Simple statistical regularities presented during sleep are detected but not retained

Chunk Duration Limits the Learning of Multiword Chunks: Behavioral and Electroencephalography Evidence from Statistical Learning

Language comprehension involves the grouping of words into larger multiword chunks. This is required to recode information into sparser representations to mitigate memory limitations and counteract forgetting. It has been suggested that electrophysiological processing time windows constrain the formation of these units. Specifically, the period of rhythmic neural activity (i.e., low-frequency neural oscillations) may set an upper limit of 2-3 sec. Here, we assess whether learning of new multiword chunks is also affected by this neural limit. We applied an auditory statistical learning paradigm of an artificial language while manipulating the duration of to-be-learned chunks. Participants listened to isochronous sequences of disyllabic pseudowords from which they could learn hidden three-word chunks based on transitional probabilities. We presented chunks of 1.95, 2.55, and 3.15 sec that were created by varying the pause interval between pseudowords. In a first behavioral experiment, we tested learning using an implicit target detection task. We found better learning for chunks of 2.55 sec as compared to longer durations in line with an upper limit of the proposed time constraint. In a second experiment, we recorded participants' electroencephalogram during the exposure phase to use frequency tagging as a neural index of statistical learning. Extending the behavioral findings, results show a significant decline in neural tracking for chunks exceeding 3 sec as compared to both shorter durations. Overall, we suggest that language learning is constrained by endogenous time constraints, possibly reflecting electrophysiological processing windows.

Early Caregiver Predictability Shapes Neural Indices of Statistical Learning Later in Infancy

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into how exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants' early environmental input shapes their brains' later ability to learn about predictable information. As part of an ongoing longitudinal study in South Africa, we recorded naturalistic, dyadic interactions between 103 (46 females and 57 males) infants and their primary caregivers at 3-6 months of age, from which we calculated the predictability of caregivers' behavior, following caregiver vocalization and overall. When the same infants were 6-12-months-old they participated in an auditory statistical learning task during EEG. We found evidence of learning-related change in infants' neural responses to predictable information during the statistical learning task. The magnitude of statistical learning-related change in infants' EEG responses was associated with the predictability of their caregiver's vocalizations several months earlier, such that infants with more predictable caregiver vocalization patterns showed more evidence of statistical learning later in the first year of life. These results suggest that early experiences with caregiver predictability influence learning, providing support for the hypothesis that the neurodevelopment of core learning and memory systems is closely tied to infants' experiences during key developmental windows.

The relationship between neural phase entrainment and statistical word-learning: A scoping review

Statistical language-learning, the capacity to extract regularities from a continuous speech stream, arguably involves the ability to segment the stream before the discrete constituents can be stored in memory. According to recent accounts, the segmentation process is reflected in the alignment of neural activity to the statistical structure embedded in the input. However, the degree to which it can predict the subsequent leaning outcome is currently unclear. As this is a relatively new avenue of research on statistical learning, a scoping review approach was adopted to identify and explore the current body of evidence on the use of neural phase entrainment as a measure of online neural statistical language-learning and its relation to the learning outcome, as well as the design characteristics of these studies. All included studies (11) observed entrainment to the underlying statistical pattern with exposure to the structured speech stream. A significant association between entrainment and learning outcome was observed in six of the studies. We discuss these findings in light of what neural entrainment in statistical word-learning experiments might represent, and speculate that it might reflect a general auditory processing mechanism, rather than segmentation of the speech stream per se. Lastly, as we find the current selection of studies to provide inconclusive evidence for neural entrainment's role in statistical learning, future research avenues are proposed.

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.

Sleep-physiological correlates of brachycephaly in dogs

The shape of the cranium is one of the most notable physical changes induced in domestic dogs through selective breeding and is measured using the cephalic index (CI). High CI (a ratio of skull width to skull length > 60) is characterized by a short muzzle and flat face and is referred to as brachycephaly. Brachycephalic dogs display some potentially harmful changes in neuroanatomy, and there are implications for differences in behavior, as well. The path from anatomy to cognition, however, has not been charted in its entirety. Here, we report that sleep-physiological markers of white-matter loss (high delta power, low frontal spindle frequency, i.e., spindle waves/s), along with a spectral profile for REM (low beta, high delta) associated with low intelligence in humans, are each linked to higher CI values in the dog. Additionally, brachycephalic subjects spent more time sleeping, suggesting that the sleep apnea these breeds usually suffer from increases daytime sleepiness. Within sleep, more time was spent in the REM sleep stage than in non-REM, while REM duration was correlated positively with the number of REM episodes across dogs. It is currently not clear if the patterns of sleep and sleep-stage duration are mainly caused by sleep-impairing troubles in breathing and thermoregulation, present a juvenile-like sleeping profile, or are caused by neuro-psychological conditions secondary to the effects of brachycephaly, e.g., frequent REM episodes are known to appear in human patients with depression. While future studies should more directly address the interplay of anatomy, physiology, and behavior within a single experiment, this represents the first description of how the dynamics of the canine brain covary with CI, as measured in resting companion dogs using a non-invasive sleep EEG methodology. The observations suggest that the neuroanatomical changes accompanying brachycephaly alter neural systems in a way that can be captured in the sleep EEG, thus supporting the utility of the latter in the study of canine brain health and function.

Learning during sleep in humans - A historical review

Sleep helps to consolidate previously acquired memories. Whether new information such as languages and other useful skills can also be learned during sleep has been debated for over a century, however, the sporadic studies' different objectives and varied methodologies make it difficult to draw definitive conclusions. This review provides a comprehensive overview of the history of sleep learning research conducted in humans, from its empirical beginnings in the 1940s to the present day. Synthesizing the findings from 51 research papers, we show that several studies support the notion that simpler forms of learning, such as habituation and conditioning, are possible during sleep. In contrast, the findings for more complex, applied learning (e.g., learning a new language during sleep) are more divergent. While there is often an indication of processing and learning during sleep when looking at neural markers, behavioral evidence for the transfer of new knowledge to wake remains inconclusive. We close by critically examining the limitations and assumptions that have contributed to the discrepancies in the literature and highlight promising new directions in the field.

Ready for action! When the brain learns, yet memory-biased action does not follow

Does memory prepare us to act? Long-term memory can facilitate signal detection, though the degree of benefit varies and can even be absent. To dissociate between learning and behavioral expression of learning, we used high-density electroencephalography (EEG) to assess memory retrieval and response processing. At learning, participants heard everyday sounds. Half of these sound clips were paired with an above-threshold lateralized tone, such that it was possible to form incidental associations between the sound clip and the location of the tone. Importantly, attention was directed to either the sound clip (Experiment 1) or the tone (Experiment 2). Participants then completed a novel detection task that separated cued retrieval from response processing. At retrieval, we observed a striking brain-behavior dissociation. Learning was observed neurally in both experiments. Behaviorally, however, signal detection was only facilitated in Experiment 2, for which there was an accompanying explicit memory for tone presence. Further, implicit neural memory for tone location correlated with the degree of response preparation, but not response execution. Together, the findings suggest 1) that attention at learning affects memory-biased action and 2) that memory prepared action via both explicit and implicit associative memory, with the latter triggering response preparation.

Tracking transitional probabilities and segmenting auditory sequences are dissociable processes in adults and neonates

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to four syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates' EEG, compared to adult behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. We also revealed that: (1) Successfully tracking transition probabilities (TP) in a sequence is not sufficient to segment it. (2) Prosodic cues, as subtle as subliminal pauses, enable to recover words segmenting capacities. (3) Adults' and neonates' capacities to segment streams seem remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the overall similarity of neural responses across infants during exposure to the stream, providing a novel neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing them to extract small coherent word-like units from auditory streams, based on the combination of statistical analyses and auditory parsing cues. RESEARCH HIGHLIGHTS: Successfully tracking transitional probabilities in a sequence is not always sufficient to segment it. Word segmentation solely based on transitional probability is limited to bi- or tri-syllabic elements. Prosodic cues, as subtle as subliminal pauses, enable to recover chunking capacities in sleeping neonates and awake adults for quadriplets.

The power of rhythms: how steady-state evoked responses reveal early neurocognitive development

Electroencephalography (EEG) is a non-invasive and painless recording of cerebral activity, particularly well-suited for studying young infants, allowing the inspection of cerebral responses in a constellation of different ways. Of particular interest for developmental cognitive neuroscientists is the use of rhythmic stimulation, and the analysis of steady-state evoked potentials (SS-EPs) - an approach also known as frequency tagging. In this paper we rely on the existing SS-EP early developmental literature to illustrate the important advantages of SS-EPs for studying the developing brain. We argue that (1) the technique is both objective and predictive: the response is expected at the stimulation frequency (and/or higher harmonics), (2) its high spectral specificity makes the computed responses particularly robust to artifacts, and (3) the technique allows for short and efficient recordings, compatible with infants' limited attentional spans. We additionally provide an overview of some recent inspiring use of the SS-EP technique in adult research, in order to argue that (4) the SS-EP approach can be implemented creatively to target a wide range of cognitive and neural processes. For all these reasons, we expect SS-EPs to play an increasing role in the understanding of early cognitive processes. Finally, we provide practical guidelines for implementing and analyzing SS-EP studies.