Mother-infant social gaze dynamics relate to infant brain activity and word segmentation.

The 'social brain', consisting of areas sensitive to social information, supposedly gates the mechanisms involved in human language learning. Early preverbal interactions are guided by ostensive signals, such as gaze patterns, which are coordinated across body, brain, and environment. However, little is known about how the infant brain processes social gaze in naturalistic interactions and how this relates to infant language development. During free-play of 9-month-olds with their mothers, we recorded hemodynamic cortical activity of ´social brain` areas (prefrontal cortex, temporo-parietal junctions) via fNIRS, and micro-coded mother's and infant's social gaze. Infants' speech processing was assessed with a word segmentation task. Using joint recurrence quantification analysis, we examined the connection between infants' ´social brain` activity and the temporal dynamics of social gaze at intrapersonal (i.e., infant's coordination, maternal coordination) and interpersonal (i.e., dyadic coupling) levels. Regression modeling revealed that intrapersonal dynamics in maternal social gaze (but not infant's coordination or dyadic coupling) coordinated significantly with infant's cortical activity. Moreover, recurrence quantification analysis revealed that intrapersonal maternal social gaze dynamics (in terms of entropy) were the best predictor of infants' word segmentation. The findings support the importance of social interaction in language development, particularly highlighting maternal social gaze dynamics.

Visual category representations in the infant brain.

Visual categorization is a human core cognitive capacity1,2 that depends on the development of visual category representations in the infant brain.3,4,5,6,7 However, the exact nature of infant visual category representations and their relationship to the corresponding adult form remains unknown.8 Our results clarify the nature of visual category representations from electroencephalography (EEG) data in 6- to 8-month-old infants and their developmental trajectory toward adult maturity in the key characteristics of temporal dynamics,2,9 representational format,10,11,12 and spectral properties.13,14 Temporal dynamics change from slowly emerging, developing representations in infants to quickly emerging, complex representations in adults. Despite those differences, infants and adults already partly share visual category representations. The format of infants' representations is visual features of low to intermediate complexity, whereas adults' representations also encode high-complexity features. Theta band activity contributes to visual category representations in infants, and these representations are shifted to the alpha/beta band in adults. Together, we reveal the developmental neural basis of visual categorization in humans, show how information transmission channels change in development, and demonstrate the power of advanced multivariate analysis techniques in infant EEG research for theory building in developmental cognitive science.

The role of mother-infant emotional synchrony in speech processing in 9-month-old infants.

Rhythmicity characterizes both interpersonal synchrony and spoken language. Emotions and language are forms of interpersonal communication, which interact with each other throughout development. We investigated whether and how emotional synchrony between mothers and their 9-month-old infants relates to infants' word segmentation as an early marker of language development. Twenty-six 9-month-old infants and their German-speaking mothers took part in the study. To measure emotional synchrony, we coded positive, neutral and negative emotional expressions of the mothers and their infants during a free play session. We then calculated the degree to which the mothers' and their infants' matching emotional expressions followed a predictable pattern. To measure word segmentation, we familiarized infants with auditory text passages and tested how long they looked at the screen while listening to familiar versus novel words. We found that higher levels of predictability (i.e. low entropy) during mother-infant interaction is associated with infants' word segmentation performance. These findings suggest that individual differences in word segmentation relate to the complexity and predictability of emotional expressions during mother-infant interactions.

DEEP: A dual EEG pipeline for developmental hyperscanning studies.

Cutting-edge hyperscanning methods led to a paradigm shift in social neuroscience. It allowed researchers to measure dynamic mutual alignment of neural processes between two or more individuals in naturalistic contexts. The ever-growing interest in hyperscanning research calls for the development of transparent and validated data analysis methods to further advance the field. We have developed and tested a dual electroencephalography (EEG) analysis pipeline, namely DEEP. Following the preprocessing of the data, DEEP allows users to calculate Phase Locking Values (PLVs) and cross-frequency PLVs as indices of inter-brain phase alignment of dyads as well as time-frequency responses and EEG power for each participant. The pipeline also includes scripts to control for spurious correlations. Our goal is to contribute to open and reproducible science practices by making DEEP publicly available together with an example mother-infant EEG hyperscanning dataset.

Enhancing reproducibility in developmental EEG research: BIDS, cluster-based permutation tests, and effect sizes.

Developmental research using electroencephalography (EEG) offers valuable insights in brain processes early in life, but at the same time, applying this sensitive technique to young children who are often non-compliant and have short attention spans comes with practical limitations. It is thus of particular importance to optimally use the limited resources to advance our understanding of development through reproducible and replicable research practices. Here, we describe methodological approaches that help maximize the reproducibility of developmental EEG research. We discuss how to transform EEG data into the standardized Brain Imaging Data Structure (BIDS) which organizes data according to the FAIR data sharing principles. We provide a tutorial on how to use cluster-based permutation testing to analyze developmental EEG data. This versatile test statistic solves the multiple comparison problem omnipresent in EEG analysis and thereby substantially decreases the risk of reporting false discoveries. Finally, we describe how to quantify effect sizes, in particular of cluster-based permutation results. Reporting effect sizes conveys a finding's impact and robustness which in turn informs future research. To demonstrate these methodological approaches to data organization, analysis and report, we use a publicly accessible infant EEG dataset and provide a complete copy of the analysis code.

An Embodied Cognition Perspective on the Role of Interoception in the Development of the Minimal Self.

Interoception is an often neglected but crucial aspect of the human minimal self. In this perspective, we extend the embodiment account of interoceptive inference to explain the development of the minimal self in humans. To do so, we first provide a comparative overview of the central accounts addressing the link between interoception and the minimal self. Grounding our arguments on the embodiment framework, we propose a bidirectional relationship between motor and interoceptive states, which jointly contribute to the development of the minimal self. We present empirical findings on interoception in development and discuss the role of interoception in the development of the minimal self. Moreover, we make theoretical predictions that can be tested in future experiments. Our goal is to provide a comprehensive view on the mechanisms underlying the minimal self by explaining the role of interoception in the development of the minimal self.

Keeping the Breath in Mind: Respiration, Neural Oscillations, and the Free Energy Principle.

Scientific interest in the brain and body interactions has been surging in recent years. One fundamental yet underexplored aspect of brain and body interactions is the link between the respiratory and the nervous systems. In this article, we give an overview of the emerging literature on how respiration modulates neural, cognitive and emotional processes. Moreover, we present a perspective linking respiration to the free-energy principle. We frame volitional modulation of the breath as an active inference mechanism in which sensory evidence is recontextualized to alter interoceptive models. We further propose that respiration-entrained gamma oscillations may reflect the propagation of prediction errors from the sensory level up to cortical regions in order to alter higher level predictions. Accordingly, controlled breathing emerges as an easily accessible tool for emotional, cognitive, and physiological regulation.

Interpersonal Neural Synchrony During Father-Child Problem Solving: An fNIRS Hyperscanning Study.

Interpersonal neural synchrony (INS) has been previously evidenced in mother-child interactions, yet findings concerning father-child interaction are wanting. The current experiment examined whether fathers and their 5- to 6-year-old children (N = 66) synchronize their brain activity during a naturalistic interaction, and addressed paternal and child factors related to INS. Compared to individual problem solving and rest, father-child dyads showed increased INS in bilateral dorsolateral prefrontal cortex and left temporo-parietal junction during cooperative problem solving. Furthermore, the father's attitude toward his role as a parent was positively related to INS during the cooperation condition. These results highlight the implication of the father's attitude to parenting in INS processes for the first time.

Effects of Reinforcement Learning on Gaze Following of Gaze and Head Direction in Early Infancy: An Interactive Eye-Tracking Study.

The current four experiments investigated gaze following behavior in response to gaze and head turns in 4-month-olds and how reinforcement learning influences this behavior (N = 99). Using interactive eye tracking, infants' gaze elicited an animation whenever infants followed a person's head or gaze orientation (Experiment 1.1, 2.1 and 2.2) or looked at the opposite side (Experiment 1.2). Infants spontaneously followed the direction of a turning head with and without simultaneously shifted gaze direction (Cohen's d: 0.93-1.05) but not the direction of isolated gaze shifts. We only found a weak effect of reinforcement on gaze following in one of the four experiments. Results will be discussed with regard to the impact of reinforcement on the maintenance of already existing gaze following behavior.

Neural synchrony in mother-child conversation: Exploring the role of conversation patterns.

Conversations are an essential form of communication in daily family life. Specific patterns of caregiver-child conversations have been linked to children's socio-cognitive development and child-relationship quality beyond the immediate family environment. Recently, interpersonal neural synchronization has been proposed as a neural mechanism supporting conversation. Here, we present a functional near-infrared spectroscopy (fNIRS) hyperscanning study looking at the temporal dynamics of neural synchrony during mother-child conversation. Preschoolers (20 boys and 20 girls, M age 5;07 years) and their mothers (M age 36.37 years) were tested simultaneously with fNIRS hyperscanning while engaging in a free verbal conversation lasting for 4 min. Neural synchrony (using wavelet transform coherence analysis) was assessed over time. Furthermore, each conversational turn was coded for conversation patterns comprising turn-taking, relevance, contingency and intrusiveness. Results from linear mixed-effects modeling revealed that turn-taking, but not relevance, contingency or intrusiveness predicted neural synchronization during the conversation over time. Results are discussed to point out possible variables affecting parent-child conversation quality and the potential functional role of interpersonal neural synchronization for parent-child conversation.

Making Sense of the World: Infant Learning From a Predictive Processing Perspective.

For human infants, the first years after birth are a period of intense exploration-getting to understand their own competencies in interaction with a complex physical and social environment. In contemporary neuroscience, the predictive-processing framework has been proposed as a general working principle of the human brain, the optimization of predictions about the consequences of one's own actions, and sensory inputs from the environment. However, the predictive-processing framework has rarely been applied to infancy research. We argue that a predictive-processing framework may provide a unifying perspective on several phenomena of infant development and learning that may seem unrelated at first sight. These phenomena include statistical learning principles, infants' motor and proprioceptive learning, and infants' basic understanding of their physical and social environment. We discuss how a predictive-processing perspective can advance the understanding of infants' early learning processes in theory, research, and application.

The effects of interaction quality on neural synchrony during mother-child problem solving.

Understanding others is fundamental to interpersonal coordination and successful cooperation. One mechanism posited to underlie both effective communication and behavioral coordination is interpersonal neural synchrony. Although presumably foundational for children's social development, research on neural synchrony in naturalistic caregiver-child interactions is lacking. Using dual-functional near-infrared spectroscopy (fNIRS), we examined the effects of interaction quality on neural synchrony during a problem-solving task in 42 dyads of mothers and their preschool children. In a cooperation condition, mothers and children were instructed to solve a tangram puzzle together. In an individual condition, mothers and children performed the same task alone with an opaque screen between them. Wavelet transform coherence (WTC) was used to assess the cross-correlation between the two fNIRS time series. Results revealed increased neural synchrony in bilateral prefrontal cortex and temporo-parietal areas during cooperative as compared to individual problem solving. Higher neural synchrony during cooperation correlated with higher behavioral reciprocity and neural synchrony predicted the dyad's problem-solving success beyond reciprocal behavior between mothers and children. State-like factors, such as maternal stress and child agency during the task, played a bigger role for neural synchronization than trait-like factors, such as child temperament. Our results emphasize neural synchrony as a biomarker for mother-child interaction quality. These findings further highlight the role of state-like factors in interpersonal synchronization processes linked to successful coordination with others and in the long-term might improve the understanding of others.

Young children integrate current observations, priors and agent information to predict others' actions.

From early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agent's preferences from systematic violations of random sampling. We investigated whether and how young children infer an agent's sampling biases. Moreover, we examined whether pupil data of toddlers follow the predictions of a computational model based on the causal Bayesian network formalization of predictive processing. We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to predict others' actions. We measured pupillary responses as a behavioral marker of 'prediction errors' (i.e., the perceived mismatch between what one's model of an agent predicts and what the agent actually does). Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to make predictions about agents and their actions. These findings shed light on the mechanisms behind toddlers' inferences about agent-caused events. To our knowledge, this is the first study in which young children's pupillary responses are used as markers of prediction errors, which were qualitatively compared to the predictions by a computational model based on the causal Bayesian network formalization of predictive processing.

Infants Distinguish Between Two Events Based on Their Relative Likelihood.

Likelihood estimations are crucial for dealing with the uncertainty of life. Here, infants' sensitivity to the difference in likelihood between two events was investigated. Infants aged 6, 12, and 18 months (N = 75) were shown animated movies of a machine simultaneously drawing likely and unlikely samples from a box filled with different colored balls. In different trials, the difference in likelihood between the two samples was manipulated. The infants' looking patterns varied as a function of the magnitude of the difference in likelihood and were modulated by the number of items in the samples. Looking patterns showed qualitative similarities across age groups. This study demonstrates that infants' looking responses are sensitive to the magnitude of the difference in likelihood between two events.