Visual statistical learning in children and young adults: how implicit?

Visual linguistic statistical learning is traceable through neural entrainment

The human brain can detect statistical regularities in the environment across a wide variety of contexts. The importance of this process is well-established not just in language acquisition but across different modalities; in addition, several neural correlates of statistical learning have been identified. A current technique for tracking the emergence of regularity learning and localizing its neural background is frequency tagging (FT). FT can detect neural entrainment not only to the frequency of stimulus presentation but also to that of a hidden structure. Auditory learning paradigms with linguistic and nonlinguistic stimuli, along with a visual paradigm using nonlinguistic stimuli, have already been tested with FT. To complete the picture, we conducted an FT experiment using written syllables as stimuli and a hidden triplet structure. Both behavioral and neural entrainment data showed evidence of structure learning. In addition, we localized two electrode clusters related to the process, which spread across the frontal and parieto-occipital areas, similar to previous findings. Accordingly, we conclude that fast-paced visual linguistic regularities can be acquired and are traceable through neural entrainment. In comparison with the literature, our findings support the view that statistical learning involves a domain-general network.

Rules generalization in children with dyslexia

BACKGROUND

Rule learning (RL) is the ability to extract and generalize higher-order repetition-based structures. Children with Developmental Dyslexia (DD) often report difficulties in learning complex regularities in sequential stimuli, which might be due to the complexity of the rule to be learned. Learning high-order repetition-based rules represents a building block for the development of language skills.

AIMS

This study investigates the ability to extract and generalize simple, repetition-based visual rules (e.g., ABA) in 8-11-year-old children without (TD) and with a diagnosis of Development Dyslexia (DD) and its relationship with language and reading skills.

METHOD

Using a forced-choice paradigm, children were first exposed to a visual sequence containing a repetition-based rule (e.g., ABA) and were then asked to recognize familiar and novel rules generated by new visual elements. Standardized language and reading tests were also administered to both groups.

RESULTS

The accuracy in recognizing rules was above chance for both groups, even though DD children were less accurate than TD children, suggesting a less efficient RL mechanism in the DD group. Moreover, visual RL was positively correlated with both language and reading skills.

CONCLUSION

These results further confirm the crucial role of RL in the acquisition of linguistic skills and mastering reading abilities.

Gamma oscillations in visual statistical learning correlate with individual behavioral differences

Statistical learning is assumed to be a fundamentally general sensory process across modalities, age, other cognitive functions, and even species. Despite this general role, behavioral testing on regularity acquisition shows great variance among individuals. The current study aimed to find neural correlates of visual statistical learning showing a correlation with behavioral results. Based on a pilot study, we conducted an EEG study where participants were exposed to associated stimulus pairs; the acquisition was tested through a familiarity test. We identified an oscillation in the gamma range (40-70 Hz, 0.5-0.75 s post-stimulus), which showed a positive correlation with the behavioral results. This change in activity was located in a left frontoparietal cluster. Based on its latency and location, this difference was identified as a late gamma activity, a correlate of model-based learning. Such learning is a summary of several top-down mechanisms that modulate the recollection of statistical relationships such as the capacity of working memory or attention. These results suggest that, during acquisition, individual behavioral variance is influenced by dominant learning processes which affect the recall of previously gained information.

Development of statistical learning ability across modalities, domains, and languages

Statistical learning (SL) is defined as our ability to use statistics (e.g., frequencies or transitional probabilities) to detect implicit regularities in the environment. Limited research has examined the developmental trajectory of SL across domains and modalities, and no previous research has made systematic comparisons across domains, modalities, and languages using comparable tasks. The current study investigated the development of SL ability across 9-, 11-, and 13-year-old native Chinese-speaking children in non-linguistic visual and auditory SL, first-language Chinese visual and auditory SL, and second-language English visual and auditory SL. Results showed that children across the three age groups achieved all types of SL, and they performed better in visual modality than in auditory modality. Furthermore, while visual SL constantly improved from 9- to 11- to 13-year-olds, auditory SL improved only from 11- to 13-year-olds but not from 9- to 11-year-olds, which could be explained by the discrepancy in developmental trajectory between auditory language and working memory. This pattern of age and modality interaction was similar across non-linguistic Chinese and English SL. A significant interaction between modality and language type also showed that better learning was achieved in visual SL as compared with auditory SL in both non-linguistic and English stimuli. However, children performed similarly across the two modalities in Chinese, possibly due to the contribution of tonal information. Together, our findings point to the joint function of age, modality, and language type in SL development.

Kindergarteners Use Cross-Situational Statistics to Infer the Meaning of Grammatical Elements

Many studies demonstrate that detecting statistical regularities in linguistic input plays a key role in language acquisition. Yet, it is unclear to what extent statistical learning is involved in more naturalistic settings, when young children have to acquire meaningful grammatical elements. In the present study, we address these points, by investigating whether statistical learning is involved in acquiring a morpho-syntactic structure from input that resembles natural languages more closely. We exposed 50 kindergarteners (M = 5 years, 5 months) to a miniature language in which they had to learn a grammatical marker that expressed number, and which could only be acquired on the basis of the distributional properties in the input. Half of the children performed an attention check during the experiment. Results show that young children are able to learn this meaning. We found no clear evidence that facilitating attention to the input increases learning performance.

Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

From an early age, exposure to a spoken language has allowed us to implicitly capture the structure underlying the succession of speech sounds in that language and to segment it into meaningful units (words). Statistical learning (SL), the ability to pick up patterns in the sensory environment without intention or reinforcement, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language, including the discovery of word boundaries in the continuous acoustic stream. Although extensive evidence has been gathered from artificial languages experiments showing that children and adults are able to track the regularities embedded in the auditory input, as the probability of one syllable to follow another syllable in the speech stream, the developmental trajectory of this ability remains controversial. In this work, we have collected Event-Related Potentials (ERPs) while 5-year-old children and young adults (university students) were exposed to a speech stream made of the repetition of eight three-syllable nonsense words presenting different levels of predictability (high vs. low) to mimic closely what occurs in natural languages and to get new insights into the changes that the mechanisms underlying auditory statistical learning (aSL) might undergo through the development. The participants performed the aSL task first under implicit and, subsequently, under explicit conditions to further analyze if children take advantage of previous knowledge of the to-be-learned regularities to enhance SL, as observed with the adult participants. These findings would also contribute to extend our knowledge of the mechanisms available to assist SL at each developmental stage. Although behavioral signs of learning, even under explicit conditions, were only observed for the adult participants, ERP data showed evidence of online segmentation in the brain in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural data suggests, however, that adults and children rely on different mechanisms to assist the extraction of word-like units from the continuous speech stream, hence supporting the view that SL with auditory linguistic materials changes through development.

EXPRESS: Are animates special? Exploring the effects of selective attention and animacy on visual statistical learning

Our visual system is built to extract regularities in how objects within our visual environment appear in relation to each other across time and space ('visual statistical learning'). Existing research indicates that visual statistical learning is modulated by selective attention. Our attentional system prioritises information that enables behaviour; for example, animates are prioritised over inanimates (the 'animacy advantage'). The present study examined the effects of selective attention and animacy on visual statistical learning in young adults (N = 284). We tested visual statistical learning of attended and unattended information across four animacy conditions: (i) living things that can self-initiate movement (animals); (ii) living things that cannot self-initiate movement (fruits and vegetables); (iii) non-living things that can generate movement (vehicles); and (iv) non-living things that cannot generate movement (tools and kitchen utensils). We implemented a four-point confidence-rating scale as an assessment of participants' awareness of the regularities in the visual statistical learning task. There were four key findings. First, selective attention plays a critical role by modulating visual statistical learning. Second, animacy does not play a special role in visual statistical learning. Third, visual statistical learning of attended information cannot be exclusively accounted for by unconscious knowledge. Fourth, performance on the visual statistical learning task is associated with the proportion of stimuli that were named or labelled. Our findings support the notion that visual statistical learning is a powerful mechanism by which our visual system resolves an abundance of sensory input over time.

Statistically Induced Chunking Recall: A Memory-Based Approach to Statistical Learning

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short-term memory is fundamentally shaped by long-term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word-like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test-retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two-alternative forced-choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature.

Not All Words Are Equally Acquired: Transitional Probabilities and Instructions Affect the Electrophysiological Correlates of Statistical Learning

Statistical learning (SL), the process of extracting regularities from the environment, is a fundamental skill of our cognitive system to structure the world regularly and predictably. SL has been studied using mainly behavioral tasks under implicit conditions and with triplets presenting the same level of difficulty, i.e., a mean transitional probability (TP) of 1.00. Yet, the neural mechanisms underlying SL under other learning conditions remain largely unknown. Here, we investigated the neurofunctional correlates of SL using triplets (i.e., three-syllable nonsense words) with a mean TP of 1.00 (easy "words") and 0.50 (hard "words") in an SL task performed under incidental (implicit) and intentional (explicit) conditions, to determine whether the same core mechanisms were recruited to assist learning. Event-related potentials (ERPs) were recorded while participants listened firstly to a continuous auditory stream made of the concatenation of four easy and four hard "words" under implicit instructions, and subsequently to another auditory stream made of the concatenation of four easy and four hard "words" drawn from another artificial language under explicit instructions. The stream in each of the SL tasks was presented in two consecutive blocks of ~3.5-min each (~7-min in total) to further examine how ERP components might change over time. Behavioral measures of SL were collected after the familiarization phase of each SL task by asking participants to perform a two-alternative forced-choice (2-AFC) task. Results from the 2-AFC tasks revealed a moderate but reliable level of SL, with no differences between conditions. ERPs were, nevertheless, sensitive to the effect of TPs, showing larger amplitudes of N400 for easy "words," as well as to the effect of instructions, with a reduced N250 for "words" presented under explicit conditions. Also, significant differences in the N100 were found as a result of the interaction between TPs, instructions, and the amount of exposure to the auditory stream. Taken together, our findings suggest that triplets' predictability impacts the emergence of "words" representations in the brain both for statistical regularities extracted under incidental and intentional instructions, although the prior knowledge of the "words" seems to favor the recruitment of different SL mechanisms.

Specificity of representations in infants' visual statistical learning

Past work has demonstrated infants' robust statistical learning across visual and auditory modalities. However, the specificity of representations produced via visual statistical learning has not been fully explored. The current study addressed this by investigating infants' abilities to identify previously learned object sequences when some object features (e.g., shape, face) aligned with prior learning and other features did not. Experiment 1 replicated past work demonstrating that infants can learn statistical regularities across sequentially presented objects and extended this finding to 16-month-olds. In Experiment 2, infants viewed test sequences in which one object feature (e.g., face) had been removed but the other feature (e.g., shape) was maintained, resulting in failure to identify familiar sequences. We further probed learning specificity by assessing infants' recognition of sequences when one feature was altered rather than removed (Experiment 3) and when one feature was uncorrelated with the original sequence structure (Experiment 4). In both cases, infants failed to identify sequences in which object features were not identical between learning and test. These findings suggest that infants are limited in their ability to generalize the statistical structure of an object sequence when the objects' features do not align between learning and test.

Assessing Visual Statistical Learning in Early-School-Aged Children: The Usefulness of an Online Reaction Time Measure

Visual statistical learning (VSL) was traditionally tested through offline two-alternative forced choice (2-AFC) questions. More recently, online reaction time (RT) measures and alternative offline question types have been developed to further investigate learning during exposure and more adequately assess individual differences in adults (Siegelman et al., 2017b, 2018). We assessed the usefulness of these measures for investigating VSL in early-school-aged children. Secondarily, we examined the effect of introducing a cover task, potentially affecting attention, on children’s VSL performance. Fifty-three children (aged 5–8 years) performed a self-paced VSL task containing triplets, in which participants determine the presentation speed and RTs to each stimulus are recorded. Half of the participants performed a cover task, while the other half did not. Online sensitivity to the statistical structure was measured by contrasting RTs to unpredictable versus predictable elements. Subsequently, participants completed 2-AFC (choose correct triplet) and 3-AFC (fill blank to complete triplet) offline questions. RTs were significantly longer for unpredictable than predictable elements, so we conclude that early-school-aged children are sensitive to the statistical structure during exposure, and that the RT task can measure that. We found no evidence as to whether children can perform above chance on offline 2-AFC or 3-AFC questions, or whether the cover task affects children’s VSL performance. These results show the feasibility of using an online RT task when assessing VSL in early-school-aged children. This task therefore seems suitable for future studies that aim to investigate VSL across development or in clinical populations, perhaps together with behavioral tasks.

Statistical Learning, Implicit Learning, and First Language Acquisition: A Critical Evaluation of Two Developmental Predictions

The role of distributional information in language learning, and learning more generally, has been studied extensively in both the statistical learning and the implicit learning literatures. Despite the similarity in research questions, the two literatures have remained largely separate. Here, we draw on findings from the two traditions to critically evaluate two developmental predictions that are central to both. The first is the question of age invariance: Does learning improve during development or is it fully developed in infancy? The combined findings suggest that both implicit and statistical learning improve during childhood, contra the age invariance prediction. This raises questions about the role of implicit statistical learning (ISL) in explaining the age-related deterioration in language learning: Children's better language learning abilities cannot be attributed to their improved distributional learning skills. The second issue we examine is the predictive relation to language outcomes: Does variation in learning predict variation in language outcomes? While there is evidence for such links, there is concern in both research traditions about the reliability of the tasks used with children. We present data suggesting that commonly used statistical learning measures may not capture stable individual differences in children, undermining their utility for assessing the link to language outcomes in developmental samples. The evaluation of both predictions highlights the empirical parallels between the implicit and statistical learning literatures, and the need to better integrate their developmental investigation. We go on to discuss several of the open challenges facing the study of ISL during development.

Measuring individual differences in statistical learning: Current pitfalls and possible solutions

Most research in statistical learning (SL) has focused on the mean success rates of participants in detecting statistical contingencies at a group level. In recent years, however, researchers have shown increased interest in individual abilities in SL, either to predict other cognitive capacities or as a tool for understanding the mechanism underlying SL. Most if not all of this research enterprise has employed SL tasks that were originally designed for group-level studies. We argue that from an individual difference perspective, such tasks are psychometrically weak, and sometimes even flawed. In particular, the existing SL tasks have three major shortcomings: (1) the number of trials in the test phase is often too small (or, there is extensive repetition of the same targets throughout the test); (2) a large proportion of the sample performs at chance level, so that most of the data points reflect noise; and (3) the test items following familiarization are all of the same type and an identical level of difficulty. These factors lead to high measurement error, inevitably resulting in low reliability, and thereby doubtful validity. Here we present a novel method specifically designed for the measurement of individual differences in visual SL. The novel task we offer displays substantially superior psychometric properties. We report data regarding the reliability of the task and discuss the importance of the implementation of such tasks in future research.

Interacting Effects of Instructions and Presentation Rate on Visual Statistical Learning

The statistical regularities of a sequence of visual shapes can be learned incidentally. Arciuli et al. (2014) recently argued that intentional instructions only improve learning at slow presentation rates as they favor the use of explicit strategies. The aim of the present study was (1) to test this assumption directly by investigating how instructions (incidental vs. intentional) and presentation rate (fast vs. slow) affect the acquisition of knowledge and (2) to examine how these factors influence the conscious vs. unconscious nature of the knowledge acquired. To this aim, we exposed participants to four triplets of shapes, presented sequentially in a pseudo-random order, and assessed their degree of learning in a subsequent completion task that integrated confidence judgments. Supporting Arciuli et al.’s (2014) claim, participant performance only benefited from intentional instructions at slow presentation rates. Moreover, informing participants beforehand about the existence of statistical regularities increased their explicit knowledge of the sequences, an effect that was not modulated by presentation speed. These results support that, although visual statistical learning can take place incidentally and, to some extent, outside conscious awareness, factors such as presentation rate and prior knowledge can boost learning of these regularities, presumably by favoring the acquisition of explicit knowledge.

Rapid Serial Auditory Presentation

Abstract. The Rapid Serial Visual Presentation procedure is a method widely used in visual perception research. In this paper we propose an adaptation of this method which can be used with auditory material and enables assessment of statistical learning in speech segmentation. Adult participants were exposed to an artificial speech stream composed of statistically defined trisyllabic nonsense words. They were subsequently instructed to perform a detection task in a Rapid Serial Auditory Presentation (RSAP) stream in which they had to detect a syllable in a short speech stream. Results showed that reaction times varied as a function of the statistical predictability of the syllable: second and third syllables of each word were responded to faster than first syllables. This result suggests that the RSAP procedure provides a reliable and sensitive indirect measure of auditory statistical learning.

Investigating implicit statistical learning mechanisms through contextual cueing

Since its inception, the contextual cueing (CC) paradigm has generated considerable interest in various fields of cognitive sciences because it constitutes an elegant approach to understanding how statistical learning (SL) mechanisms can detect contextual regularities during a visual search. In this article we review and discuss five aspects of CC: (i) the implicit nature of learning, (ii) the mechanisms involved in CC, (iii) the mediating factors affecting CC, (iv) the generalization of CC phenomena, and (v) the dissociation between implicit and explicit CC phenomena. The findings suggest that implicit SL is an inherent component of ongoing processing which operates through clustering, associative, and reinforcement processes at various levels of sensory-motor processing, and might result from simple spike-timing-dependent plasticity.