Evidence of hippocampal learning in human infants

Helpless infants are learning a foundation model

Humans have a protracted postnatal helplessness period, typically attributed to human-specific maternal constraints causing an early birth when the brain is highly immature. By aligning neurodevelopmental events across species, however, it has been found that humans are not born with especially immature brains compared with animal species with a shorter helpless period. Consistent with this, the rapidly growing field of infant neuroimaging has found that brain connectivity and functional activation at birth share many similarities with the mature brain. Inspired by machine learning, where deep neural networks also benefit from a 'helpless period' of pre-training, we propose that human infants are learning a foundation model: a set of fundamental representations that underpin later cognition with high performance and rapid generalisation.

Integrating developmental neuroscience with community-engaged approaches to address mental health outcomes for housing-insecure youth: Implications for research, practice, and policy

One in three children in the United States is exposed to insecure housing conditions, including unaffordable, inconsistent, and unsafe housing. These exposures have detrimental impacts on youth mental health. Delineating the neurobehavioral pathways linking exposure to housing insecurity with children's mental health has the potential to inform interventions and policy. However, in approaching this work, carefully considering the lived experiences of youth and families is essential to translating scientific discovery to improve health outcomes in an equitable and representative way. In the current paper, we provide an introduction to the range of stressful experiences that children may face when exposed to insecure housing conditions. Next, we highlight findings from the early-life stress literature regarding the potential neurobehavioral consequences of insecure housing, focusing on how unpredictability is associated with the neural circuitry supporting cognitive and emotional development. We then delineate how community-engaged research (CEnR) approaches have been leveraged to understand the effects of housing insecurity on mental health, and we propose future research directions that integrate developmental neuroscience research and CEnR approaches to maximize the impact of this work. We conclude by outlining practice and policy recommendations that aim to improve the mental health of children exposed to insecure housing.

Infant neuroscience: how to measure brain activity in the youngest minds

The functional properties of the infant brain are poorly understood. Recent advances in cognitive neuroscience are opening new avenues for measuring brain activity in human infants. These include novel uses of existing technologies such as electroencephalography (EEG) and magnetoencephalography (MEG), the availability of newer technologies including functional near-infrared spectroscopy (fNIRS) and optically pumped magnetometry (OPM), and innovative applications of functional magnetic resonance imaging (fMRI) in awake infants during cognitive tasks. In this review article we catalog these available non-invasive methods, discuss the challenges and opportunities encountered when applying them to human infants, and highlight the potential they may ultimately hold for advancing our understanding of the youngest minds.

Episodic memory development: Bridging animal and human research

Human episodic memory is not functionally evident until about 2 years of age and continues to develop into the school years. Behavioral studies have elucidated this developmental timeline and its constituent processes. In tandem, lesion and neurophysiological studies in non-human primates and rodents have identified key neural substrates and circuit mechanisms that may underlie episodic memory development. Despite this progress, collaborative efforts between psychologists and neuroscientists remain limited, hindering progress. Here, we seek to bridge human and non-human episodic memory development research by offering a comparative review of studies using humans, non-human primates, and rodents. We highlight critical theoretical and methodological issues that limit cross-fertilization and propose a common research framework, adaptable to different species, that may facilitate cross-species research endeavors.

Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain

This review offers an accessible primer to social neuroscientists interested in neural networks. It begins by providing an overview of key concepts in deep learning. It then discusses three ways neural networks can be useful to social neuroscientists: (i) building statistical models to predict behavior from brain activity; (ii) quantifying naturalistic stimuli and social interactions; and (iii) generating cognitive models of social brain function. These applications have the potential to enhance the clinical value of neuroimaging and improve the generalizability of social neuroscience research. We also discuss the significant practical challenges, theoretical limitations and ethical issues faced by deep learning. If the field can successfully navigate these hazards, we believe that artificial neural networks may prove indispensable for the next stage of the field's development: deep social neuroscience.

Sex- and age-based differences in fetal and early childhood hippocampus maturation: a cross-sectional and longitudinal analysis

The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about its development during early fetal life until early childhood. In a cross-sectional and longitudinal study, we examined the sex-, age-, and laterality-related developmental trajectories of hippocampal volumes in fetuses, infants, and toddlers associated with age. Third trimester fetuses (27-38 weeks' gestational age), newborns (0-4 weeks' postnatal age), infants (5-50 weeks' postnatal age), and toddlers (2-3 years postnatal age) were scanned with magnetic resonance imaging. A total of 133 datasets (62 female, postmenstrual age [weeks] M = 69.38, SD = 51.39, range = 27.6-195.3) were processed using semiautomatic segmentation methods. Hippocampal volumes increased exponentially during the third trimester and the first year of life, beginning to slow at approximately 2 years. Overall, boys had larger hippocampal volumes than girls. Lateralization differences were evident, with left hippocampal growth beginning to plateau sooner than the right. This period of rapid growth from the third trimester, continuing through the first year of life, may support the development of cognitive and affective function during this period.

Multiple Memory Subsystems: Reconsidering Memory in the Mind and Brain

The multiple-memory-systems framework-that distinct types of memory are supported by distinct brain systems-has guided learning and memory research for decades. However, recent work challenges the one-to-one mapping between brain structures and memory types central to this taxonomy, with key memory-related structures supporting multiple functions across substructures. Here we integrate cross-species findings in the hippocampus, striatum, and amygdala to propose an updated framework of multiple memory subsystems (MMSS). We provide evidence for two organizational principles of the MMSS theory: First, opposing memory representations are colocated in the same brain structures; second, parallel memory representations are supported by distinct structures. We discuss why this burgeoning framework has the potential to provide a useful revision of classic theories of long-term memory, what evidence is needed to further validate the framework, and how this novel perspective on memory organization may guide future research.

A neural network model of hippocampal contributions to category learning

In addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this form of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network's ability to categorize and recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus-connecting entorhinal cortex to dentate gyrus, CA3, and CA1-was critical for remembering exemplar-specific information, reflecting the rapid binding and pattern separation capabilities of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, specialized in detecting the regularities that define category structure across exemplars, supported by the use of distributed representations and a relatively slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.

Expectation adaptation for rare cadences in music: Item order matters in repetition priming

Humans make predictions about future events in many domains, including when they listen to music. Previous accounts of harmonic expectation in music have emphasised the role of implicit musical knowledge acquired in the long term through the mechanism of statistical learning. However, it is not known whether listeners can adapt their expectations for unusual harmonies in the short term through repetition priming, and whether the extent of any short-term adaptation depends on the unfolding statistical structure of the music. To explore these possibilities, we presented 150 participants with phrases from Bach chorales that ended with a cadence that was either a priori likely or unlikely based on the long-term statistical structure of the corpus of chorales. While holding the 50-50 incidence of likely vs. unlikely cadences constant, we manipulated the order in which these phrases were presented such that the local probability of hearing an unlikely cadence changed throughout the experiment. For each phrase, participants provided two judgements: (a) a prospective rating of how confident they were in their expectations for the cadence, and (b) a retrospective rating of how well the presented cadence matched their expectations. While confidence ratings increased over the course of the experiment, the rate of change decreased as the local probability of an unexpected cadence increased. Participants' expectations favoured likely cadences over unlikely cadences on average, but their expectation ratings for unlikely cadences increased at a faster rate over the course of the experiment than for likely cadences, particularly when the local probability of hearing an unlikely cadence was high. Thus, despite entrenched long-term statistics about cadences, listeners can indeed adapt to unusual musical harmonies and are sensitive to the local statistical structure of the musical environment. We suggest that this adaptation is an instance of Bayesian belief updating, a domain-general process that accounts for expectation adaptation in multiple domains.

Four-week repeated exposure to tire-derived 6-PPD quinone causes multiple organ injury in male BALB/c mice

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6-PPDQ) is the ozonation product of tire antioxidant 6-PPD. 6-PPDQ can be detected in different environments, such as roadway runoff and dust. Although 6-PPDQ toxicity has been frequently assessed in aquatic organisms, the possible toxic effects of 6-PPDQ on mammals remain largely unclear. We here aimed to perform systematic assessment to evaluate 6-PPDQ toxicity on multiple organs in mice. Male BALB/c mice were intraperitoneally injected with 6-PPDQ for two exposure modes, single intraperitoneal injection and repeated intraperitoneal injection every four days for 28 days. Serum, liver, kidney, lung, spleen, testis, brain, and heart were collected for injury evaluation by organ index, histopathology analysis and biochemical parameters. In 0.4 and 4 mg/kg 6-PPDQ single injected mice, no significant changes in organ indexes and biochemical parameters were detected, and only moderate pathological changes were observed in organs of liver, kidney, lung, and brain. Very different from this, in 0.4 and 4 mg/kg 6-PPDQ repeated injected mice, we observed the obvious increase in organ indexes of liver, kidney, lung, testis, and brain, and the decrease in spleen index. Meanwhile, the significant pathological changes were formed in liver, kidney, lung, spleen, testis, and brain in 0.4 and 4 mg/kg 6-PPDQ repeated injected mice. Biochemical parameters of liver (alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP)) and kidney (urea and creatinine) were all significantly upregulated by repeated injection with 0.4 and 4 mg/kg 6-PPDQ. After repeated exposure, most of 6-PPDQ was accumulated in liver and lung of mice. Therefore, our results suggested the risk of repeated exposure to 6-PPDQ in inducing toxicity on multiple organs in mice.

Accounting for multiscale processing in adaptive real-world decision-making via the hippocampus

For adaptive real-time behavior in real-world contexts, the brain needs to allow past information over multiple timescales to influence current processing for making choices that create the best outcome as a person goes about making choices in their everyday life. The neuroeconomics literature on value-based decision-making has formalized such choice through reinforcement learning models for two extreme strategies. These strategies are model-free (MF), which is an automatic, stimulus-response type of action, and model-based (MB), which bases choice on cognitive representations of the world and causal inference on environment-behavior structure. The emphasis of examining the neural substrates of value-based decision making has been on the striatum and prefrontal regions, especially with regards to the "here and now" decision-making. Yet, such a dichotomy does not embrace all the dynamic complexity involved. In addition, despite robust research on the role of the hippocampus in memory and spatial learning, its contribution to value-based decision making is just starting to be explored. This paper aims to better appreciate the role of the hippocampus in decision-making and advance the successor representation (SR) as a candidate mechanism for encoding state representations in the hippocampus, separate from reward representations. To this end, we review research that relates hippocampal sequences to SR models showing that the implementation of such sequences in reinforcement learning agents improves their performance. This also enables the agents to perform multiscale temporal processing in a biologically plausible manner. Altogether, we articulate a framework to advance current striatal and prefrontal-focused decision making to better account for multiscale mechanisms underlying various real-world time-related concepts such as the self that cumulates over a person's life course.

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.

Integrating cognition in the laboratory with cognition in the real world: the time cognition takes, task fidelity, and finding tasks when they are mixed together

It is now possible for real-life activities, unfolding over their natural range of temporal and spatial scales, to become the primary targets of cognitive studies. Movement toward this type of research will require an integrated methodological approach currently uncommon in the field. When executed hand in hand with thorough and ecologically valid empirical description, properly developed laboratory tasks can serve as model systems to capture the essentials of a targeted real-life activity. When integrated together, data from these two kinds of studies can facilitate causal analysis and modeling of the mental and neural processes that govern that activity, enabling a fuller account than either method can provide on its own. The resulting account, situated in the activity's natural environmental, social, and motivational context, can then enable effective and efficient development of interventions to support and improve the activity as it actually unfolds in real time. We believe that such an integrated multi-level research program should be common rather than rare and is necessary to achieve scientifically and societally important goals. The time is right to finally abandon the boundaries that separate the laboratory from the outside world.

A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics

The hippocampal subfields, pivotal to episodic memory, are distinct both in terms of cyto- and myeloarchitectony. Studying the structure of hippocampal subfields in vivo is crucial to understand volumetric trajectories across the lifespan, from the emergence of episodic memory during early childhood to memory impairments found in older adults. However, segmenting hippocampal subfields on conventional MRI sequences is challenging because of their small size. Furthermore, there is to date no unified segmentation protocol for the hippocampal subfields, which limits comparisons between studies. Therefore, we introduced a novel segmentation tool called HSF short for hippocampal segmentation factory, which leverages an end-to-end deep learning pipeline. First, we validated HSF against currently used tools (ASHS, HIPS, and HippUnfold). Then, we used HSF on 3,750 subjects from the HCP development, young adults, and aging datasets to study the effect of age and sex on hippocampal subfields volumes. Firstly, we showed HSF to be closer to manual segmentation than other currently used tools (p < 0.001), regarding the Dice Coefficient, Hausdorff Distance, and Volumetric Similarity. Then, we showed differential maturation and aging across subfields, with the dentate gyrus being the most affected by age. We also found faster growth and decay in men than in women for most hippocampal subfields. Thus, while we introduced a new, fast and robust end-to-end segmentation tool, our neuroanatomical results concerning the lifespan trajectories of the hippocampal subfields reconcile previous conflicting results.

Baby Brains at Work: How Task-Based Functional Magnetic Resonance Imaging Can Illuminate the Early Emergence of Psychiatric Risk

Psychiatric disorders are complex, often emerging from multiple atypical processes within specified domains over the course of development. Characterizing the development of the neural circuits supporting these domains may help break down the components of complex disorders and reveal variations in functioning associated with psychiatric risk. This review highlights the current and potential role of infant task-based functional magnetic resonance imaging (fMRI) in elucidating the developmental neurobiology of psychiatric disorders. Task-fMRI measures evoked brain activity in response to specific stimuli through changes in the blood oxygen level-dependent signal. First, we review extant studies using task fMRI from birth through the first few years of life and synthesize current evidence for when, where, and how different neural computations are performed across the infant brain. Neural circuits for sensory perception, the perception of abstract categories, and the detection of statistical regularities have been characterized with task fMRI in infants, providing developmental context for identifying and interpreting variation in the functioning of neural circuits related to psychiatric risk. Next, we discuss studies that specifically examine variation in the functioning of these neural circuits during infancy in relation to risk for psychiatric disorders. These studies reveal when maturation of specific neural circuits diverges, the influence of environmental risk factors, and the potential utility for task fMRI to facilitate early treatment or prevention of later psychiatric problems. Finally, we provide considerations for future infant task-fMRI studies with the potential to advance understanding of both functioning of neural circuits during infancy and subsequent risk for psychiatric disorders.

The development of intrinsic timescales: A comparison between the neonate and adult brain

In human adults and other mammals, different brain regions have distinct intrinsic timescales over which they integrate information, from shorter in unimodal sensory-motor regions to longer in transmodal higher-order regions. These have been related to cognitive performance and clinical symptoms, but it remains unclear how they develop. We asked if there are regional differences in timescales at birth that could shape learning by acting as an inductive bias, or if they develop later as the temporal statistics of the environment are learned. We used resting-state fMRI to characterise timescales in human neonates and adults. They were highly consistent across two independent neonatal groups, but in both sensory-motor and higher order areas, timescales were longer in infants compared to adults, as might be expected from their less developed myelination, and recent evidence of longer neural segments in infants watching naturalistic stimuli. In adults, we replicated the finding that transmodal areas have longer timescales than sensory-motor areas, but in infants the opposite pattern was found, driven by long infant timescales in the somotomotor network. Across regions within single brain networks, both positive (limbic) and negative (visual) correlations were found between neonates and adults. In conclusion, neonatal timescales were found to be highly structured, but distinct from adults, suggesting they act as an inductive bias that favours learning on longer timescales, particularly in unimodal regions and then develop with experience or maturation. This "take it slow" initial approach might help human infants to create more regularised, holistic representations of the input less bound to fleeting details, which would favour the development of abstract and contextual representations.

A shift in the mechanisms controlling hippocampal engram formation during brain maturation

The ability to form precise, episodic memories develops with age, with young children only able to form gist-like memories that lack precision. The cellular and molecular events in the developing hippocampus that underlie the emergence of precise, episodic-like memory are unclear. In mice, the absence of a competitive neuronal engram allocation process in the immature hippocampus precluded the formation of sparse engrams and precise memories until the fourth postnatal week, when inhibitory circuits in the hippocampus mature. This age-dependent shift in precision of episodic-like memories involved the functional maturation of parvalbumin-expressing interneurons in subfield CA1 through assembly of extracellular perineuronal nets, which is necessary and sufficient for the onset of competitive neuronal allocation, sparse engram formation, and memory precision.

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.

Automated and manual segmentation of the hippocampus in human infants

The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the "gold standard" approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these automated protocols in infants, particularly when anatomical scan quality is degraded by head motion or the use of shorter and quieter infant-friendly sequences. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4-23 months. Two expert tracers first segmented the hippocampus in both hemispheres manually. The resulting inter-rater reliability (IRR) was only moderate, reflecting the difficulty of infant segmentation. We then used four protocols to predict these manual segmentations: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding the manual IRR of experts. Automated methods thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development.

Face processing in the infant brain after pandemic lockdown

The role of visual experience in the development of face processing has long been debated. We present a new angle on this question through a serendipitous study that cannot easily be repeated. Infants viewed short blocks of faces during fMRI in a repetition suppression task. The same identity was presented multiple times in half of the blocks (repeat condition) and different identities were presented once each in the other half (novel condition). In adults, the fusiform face area (FFA) tends to show greater neural activity for novel versus repeat blocks in such designs, suggesting that it can distinguish same versus different face identities. As part of an ongoing study, we collected data before the COVID-19 pandemic and after an initial local lockdown was lifted. The resulting sample of 12 infants (9-24 months) divided equally into pre- and post-lockdown groups with matching ages and data quantity/quality. The groups had strikingly different FFA responses: pre-lockdown infants showed repetition suppression (novel > repeat), whereas post-lockdown infants showed the opposite (repeat > novel), often referred to as repetition enhancement. These findings provide speculative evidence that altered visual experience during the lockdown, or other correlated environmental changes, may have affected face processing in the infant brain.

Memory for nonadjacent dependencies in the first year of life and its relation to sleep

Grammar learning requires memory for dependencies between nonadjacent elements in speech. Immediate learning of nonadjacent dependencies has been observed in very young infants, but their memory of such dependencies has remained unexplored. Here we used event-related potentials to investigate whether 6- to 8-month-olds retain nonadjacent dependencies and if sleep after learning affects this memory. Infants were familiarised with two rule-based morphosyntactic dependencies, presented in sentences of an unknown language. Brain responses after a retention period reveal memory of the nonadjacent dependencies, independent of whether infants napped or stayed awake. Napping, however, altered a specific processing stage, suggesting that memory evolves during sleep. Infants with high left frontal spindle activity show an additional brain response indicating memory of individual speech phrases. Results imply that infants as young as 6 months are equipped with memory mechanisms relevant to grammar learning. They also suggest that during sleep, consolidation of highly specific information can co-occur with changes in the nature of generalised memory.

Has the concept of systems consolidation outlived its usefulness? Identification and evaluation of premises underlying systems consolidation

Systems consolidation has mostly been treated as a neural construct defined by the time-dependent change in memory representation from the hippocampus (HPC) to other structures, primarily the neocortex. Here, we identify and evaluate the explicit and implicit premises that underlie traditional or standard models and theories of systems consolidation based on evidence from research on humans and other animals. We use the principle that changes in neural representation over time and experience are accompanied by corresponding changes in psychological representations, and vice versa, to argue that each of the premises underlying traditional or standard models and theories of systems consolidation is found wanting. One solution is to modify or abandon the premises or theories and models. This is reflected in moderated models of systems consolidation that emphasize the early role of the HPC in training neocortical memories until they stabilize. The fault, however, may lie in the very concept of systems consolidation and its defining feature. We propose that the concept be replaced by one of memory systems reorganization, which does not carry the theoretical baggage of systems consolidation and is flexible enough to capture the dynamic nature of memory from inception to very long-term retention and retrieval at a psychological and neural level. The term "memory system reorganization" implies that memory traces are not fixed, even after they are presumably consolidated. Memories can continue to change as a result of experience and interactions among memory systems across the lifetime. As will become clear, hippocampal training of neocortical memories is only one type of such interaction, and not always the most important one, even at inception. We end by suggesting some principles of memory reorganization that can help guide research on dynamic memory processes that capture corresponding changes in memory at the psychological and neural levels.

Contributions of memory and brain development to the bioregulation of naps and nap transitions in early childhood

The transition from multiple sleep bouts each day to a single overnight sleep bout (i.e., nap transition) is a universal process in human development. Naps are important during infancy and early childhood as they enhance learning through memory consolidation. However, a normal part of development is the transition out of naps. Understanding nap transitions is essential in order to maximize early learning and promote positive long-term cognitive outcomes. Here, we propose a novel hypothesis regarding the cognitive, physiological, and neural changes that accompany nap transitions. Specifically, we posit that maturation of the hippocampal-dependent memory network results in more efficient memory storage, which reduces the buildup of homeostatic sleep pressure across the cortex (as reflected by slow-wave activity), and eventually, contributes to nap transitions. This hypothesis synthesizes evidence of bioregulatory mechanisms underlying nap transitions and sheds new light on an important window of change in development. This framework can be used to evaluate multiple untested predictions from the field of sleep science and ultimately, yield science-based guidelines and policies regarding napping in childcare and early education settings.

Neural event segmentation of continuous experience in human infants

How infants experience the world is fundamental to understanding their cognition and development. A key principle of adult experience is that, despite receiving continuous sensory input, we perceive this input as discrete events. Here we investigate such event segmentation in infants and how it differs from adults. Research on event cognition in infants often uses simplified tasks in which (adult) experimenters help solve the segmentation problem for infants by defining event boundaries or presenting discrete actions/vignettes. This presupposes which events are experienced by infants and leaves open questions about the principles governing infant segmentation. We take a different, data-driven approach by studying infant event segmentation of continuous input. We collected whole-brain functional MRI (fMRI) data from awake infants (and adults, for comparison) watching a cartoon and used a hidden Markov model to identify event states in the brain. We quantified the existence, timescale, and organization of multiple-event representations across brain regions. The adult brain exhibited a known hierarchical gradient of event timescales, from shorter events in early visual regions to longer events in later visual and associative regions. In contrast, the infant brain represented only longer events, even in early visual regions, with no timescale hierarchy. The boundaries defining these infant events only partially overlapped with boundaries defined from adult brain activity and behavioral judgments. These findings suggest that events are organized differently in infants, with longer timescales and more stable neural patterns, even in sensory regions. This may indicate greater temporal integration and reduced temporal precision during dynamic, naturalistic perception.

Everyday Parameters for Episode-to-Episode Dynamics in the Daily Music of Infancy

Experience‐dependent change pervades early human development. Though trajectories of developmental change have been well charted in many domains, the episode‐to‐episode schedules of experiences on which they are hypothesized to depend have not. Here, we took up this issue in a domain known to be governed in part by early experiences: music. Using a corpus of longform audio recordings, we parameterized the daily schedules of music encountered by 35 infants ages 6–12 months. We discovered that everyday music episodes, as well as the interstices between episodes, typically persisted less than a minute, with most daily schedules also including some very extended episodes and interstices. We also discovered that infants encountered music episodes in a bursty rhythm, rather than a periodic or random rhythm, over the day. These findings join a suite of recent discoveries from everyday vision, motor, and language that expand our imaginations beyond artificial learning schedules and enable theorists to model the history‐dependence of developmental process in ways that respect everyday sensory histories. Future theories about how infants build knowledge across multiple episodes can now be parameterized using these insights from infants’ everyday lives.

Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion

During the early postnatal years, most infants rapidly learn to understand two naturally evolved communication systems: language and emotion. While these two domains include different types of content knowledge, it is possible that similar learning processes subserve their acquisition. In this review, we compare the learnable statistical regularities in language and emotion input. We then consider how domain-general learning abilities may underly the acquisition of language and emotion, and how this process may be constrained in each domain. This comparative developmental approach can advance our understanding of how humans learn to communicate with others.

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.

Cortical Source Analysis of Event-Related Potentials: A Developmental Approach

Cortical source analysis of electroencephalographic (EEG) signals has become an important tool in the analysis of brain activity. The aim of source analysis is to reconstruct the cortical generators (sources) of the EEG signal recorded on the scalp. The quality of the source reconstruction relies on the accuracy of the forward problem, and consequently the inverse problem. An accurate forward solution is obtained when an appropriate imaging modality (i.e., structural magnetic resonance imaging - MRI) is used to describe the head geometry, precise electrode locations are identified with 3D maps of the sensor positions on the scalp, and realistic conductivity values are determined for each tissue type of the head model. Together these parameters contribute to the definition of realistic head models. Here, we describe the steps necessary to reconstruct the cortical generators of the EEG signal recorded on the scalp. We provide an example of source reconstruction of event-related potentials (ERPs) during a face-processing task performed by a 6-month-old infant. We discuss the adjustments necessary to perform source analysis with measures different from the ERPs. The proposed pipeline can be applied to the investigation of different cognitive tasks in both younger and older participants.

Sleeping neonates track transitional probabilities in speech but only retain the first syllable of words

Extracting statistical regularities from the environment is a primary learning mechanism that might support language acquisition. While it has been shown that infants are sensitive to transition probabilities between syllables in speech, it is still not known what information they encode. Here we used electrophysiology to study how full-term neonates process an artificial language constructed by randomly concatenating four pseudo-words and what information they retain after a few minutes of exposure. Neural entrainment served as a marker of the regularities the brain was tracking during learning. Then in a post-learning phase, evoked-related potentials (ERP) to different triplets explored which information was retained. After two minutes of familiarization with the artificial language, neural entrainment at the word rate emerged, demonstrating rapid learning of the regularities. ERPs in the test phase significantly differed between triplets starting or not with the correct first syllables, but no difference was associated with subsequent violations in transition probabilities. Thus, our results revealed a two-step learning process: neonates segmented the stream based on its statistical regularities, but memory encoding targeted during the word recognition phase entangled the ordinal position of the syllables but was still incomplete at that age.

The Role of Implicit Memory in the Development and Recovery from Trauma-Related Disorders

Post-traumatic Stress Disorder is a chronic condition that occurs following a traumatic experience. Information processing models of PTSD focus on integrating situationally triggered sensory-emotional memories with consciously accessible autobiographical memories. Review of the nature of implicit memory supports the view that sensory-emotional memories are implicit in nature. Dissociation was also found to be associated with the development and severity of PTSD, as well as deficits in autobiographical memory. Moreover, disorganized attachment (DA) was associated with greater degrees of dissociation and PTSD, and like the defining neural activation in PTSD, was found to be associated with basal ganglia activity. In addition, subcortical neuroception of safety promotes a neurophysiological substrate supportive of social engagement and inhibition of fear-based responses. Furthermore, activation of representations of co-created imagined scenes of safety and secure attachment are associated with increases in this neurophysiological substrate. Repeated priming of secure attachment imagery was associated with modification of internal working models of DA along with reductions in dissociation and recovery from complex PTSD. In conclusion, it is posited that adequate recovery from extensive trauma experiences requires more than conscious elaboration of traumatic autobiographical memories and that the application of implicit nonconscious memory modification strategies will facilitate more optimal recovery.

OUP accepted manuscript

One of the great frontiers of consciousness science is understanding how early consciousness arises in the development of the human infant. The reciprocal relationship between the default mode network and fronto-parietal networks—the dorsal attention and executive control network—is thought to facilitate integration of information across the brain and its availability for a wide set of conscious mental operations. It remains unknown whether the brain mechanism of conscious awareness is instantiated in infants from birth. To address this gap, we investigated the development of the default mode and fronto-parietal networks and of their reciprocal relationship in neonates. To understand the effect of early neonate age on these networks, we also assessed neonates born prematurely or before term-equivalent age. We used the Developing Human Connectome Project, a unique Open Science dataset which provides a large sample of neonatal functional MRI data with high temporal and spatial resolution. Resting state functional MRI data for full-term neonates (n = 282, age 41.2 weeks ± 12 days) and preterm neonates scanned at term-equivalent age (n = 73, 40.9 weeks ± 14.5 days), or before term-equivalent age (n = 73, 34.6 weeks ± 13.4 days), were obtained from the Developing Human Connectome Project, and for a reference adult group (n = 176, 22–36 years), from the Human Connectome Project. For the first time, we show that the reciprocal relationship between the default mode and dorsal attention network was present at full-term birth or term-equivalent age. Although different from the adult networks, the default mode, dorsal attention and executive control networks were present as distinct networks at full-term birth or term-equivalent age, but premature birth was associated with network disruption. By contrast, neonates before term-equivalent age showed dramatic underdevelopment of high-order networks. Only the dorsal attention network was present as a distinct network and the reciprocal network relationship was not yet formed. Our results suggest that, at full-term birth or by term-equivalent age, infants possess key features of the neural circuitry that enables integration of information across diverse sensory and high-order functional modules, giving rise to conscious awareness. Conversely, they suggest that this brain infrastructure is not present before infants reach term-equivalent age. These findings improve understanding of the ontogeny of high-order network dynamics that support conscious awareness and of their disruption by premature birth.

The hippocampal formation and action at a distance

The question of why our conceptions of space and time are intertwined with memory in the hippocampal formation is at the forefront of much current theorizing about this brain system. In this article I argue that animals bridge spatial and temporal gaps through the creation of internal models that allow them to act on the basis of things that exist in a distant place and/or existed at a different time. The hippocampal formation plays a critical role in these processes by stitching together spatiotemporally disparate entities and events. It does this by 1) constructing cognitive maps that represent extended spatial contexts, incorporating and linking aspects of an environment that may never have been experienced together; 2) creating neural trajectories that link the parts of an event, whether they occur in close temporal proximity or not, enabling the construction of event representations even when elements of that event were experienced at quite different times; and 3) using these maps and trajectories to simulate possible futures. As a function of these hippocampally driven processes, our subjective sense of both space and time are interwoven constructions of the mind, much as the philosopher Immanuel Kant postulated.

Affective regulation through touch: homeostatic and allostatic mechanisms

We focus on social touch as a paradigmatic case of the embodied, cognitive, and metacognitive processes involved in social, affective regulation. Social touch appears to contribute three interrelated but distinct functions to affective regulation. First, it regulates affects by fulfilling embodied predictions about social proximity and attachment. Second, caregiving touch, such as warming an infant, regulates affect by socially enacting homeostatic control and co-regulation of physiological states. Third, affective touch such as gentle stroking or tickling regulates affect by allostatic regulation of the salience and epistemic gain of particular experiences in given contexts and timescales. These three functions of affective touch are most likely mediated, at least partly, by different neurobiological processes, including convergent hedonic, dopaminergic and analgesic, opioidergic pathways for the attachment function, 'calming' autonomic and endocrine pathways for the homeostatic function, while the allostatic function may be mediated by oxytocin release and related 'salience' neuromodulators and circuits.

Some implications of postnatal hippocampal development

It is well established that in most species, the hippocampus shows extensive postnatal development. This delayed maturation has a number of implications, which can be thought of in three categories. First, the late maturation has the direct effect of depriving the developing organism of at least some of the functions of the hippocampus, in particular place learning, context coding and in humans, episodic memory. Second, such learning that does occur very early in life, prior to hippocampal maturation, will largely bear the imprint and properties of those brain systems that, unlike the hippocampus, are fully functional early in life. Third, the active state of development of hippocampus in the first weeks and months of life render this structure susceptible to disruption by environmental and/or chromosomal factors. In this article, I discuss my efforts, with many colleagues over the past 40 years, to understand each of these implications.