Do infants retain the statistics of a statistical learning experience? Insights from a developmental cognitive neuroscience perspective

Playing hide and seek: Contextual regularity learning develops between 3 and 5 years of age

The ability to acquire contextual regularities is fundamental in everyday life because it helps us to navigate the environment, directing our attention where relevant events are more likely to occur. Sensitivity to spatial regularities has been largely reported from infancy. Nevertheless, it is currently unclear when children can use this rapidly acquired contextual knowledge to guide their behavior. Evidence of this ability is indeed mixed in school-aged children and, to date, it has never been explored in younger children and toddlers. The current study investigated the development of contextual regularity learning in children aged 3 to 5 years. To this aim, we designed a new contextual learning paradigm in which young children were presented with recurring configurations of bushes and were asked to guess behind which bush a cartoon monkey was hiding. In a series of two experiments, we manipulated the relevance of color and visuospatial cues for the underlying task goal and tested how this affected young children's behavior. Our results bridge the gap between the infant and adult literatures, showing that sensitivity to spatial configurations persists from infancy to childhood, but it is only around the fifth year of life that children naturally start to integrate multiple cues to guide their behavior.

Developmental changes in retention and generalization of nonadjacent dependencies over a period containing sleep in 18-mo-old infants

Sleep promotes the stabilization of memories in adulthood, with a growing literature on the benefits of sleep for memory in infants and children. In two studies, we examined the role of sleep in the retention and generalization of nonadjacent dependencies (NADs; e.g., a-X-b/c-X-d phrases) in an artificial language. Previously, a study demonstrated that over a delay of 4 h, 15 mo olds who nap after training retain a general memory of the NAD rule instead of memory for specific NADs heard during training. In experiment 1, we designed a replication of the nap condition used in the earlier study but tested 18-mo-old infants. Infants of this age retained veridical memory for specific NADs over a delay containing sleep, providing preliminary evidence of the development of memory processes (experiment 1). In experiment 2, we tested 18 mo olds' ability to generalize the NAD to new vocabulary, finding only infants who napped after training generalized their knowledge of the pattern to completely novel phrases. Overall, by 18 mo of age, children retain specific memories over a period containing sleep, and sleep promotes abstract memories to a greater extent than wakefulness.

Memories of structured input become increasingly distorted across development

Trajectories of cognitive and neural development suggest that, despite early emergence, the ability to extract environmental patterns changes across childhood. Here, 5- to 9-year-olds and adults (N = 211, 110 females, in a large Canadian city) completed a memory test assessing what they remembered after watching a stream of shape triplets: the particular sequence in which the shapes occurred and/or their group-level structure. After accounting for developmental improvements in overall memory, all ages remembered specific transitions, while memory for group membership was only observed in older children and adults (age by test-type interaction η2  = .05). Thus, while young children form memories for specifics of structured experience, memory for derived associations is refined later-underscoring that adults and young children form different memories despite identical experience.

The relation between rhythm processing and cognitive abilities during child development: The role of prediction

Rhythm and meter are central elements of music. From the very beginning, children are responsive to rhythms and acquire increasingly complex rhythmic skills over the course of development. Previous research has shown that the processing of musical rhythm is not only related to children’s music-specific responses but also to their cognitive abilities outside the domain of music. However, despite a lot of research on that topic, the connections and underlying mechanisms involved in such relation are still unclear in some respects. In this article, we aim at analyzing the relation between rhythmic and cognitive-motor abilities during childhood and at providing a new hypothesis about this relation. We consider whether predictive processing may be involved in the relation between rhythmic and various cognitive abilities and hypothesize that prediction as a cross-domain process is a central mechanism building a bridge between rhythm processing and cognitive-motor abilities. Further empirical studies focusing on rhythm processing and cognitive-motor abilities are needed to precisely investigate the links between rhythmic, predictive, and cognitive processes.

Real-world statistics at two timescales and a mechanism for infant learning of object names

Infants learn mappings between heard names and seen things before their first birthday and before they produce spoken language. Two challenges to explaining this early learning are the immaturity of infant memory systems and the infrequency of any individual object name in the heard language input. We quantified the frequency of visual referents, heard names, and the cooccurrences of referents and names in infant everyday experiences. We discovered statistical patterns at two timescales that align with a cortical mechanism of associative memory formation that supports the rapid formation of durable associative memories from very few experienced cooccurrences.

Infants begin learning the visual referents of nouns before their first birthday. Despite considerable empirical and theoretical effort, little is known about the statistics of the experiences that enable infants to break into object–name learning. We used wearable sensors to collect infant experiences of visual objects and their heard names for 40 early-learned categories. The analyzed data were from one context that occurs multiple times a day and includes objects with early-learned names: mealtime. The statistics reveal two distinct timescales of experience. At the timescale of many mealtime episodes (n = 87), the visual categories were pervasively present, but naming of the objects in each of those categories was very rare. At the timescale of single mealtime episodes, names and referents did cooccur, but each name–referent pair appeared in very few of the mealtime episodes. The statistics are consistent with incremental learning of visual categories across many episodes and the rapid learning of name–object mappings within individual episodes. The two timescales are also consistent with a known cortical learning mechanism for one-episode learning of associations: new information, the heard name, is incorporated into well-established memories, the seen object category, when the new information cooccurs with the reactivation of that slowly established memory.

The Sweet Spot: When Children’s Developing Abilities, Brains, and Knowledge Make Them Better Learners Than Adults

Cognitive development is marked by age-related improvements across a number of domains, as young children perform worse than their older counterparts on most tasks. However, there are cases in which young children, and even infants, outperform older children and adults. So when, and why, does being young sometimes confer an advantage? This article provides a comprehensive examination of the peculiar cases in which younger children perform better. First, we outline the specific instances in which younger is better across domains, including mastering language, using probabilistic information, detecting causal relations, remembering certain information, and even solving problems. We then examine how children’s reduced cognitive abilities, ongoing brain development, more limited prior knowledge, and heightened tendency to explore benefits their learning, reasoning, perception, and memory from a mechanistic perspective. We hold that considering all of these factors together is essential for understanding the ways in which children’s learning is unique and that science has much to learn from a careful consideration of childhood.

THE DEVELOPMENT OF COMMUNICATION ACROSS TIMESCALES

How do young children learn to organize the statistics of communicative input across milliseconds and months? Developmental science has made progress in understanding how infants learn patterns in language and how infant-directed speech is engineered to ease short-timescale processing, but less is known about how they link perceptual experiences across multiple levels of processing within an interaction (from syllables to stories) and across development. In this article, we propose that three domains of research - statistical summary, neural processing hierarchies, and neural coupling - will be fruitful in uncovering the dynamic exchange of information between children and adults, both in the moment and in aggregate. In particular, we discuss how the study of brain-to-brain and brain-to-behavior coupling between children and adults will further our understanding of how children's neural representations become aligned with the increasingly complex statistics of communication across timescales.

The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach

Being proficient in several foreign languages is an essential part of every-day life. In contrast to childhood, learning a new language can be highly challenging for adults. The present study aims at investigating neural mechanisms supporting very initial foreign language learning in adulthood. For this reason, subjects underwent an implicit semantic associative training in which they had to learn new pseudoword-picture pairings. Learning success was measured via a recognition experiment presenting learned versus new pseudoword-picture pairings. Neural correlates were assessed by an innovative multi-methodological approach simultaneously applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Results indicate memory-related processes based on familiarity and mechanisms of cognitive control to be present during initial vocabulary learning. Findings underline the fascinating plasticity of the adult brain during foreign language learning, even after a short semantic training of only 18 minutes as well as the importance of comparing evidence from different neuroscientific methods and behavioral data.

Prediction Error-Driven Memory Consolidation for Continual Learning: On the Case of Adaptive Greenhouse Models

This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of an episodic memory system and of prediction-error driven memory consolidation. In line with evidence from brain sciences, memories are retained depending on their congruence with the prior knowledge stored in the system. In this work, congruence is estimated in terms of prediction error resulting from a deep neural model. The proposed AI system is transferred onto an innovative application in the horticulture industry: the learning and transfer of greenhouse models. This work presents models trained on data recorded from research facilities and transferred to a production greenhouse.

Divided attention does not affect the acquisition and consolidation of transitional probabilities

Statistical learning facilitates the efficient processing and prediction of environmental events and contributes to the acquisition of automatic behaviors. Whereas a minimal level of attention seems to be required for learning to occur, it is still unclear how acquisition and consolidation of statistical knowledge are affected when attention is divided during learning. To test the effect of divided attention on statistical learning and consolidation, ninety-six healthy young adults performed the Alternating Serial Reaction Time task in which they incidentally acquired second-order transitional probabilities. Half of the participants completed the task with a concurrent secondary intentional sequence learning task that was applied to the same stimulus stream. The other half of the participants performed the task without any attention manipulation. Performance was retested after a 12-h post-learning offline period. Half of each group slept during the delay, while the other half had normal daily activity, enabling us to test the effect of delay activity (sleep vs. wake) on the consolidation of statistical knowledge. Divided attention had no effect on statistical learning: The acquisition of second-order transitional probabilities was comparable with and without the secondary task. Consolidation was neither affected by divided attention: Statistical knowledge was similarly retained over the 12-h delay, irrespective of the delay activity. Our findings can contribute to a better understanding of the role of attentional processes in and the robustness of visuomotor statistical learning and consolidation.

Children automatically abstract categorical regularities during statistical learning

Statistical learning allows us to discover myriad structures in our environment, which is saturated with information at many different levels-from items to categories. How do children learn different levels of information-about regularities that pertain to items and the categories they come from-and how does this differ from adults? Studies on category learning and memory have suggested that children may be more focused on items than adults. If this is also the case for statistical learning, children may not extract and learn the multi-level regularities that adults can. We report three experiments showing that children and adults extract both item- and category-level regularities in statistical learning. In Experiments 1 and 2, we show that both children and adults can learn structure at the item and category levels when they are measured independently. In Experiment 3, we show that both children and adults learn about categories even when exposure does not require this: both are able to generalize their learning from the item to the category level. Results indicate that statistical learning operates across multi-level structure in children and adults alike, enabling generalization of learning from specific items to exemplars from categories of those items that observers have never seen. Even though children may be more focused on items during other forms of learning, they learn about categories from item-level input during statistical learning.

Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants' endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item's chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities.

Individual and Developmental Differences in Distributional Learning

Purpose

This study examined whether children and adults with developmental language disorder (DLD) could use distributional information in an artificial language to learn about grammatical category membership similarly to their typically developing (TD) peers and whether developmental differences existed within and between DLD and TD groups.

Method

Sixteen children ages 7-9 with DLD, 26 age-matched TD children, 17 college students with DLD, and 17 TD college students participated in this task. We used an artificial grammar learning paradigm in which participants had to use knowledge of category membership to determine the acceptability of test items that they had not heard during a training phase.

Results

Individuals with DLD performed similarly to TD peers in distinguishing grammatical from ungrammatical combinations, with no differences between age groups. The order in which items were heard at test differentially affected child versus adult participants and showed a relation with attention and phonological working memory as well.

Conclusion

Differences in ratings between grammatical and ungrammatical items in this task suggest that individuals with DLD can form grammatical categories from novel input and more broadly use distributional information. Differences in order effects suggest a developmental timeline for sensitivity to updating distributional information.

Superior learning in synesthetes: Consistent grapheme-color associations facilitate statistical learning

In synesthesia activation in one sensory domain, such as smell or sound, triggers an involuntary and unusual secondary sensory or cognitive experience. In the present study, we ask whether the added sensory experience of synesthesia can aid statistical learning-the ability to track environmental regularities in order to segment continuous information. To investigate this, we measured statistical learning outcomes, using an aurally presented artificial language, in two groups of synesthetes alongside controls and simulated the multimodal experience of synesthesia in non-synesthetes. One group of synesthetes exclusively had grapheme-color (GC) synesthesia, in which the experience of color is automatically triggered by exposure to written or spoken graphemes. The other group had both grapheme-color and sound-color (SC+) synesthesia, in which the experience of color is also triggered by the waveform properties of a voice, such as pitch, timbre, and/or musical chords. Unlike GC-only synesthetes, the experience of color in the SC+ group is not perfectly consistent with the statistics that signal word boundaries. We showed that GC-only synesthetes outperformed both non-synesthetes and SC+ synesthetes, likely because the visual concurrents for GC-only synesthetes are highly consistent with the artificial language. We further observed that our simulations of GC synesthesia, but not SC+ synesthesia produced superior statistical learning, showing that synesthesia likely boosts learning outcomes by providing a consistent secondary cue. Findings are discussed with regard to how multimodal experience can improve learning, with the present data indicating that this boost is more likely to occur through explicit, as opposed to implicit, learning systems.

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

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

When learning goes beyond statistics: Infants represent visual sequences in terms of chunks

Much research has documented infants' sensitivity to statistical regularities in auditory and visual inputs, however the manner in which infants process and represent statistically defined information remains unclear. Two types of models have been proposed to account for this sensitivity: statistical models, which posit that learners represent statistical relations between elements in the input; and chunking models, which posit that learners represent statistically-coherent units of information from the input. Here, we evaluated the fit of these two types of models to behavioral data that we obtained from 8-month-old infants across four visual sequence-learning experiments. Experiments examined infants' representations of two types of structures about which statistical and chunking models make contrasting predictions: illusory sequences (Experiment 1) and embedded sequences (Experiments 2-4). In all four experiments, infants discriminated between high probability sequences and low probability part-sequences, providing strong evidence of learning. Critically, infants also discriminated between high probability sequences and statistically-matched sequences (illusory sequences in Experiment 1, embedded sequences in Experiments 2-3), suggesting that infants learned coherent chunks of elements. Experiment 4 examined the temporal nature of chunking, and demonstrated that the fate of embedded chunks depends on amount of exposure. These studies contribute important new data on infants' visual statistical learning ability, and suggest that the representations that result from infants' visual statistical learning are best captured by chunking models.

Reading and the Neurocognitive Bases of Statistical Learning1

The processes underlying word reading are shaped by statistical properties of the writing system. According to some theoretical perspectives (e.g. Harm & Seidenberg, 2004) reading acquisition should be understood as an exercise in statistical learning. Statistical learning (SL) involves the extraction of organizing principles from a set of inputs. Several lines of research provide convergent evidence supporting the connection between SL and reading acquisition (e.g., Arciuli & Simpson, 2012; Frost et al., 2014; Bogaerts et al., 2015). An obstacle to fully appreciating the theoretical and educational implications of these findings is that SL is itself not well understood. In this paper, we review the current literature on SL with a particular focus on organizing this literature by grounding it in theories of learning and memory more generally. This approach can clarify the nature of SL and provide a framework for understanding its role in reading, reading acquisition, and reading disorders.

The longevity of statistical learning: When infant memory decays, isolated words come to the rescue

Research over the past 2 decades has demonstrated that infants are equipped with remarkable computational abilities that allow them to find words in continuous speech. Infants can encode information about the transitional probability (TP) between syllables to segment words from artificial and natural languages. As previous research has tested infants immediately after familiarization, infants' ability to retain sequential statistics beyond the immediate familiarization context remains unknown. Here, we examine infants' memory for statistically defined words 10 min after familiarization with an Italian corpus. Eight-month-old English-learning infants were familiarized with Italian sentences that contained 4 embedded target words-2 words had high internal TP (HTP, TP = 1.0) and 2 had low TP (LTP, TP = .33)-and were tested on their ability to discriminate HTP from LTP words using the Headturn Preference Procedure. When tested after a 10-min delay, infants failed to discriminate HTP from LTP words, suggesting that memory for statistical information likely decays over even short delays (Experiment 1). Experiments 2-4 were designed to test whether experience with isolated words selectively reinforces memory for statistically defined (i.e., HTP) words. When 8-month-olds were given additional experience with isolated tokens of both HTP and LTP words immediately after familiarization, they looked significantly longer on HTP than LTP test trials 10 min later. Although initial representations of statistically defined words may be fragile, our results suggest that experience with isolated words may reinforce the output of statistical learning by helping infants create more robust memories for words with strong versus weak co-occurrence statistics. (PsycINFO Database Record

Towards a theory of individual differences in statistical learning

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

Abstraction and generalization in statistical learning: implications for the relationship between semantic types and episodic tokens

Statistical approaches to emergent knowledge have tended to focus on the process by which experience of individual episodes accumulates into generalizable experience across episodes. However, there is a seemingly opposite, but equally critical, process that such experience affords: the process by which, from a space of types (e.g. onions-a semantic class that develops through exposure to individual episodes involving individual onions), we can perceive or create, on-the-fly, a specific token (a specific onion, perhaps one that is chopped) in the absence of any prior perceptual experience with that specific token. This article reviews a selection of statistical learning studies that lead to the speculation that this process-the generation, on the basis of semantic memory, of a novel episodic representation-is itself an instance of a statistical, in fact associative, process. The article concludes that the same processes that enable statistical abstraction across individual episodes to form semantic memories also enable the generation, from those semantic memories, of representations that correspond to individual tokens, and of novel episodic facts about those tokens. Statistical learning is a window onto these deeper processes that underpin cognition.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

The multi-component nature of statistical learning

The central argument presented in this paper is that statistical learning (SL) is an ability comprised of multiple components that operate largely implicitly. Components relating to the stimulus encoding, retention and abstraction required for SL may include, but are not limited to, certain types of attention, processing speed and memory. It is likely that individuals vary in terms of the efficiency of these underlying components, and in patterns of connectivity among these components, and that SL tasks differ from one another in how they draw on certain underlying components more than others. This theoretical framework is of value because it can assist in gaining a clearer understanding of how SL is linked with individual differences in complex mental activities such as language processing. Variability in language processing across individuals is of central concern to researchers interested in child development, including those interested in neurodevelopmental disorders where language can be affected such as autism spectrum disorders (ASD). This paper discusses the link between SL and individual differences in language processing in the context of age-related changes in SL during infancy and childhood, and whether SL is affected in ASD. Viewing SL as a multi-component ability may help to explain divergent findings from previous empirical research in these areas and guide the design of future studies.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.