Rhythmically Modulating Neural Entrainment during Exposure to Regularities Influences Statistical Learning.

The ability to discover regularities in the environment, such as syllable patterns in speech, is known as statistical learning. Previous studies have shown that statistical learning is accompanied by neural entrainment, in which neural activity temporally aligns with repeating patterns over time. However, it is unclear whether these rhythmic neural dynamics play a functional role in statistical learning or whether they largely reflect the downstream consequences of learning, such as the enhanced perception of learned words in speech. To better understand this issue, we manipulated participants' neural entrainment during statistical learning using continuous rhythmic visual stimulation. Participants were exposed to a speech stream of repeating nonsense words while viewing either (1) a visual stimulus with a "congruent" rhythm that aligned with the word structure, (2) a visual stimulus with an incongruent rhythm, or (3) a static visual stimulus. Statistical learning was subsequently measured using both an explicit and implicit test. Participants in the congruent condition showed a significant increase in neural entrainment over auditory regions at the relevant word frequency, over and above effects of passive volume conduction, indicating that visual stimulation successfully altered neural entrainment within relevant neural substrates. Critically, during the subsequent implicit test, participants in the congruent condition showed an enhanced ability to predict upcoming syllables and stronger neural phase synchronization to component words, suggesting that they had gained greater sensitivity to the statistical structure of the speech stream relative to the incongruent and static groups. This learning benefit could not be attributed to strategic processes, as participants were largely unaware of the contingencies between the visual stimulation and embedded words. These results indicate that manipulating neural entrainment during exposure to regularities influences statistical learning outcomes, suggesting that neural entrainment may functionally contribute to statistical learning. Our findings encourage future studies using non-invasive brain stimulation methods to further understand the role of entrainment in statistical learning.

Children extract a new linguistic rule more quickly than adults.

Children achieve better long-term language outcomes than adults. However, it remains unclear whether children actually learn language more quickly than adults during real-time exposure to input-indicative of true superior language learning abilities-or whether this advantage stems from other factors. To examine this issue, we compared the rate at which children (8-10 years) and adults extracted a novel, hidden linguistic rule, in which novel articles probabilistically predicted the animacy of associated nouns (e.g., "gi lion"). Participants categorized these two-word phrases according to a second, explicitly instructed rule over two sessions, separated by an overnight delay. Both children and adults successfully learned the hidden animacy rule through mere exposure to the phrases, showing slower response times and decreased accuracy to occasional phrases that violated the rule. Critically, sensitivity to the hidden rule emerged much more quickly in children than adults; children showed a processing cost for violation trials from very early on in learning, whereas adults did not show reliable sensitivity to the rule until the second session. Children also showed superior generalization of the hidden animacy rule when asked to classify nonword trials (e.g., "gi badupi") according to the hidden animacy rule. Children and adults showed similar retention of the hidden rule over the delay period. These results provide insight into the nature of the critical period for language, suggesting that children have a true advantage over adults in the rate of implicit language learning. Relative to adults, children more rapidly extract hidden linguistic structures during real-time language exposure. RESEARCH HIGHLIGHTS: Children and adults both succeeded in implicitly learning a novel, uninstructed linguistic rule, based solely on exposure to input. Children learned the novel linguistic rules much more quickly than adults. Children showed better generalization performance than adults when asked to apply the novel rule to nonsense words without semantic content. Results provide insight into the nature of critical period effects in language, indicating that children have an advantage over adults in real-time language learning.

Dentate Gyrus Integrity Is Necessary for Behavioral Pattern Separation But Not Statistical Learning.

Pattern separation, the creation of distinct representations of similar inputs, and statistical learning, the rapid extraction of regularities across multiple inputs, have both been linked to hippocampal processing. It has been proposed that there may be functional differentiation within the hippocampus, such that the trisynaptic pathway (entorhinal cortex > dentate gyrus > CA3 > CA1) supports pattern separation, whereas the monosynaptic pathway (entorhinal cortex > CA1) supports statistical learning. To test this hypothesis, we investigated the behavioral expression of these two processes in B. L., an individual with highly selective bilateral lesions in the dentate gyrus that presumably disrupt the trisynaptic pathway. We tested pattern separation with two novel auditory versions of the continuous mnemonic similarity task, requiring the discrimination of similar environmental sounds and trisyllabic words. For statistical learning, participants were exposed to a continuous speech stream made up of repeating trisyllabic words. They were then tested implicitly through a RT-based task and explicitly through a rating task and a forced-choice recognition task. B. L. showed significant deficits in pattern separation on the mnemonic similarity tasks and on the explicit rating measure of statistical learning. In contrast, B. L. showed intact statistical learning on the implicit measure and the familiarity-based forced-choice recognition measure. Together, these results suggest that dentate gyrus integrity is critical for high-precision discrimination of similar inputs, but not the implicit expression of statistical regularities in behavior. Our findings offer unique new support for the view that pattern separation and statistical learning rely on distinct neural mechanisms.

Preverbal Infants Discover Statistical Word Patterns at Similar Rates as Adults: Evidence From Neural Entrainment.

The discovery of words in continuous speech is one of the first challenges faced by infants during language acquisition. This process is partially facilitated by statistical learning, the ability to discover and encode relevant patterns in the environment. Here, we used an electroencephalogram (EEG) index of neural entrainment to track 6-month-olds' (N = 25) segmentation of words from continuous speech. Infants' neural entrainment to embedded words increased logarithmically over the learning period, consistent with a perceptual shift from isolated syllables to wordlike units. Moreover, infants' neural entrainment during learning predicted postlearning behavioral measures of word discrimination (n = 18). Finally, the logarithmic increase in entrainment to words was comparable in infants and adults, suggesting that infants and adults follow similar learning trajectories when tracking probability information among speech sounds. Statistical-learning effects in infants and adults may reflect overlapping neural mechanisms, which emerge early in life and are maintained throughout the life span.

Phase of Spontaneous Slow Oscillations during Sleep Influences Memory-Related Processing of Auditory Cues.

Slow oscillations during slow-wave sleep (SWS) may facilitate memory consolidation by regulating interactions between hippocampal and cortical networks. Slow oscillations appear as high-amplitude, synchronized EEG activity, corresponding to upstates of neuronal depolarization and downstates of hyperpolarization. Memory reactivations occur spontaneously during SWS, and can also be induced by presenting learning-related cues associated with a prior learning episode during sleep. This technique, targeted memory reactivation (TMR), selectively enhances memory consolidation. Given that memory reactivation is thought to occur preferentially during the slow-oscillation upstate, we hypothesized that TMR stimulation effects would depend on the phase of the slow oscillation. Participants learned arbitrary spatial locations for objects that were each paired with a characteristic sound (eg, cat-meow). Then, during SWS periods of an afternoon nap, one-half of the sounds were presented at low intensity. When object location memory was subsequently tested, recall accuracy was significantly better for those objects cued during sleep. We report here for the first time that this memory benefit was predicted by slow-wave phase at the time of stimulation. For cued objects, location memories were categorized according to amount of forgetting from pre- to post-nap. Conditions of high versus low forgetting corresponded to stimulation timing at different slow-oscillation phases, suggesting that learning-related stimuli were more likely to be processed and trigger memory reactivation when they occurred at the optimal phase of a slow oscillation. These findings provide insight into mechanisms of memory reactivation during sleep, supporting the idea that reactivation is most likely during cortical upstates. SIGNIFICANCE STATEMENT: Slow-wave sleep (SWS) is characterized by synchronized neural activity alternating between active upstates and quiet downstates. The slow-oscillation upstates are thought to provide a window of opportunity for memory consolidation, particularly conducive to cortical plasticity. Recent evidence shows that sensory cues associated with previous learning can be delivered subtly during SWS to selectively enhance memory consolidation. Our results demonstrate that this behavioral benefit is predicted by slow-oscillation phase at stimulus presentation time. Cues associated with high versus low forgetting based on analysis of subsequent recall performance were delivered at opposite slow-oscillation phases. These results provide evidence of an optimal slow-oscillation phase for memory consolidation during sleep, supporting the idea that memory processing occurs preferentially during cortical upstates.

Vocabulary learning benefits from REM after slow-wave sleep.

Memory reactivation during slow-wave sleep (SWS) influences the consolidation of recently acquired knowledge. This reactivation occurs spontaneously during sleep but can also be triggered by presenting learning-related cues, a technique known as targeted memory reactivation (TMR). Here we examined whether TMR can improve vocabulary learning. Participants learned the meanings of 60 novel words. Auditory cues for half the words were subsequently presented during SWS in an afternoon nap. Memory performance for cued versus uncued words did not differ at the group level but was systematically influenced by REM sleep duration. Participants who obtained relatively greater amounts of REM showed a significant benefit for cued relative to uncued words, whereas participants who obtained little or no REM demonstrated a significant effect in the opposite direction. We propose that REM after SWS may be critical for the consolidation of highly integrative memories, such as new vocabulary. Reactivation during SWS may allow newly encoded memories to be associated with other information, but this association can include disruptive linkages with pre-existing memories. Subsequent REM sleep may then be particularly beneficial for integrating new memories into appropriate pre-existing memory networks. These findings support the general proposition that memory storage benefits optimally from a cyclic succession of SWS and REM.

Rapid Statistical Learning Supporting Word Extraction From Continuous Speech.

The identification of words in continuous speech, known as speech segmentation, is a critical early step in language acquisition. This process is partially supported by statistical learning, the ability to extract patterns from the environment. Given that speech segmentation represents a potential bottleneck for language acquisition, patterns in speech may be extracted very rapidly, without extensive exposure. This hypothesis was examined by exposing participants to continuous speech streams composed of novel repeating nonsense words. Learning was measured on-line using a reaction time task. After merely one exposure to an embedded novel word, learners demonstrated significant learning effects, as revealed by faster responses to predictable than to unpredictable syllables. These results demonstrate that learners gained sensitivity to the statistical structure of unfamiliar speech on a very rapid timescale. This ability may play an essential role in early stages of language acquisition, allowing learners to rapidly identify word candidates and "break in" to an unfamiliar language.

Neural Measures Reveal Implicit Learning during Language Processing.

Language input is highly variable; phonological, lexical, and syntactic features vary systematically across different speakers, geographic regions, and social contexts. Previous evidence shows that language users are sensitive to these contextual changes and that they can rapidly adapt to local regularities. For example, listeners quickly adjust to accented speech, facilitating comprehension. It has been proposed that this type of adaptation is a form of implicit learning. This study examined a similar type of adaptation, syntactic adaptation, to address two issues: (1) whether language comprehenders are sensitive to a subtle probabilistic contingency between an extraneous feature (font color) and syntactic structure and (2) whether this sensitivity should be attributed to implicit learning. Participants read a large set of sentences, 40% of which were garden-path sentences containing temporary syntactic ambiguities. Critically, but unbeknownst to participants, font color probabilistically predicted the presence of a garden-path structure, with 75% of garden-path sentences (and 25% of normative sentences) appearing in a given font color. ERPs were recorded during sentence processing. Almost all participants indicated no conscious awareness of the relationship between font color and sentence structure. Nonetheless, after sufficient time to learn this relationship, ERPs time-locked to the point of syntactic ambiguity resolution in garden-path sentences differed significantly as a function of font color. End-of-sentence grammaticality judgments were also influenced by font color, suggesting that a match between font color and sentence structure increased processing fluency. Overall, these findings indicate that participants can implicitly detect subtle co-occurrences between physical features of sentences and abstract, syntactic properties, supporting the notion that implicit learning mechanisms are generally operative during online language processing.

Functional differences between statistical learning with and without explicit training.

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly.

Early nap cessation in young children as a correlate of language and psychosocial outcomes: Evidence from a large Canadian sample.

OBJECTIVES: Most children stop napping between 2 and 5years old. We tested the association of early nap cessation (ie, children who stopped before their third birthday) and language, cognition functioning and psychosocial outcomes. METHODS: Data were from a national, longitudinal sample of Canadian children, with three timepoints. Children were 0-to-1 year old at T1, 2-to-3 years old at T2, and 4-to-5 years old at T3. Early nap cessation was tested as a correlate of children's psychosocial functioning (cross-sectionally and longitudinally), cognitive function (longitudinally), and language skills (longitudinally). There were 4923 children (50.9% male; 90.0% White) and their parents in this study who were included in the main analyses. Parents reported on demographics, perinatal and developmental variables, child functioning, and child sleep. Children completed direct assessments of receptive language and cognitive ability. Nap cessation, demographic, and developmental-control variables were tested as correlates of cross-sectional and longitudinal outcomes using linear regression (with a model-building approach). RESULTS: Early nap cessation correlated with higher receptive language ability (ß = 0.059 ± 0.028) and lower anxiety (ß = -0.039 ± 0.028) at T3, after controlling for known correlates of nap cessation, nighttime sleep, and other sociodemographic correlates of the outcomes. Cognitive ability, hyperactivity-inattention, and aggression were not correlated with nap cessation. CONCLUSIONS: Early nap cessation is related to specific benefits (ie, better receptive language and lower anxiety symptoms). These findings align with previous research. Future research should investigate differences associated with late nap cessation and in nap-encouraging cultures, and by ethnicity.

Of words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speech.

Statistical learning is an ability that allows individuals to effortlessly extract patterns from the environment, such as sound patterns in speech. Some prior evidence suggests that statistical learning operates more robustly for speech compared to non-speech stimuli, supporting the idea that humans are predisposed to learn language. However, any apparent statistical learning advantage for speech could be driven by signal acoustics, rather than the subjective perception per se of sounds as speech. To resolve this issue, the current study assessed whether there is a statistical learning advantage for ambiguous sounds that are subjectively perceived as speech-like compared to the same sounds perceived as non-speech, thereby controlling for acoustic features. We first induced participants to perceive sine-wave speech (SWS)-a degraded form of speech not immediately perceptible as speech-as either speech or non-speech. After this induction phase, participants were exposed to a continuous stream of repeating trisyllabic nonsense words, composed of SWS syllables, and then completed an explicit familiarity rating task and an implicit target detection task to assess learning. Critically, participants showed robust and equivalent performance on both measures, regardless of their subjective speech perception. In contrast, participants who perceived the SWS syllables as more speech-like showed better detection of individual syllables embedded in speech streams. These results suggest that speech perception facilitates processing of individual sounds, but not the ability to extract patterns across sounds. Our findings suggest that statistical learning is not influenced by the perceived linguistic relevance of sounds, and that it may be conceptualized largely as an automatic, stimulus-driven mechanism.

Separate but not independent: Behavioral pattern separation and statistical learning are differentially affected by aging.

Our brains are capable of discriminating similar inputs (pattern separation) and rapidly generalizing across inputs (statistical learning). Are these two processes dissociable in behavior? Here, we asked whether cognitive aging affects them in a differential or parallel manner. Older and younger adults were tested on their ability to discriminate between similar trisyllabic words and to extract trisyllabic words embedded in a continuous speech stream. Older adults demonstrated intact statistical learning on an implicit, reaction time-based measure and an explicit, familiarity-based measure of learning. However, they performed poorly in discriminating similar items presented in isolation, both for episodically-encoded items and for statistically-learned regularities. These results indicate that pattern separation and statistical learning are dissociable and differentially affected by aging. The acquisition of implicit representations of statistical regularities operates robustly into old age, whereas pattern separation influences the expression of statistical learning with high representational fidelity and is subject to age-related decline.

Learning words without trying: Daily second language podcasts support word-form learning in adults.

Spoken language contains overlapping patterns across different levels, from syllables to words to phrases. The discovery of these structures may be partially supported by statistical learning (SL), the unguided, automatic extraction of regularities from the environment through passive exposure. SL supports word learning in artificial language experiments, but few studies have examined whether it scales up to support natural language learning in adult second language learners. Here, adult English speakers (n = 70) listened to daily podcasts in either Italian or English for 2 weeks while going about their normal routines. To measure word knowledge, participants provided familiarity ratings of Italian words and nonwords both before and after the listening period. Critically, compared with English controls, Italian listeners significantly improved in their ability to discriminate Italian words and nonwords. These results suggest that unguided exposure to natural, foreign language speech supports the extraction of relevant word features and the development of nascent word forms. At a theoretical level, these findings indicate that SL may effectively scale up to support real-world language acquisition. These results also have important practical implications, suggesting that adult learners may be able to acquire relevant speech patterns and initial word forms simply by listening to the language. This form of learning can occur without explicit effort, formal instruction or focused study.

Simple statistical regularities presented during sleep are detected but not retained.

In recent years, there has been growing interest and excitement over the newly discovered cognitive capacities of the sleeping brain, including its ability to form novel associations. These recent discoveries raise the possibility that other more sophisticated forms of learning may also be possible during sleep. In the current study, we tested whether sleeping humans are capable of statistical learning - the process of becoming sensitive to repeating, hidden patterns in environmental input, such as embedded words in a continuous stream of speech. Participants' EEG was recorded while they were presented with one of two artificial languages, composed of either trisyllabic or disyllabic nonsense words, during slow-wave sleep. We used an EEG measure of neural entrainment to assess whether participants became sensitive to the repeating regularities during sleep-exposure to the language. We further probed for long-term memory representations by assessing participants' performance on implicit and explicit tests of statistical learning during subsequent wake. In the disyllabic-but not trisyllabic-language condition, participants' neural entrainment to words increased over time, reflecting a gradual gain in sensitivity to the embedded regularities. However, no significant behavioural effects of sleep-exposure were observed after the nap, for either language. Overall, our results indicate that the sleeping brain can detect simple, repeating pairs of syllables, but not more complex triplet regularities. However, the online detection of these regularities does not appear to produce any durable long-term memory traces that persist into wake - at least none that were revealed by our current measures and sample size. Although some perceptual aspects of statistical learning are preserved during sleep, the lack of memory benefits during wake indicates that exposure to a novel language during sleep may have limited practical value.

No statistical learning advantage in children over adults: Evidence from behaviour and neural entrainment.

Explicit recognition measures of statistical learning (SL) suggest that children and adults have similar linguistic SL abilities. However, explicit tasks recruit additional cognitive processes that are not directly relevant for SL and may thus underestimate children's true SL capacities. In contrast, implicit tasks and neural measures of SL should be less influenced by explicit, higher-level cognitive abilities and thus may be better suited to capturing developmental differences in SL. Here, we assessed SL to six minutes of an artificial language in English-speaking children (n = 56, 24 females, M = 9.98 years) and adults (n = 44; 31 females, M = 22.97 years), using explicit and implicit behavioural measures and an EEG measure of neural entrainment. With few exceptions, children and adults showed largely similar performance on the behavioural explicit and implicit tasks, replicating prior work. Children and adults also demonstrated robust neural entrainment to both words and syllables, with a similar time course of word-level entrainment, reflecting learning of the hidden word structure. These results demonstrate that children and adults have similar linguistic SL abilities, even when learning is assessed through implicit performance-based and neural measures.

Musical instrument familiarity affects statistical learning of tone sequences.

Most listeners have an implicit understanding of the rules that govern how music unfolds over time. This knowledge is acquired in part through statistical learning, a robust learning mechanism that allows individuals to extract regularities from the environment. However, it is presently unclear how this prior musical knowledge might facilitate or interfere with the learning of novel tone sequences that do not conform to familiar musical rules. In the present experiment, participants listened to novel, statistically structured tone sequences composed of pitch intervals not typically found in Western music. Between participants, the tone sequences either had the timbre of artificial, computerized instruments or familiar instruments (piano or violin). Knowledge of the statistical regularities was measured as by a two-alternative forced choice recognition task, requiring discrimination between novel sequences that followed versus violated the statistical structure, assessed at three time points (immediately post-training, as well as one day and one week post-training). Compared to artificial instruments, training on familiar instruments resulted in reduced accuracy. Moreover, sequences from familiar instruments - but not artificial instruments - were more likely to be judged as grammatical when they contained intervals that approximated those commonly used in Western music, even though this cue was non-informative. Overall, these results demonstrate that instrument familiarity can interfere with the learning of novel statistical regularities, presumably through biasing memory representations to be aligned with Western musical structures. These results demonstrate that real-world experience influences statistical learning in a non-linguistic domain, supporting the view that statistical learning involves the continuous updating of existing representations, rather than the establishment of entirely novel ones.

Optimizing steady-state responses to index statistical learning: Response to Benjamin and colleagues.

Neural entrainment refers to the tendency of neural activity to align with an ongoing rhythmic stimulus. Measures of neural entrainment have been increasingly leveraged as a tool to understand how the brain tracks different types of regularities in sensory input. However, the methods used to quantify neural entrainment are varied, with numerous analytic decision points whose consequences have not been well-characterized. In a valuable contribution to this field, Benjamin, Dehaene-Lambertz and Flo (submitted) systematically compare various methodological approaches for studying neural entrainment. They demonstrate that the use of overlapping epochs, in which sliding time windows are extracted and analyzed, results in an artifactual inflation of entrainment estimates at the frequency of overlap. Here, in response to this updated best practice recommendation, we reanalyzed three previously published datasets that had been previously analyzed with overlapping epochs. Although our main results and conclusions are unaltered from those originally reported, we agree with Benjamin and colleagues that overlapping epochs should generally be avoided in classic analyses of steady-state experiments, which aim to quantify overall peaks in phase or power across an entire experimental duration. However, we present a case that overlapping epochs may be beneficial in fine-grained analyses of neural entrainment over time. The use of overlapping epochs in such analyses could improve temporal resolution without complicating interpretability of the results in cases where the question of interest relates to relative changes in neural entrainment over time within a given frequency.

Statistical learning of speech regularities can occur outside the focus of attention.

Statistical learning, the process of extracting regularities from the environment, plays an essential role in many aspects of cognition, including speech segmentation and language acquisition. A key component of statistical learning in a linguistic context is the perceptual binding of adjacent individual units (e.g., syllables) into integrated composites (e.g., multisyllabic words). A second, conceptually dissociable component of statistical learning is the memory storage of these integrated representations. Here we examine whether these two dissociable components of statistical learning are differentially impacted by top-down, voluntary attentional resources. Learners' attention was either focused towards or diverted from a speech stream made up of repeating nonsense words. Building on our previous findings, we quantified the online perceptual binding of individual syllables into component words using an EEG-based neural entrainment measure. Following exposure, statistical learning was assessed using offline tests, sensitive to both perceptual binding and memory storage. Neural measures verified that our manipulation of selective attention successfully reduced limited-capacity resources to the speech stream. Diverting attention away from the speech stream did not alter neural entrainment to the component words or post-exposure familiarity ratings, but did impact performance on an indirect reaction-time based memory test. We conclude that theoretically dissociable components of statistically learning are differentially impacted by attention and top-down processing resources. A reduction in attention to the speech stream may impede memory storage of the component words. In contrast, the moment-by-moment perceptual binding of speech regularities can occur even while learners' attention is focused on a demanding concurrent task, and we found no evidence that selective attention modulates this process. These results suggest that learners can acquire basic statistical properties of language without directly focusing on the speech input, potentially opening up previously overlooked opportunities for language learning, particularly in adult learners.

Understanding the Neural Bases of Implicit and Statistical Learning.

Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of "implicit statistical learning." However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with "implicit learning" reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities.

Sleep-based memory processing facilitates grammatical generalization: Evidence from targeted memory reactivation.

Generalization-the ability to abstract regularities from specific examples and apply them to novel instances-is an essential component of language acquisition. Generalization not only depends on exposure to input during wake, but may also improve offline during sleep. Here we examined whether targeted memory reactivation during sleep can influence grammatical generalization. Participants gradually acquired the grammatical rules of an artificial language through an interactive learning procedure. Then, phrases from the language (experimental group) or stimuli from an unrelated task (control group) were covertly presented during an afternoon nap. Compared to control participants, participants re-exposed to the language during sleep showed larger gains in grammatical generalization. Sleep cues produced a bias, not necessarily a pure gain, suggesting that the capacity for memory replay during sleep is limited. We conclude that grammatical generalization was biased by auditory cueing during sleep, and by extension, that sleep likely influences grammatical generalization in general.