving information.MethodsWe replicated the cognitive experiment of Onnis and Thiessen on implicit statistical learning ability in language acquisition but included additional factors of prosodic variables and explicit supervised learning. Functional magnetic resonance imaging was performed to identify the functional network connections for schema updating by alternately using unsupervised and supervised artificial grammar learning tasks to segment potential words.ResultsRegardless of the quality of task performance, the default mode network represented the first stage of spontaneous unsupervised learning, and the wrap‐up accomplishment for successful subjects of the whole hybrid learning in concurrence with the task‐related auditory language networks. Furthermore, subjects who could easily “tune” the schema for recording a high task precision rate resorted even at an early stage to a self‐supervised learning, or “superlearning,” as a set of different learning mechanisms that act in synergy to trigger widespread neuro‐transformation with a focus on the cerebellum.ConclusionsInvestigation of the brain dynamics revealed by functional connectivity imaging analysis was able to differentiate the synchronized neural responses with respect to learning methods and the order effect that affects hybrid learning.