The infant motor system predicts actions based on visual statistical learning

The Emergence of Organized Emotion Dynamics in Childhood

Emotions change from one moment to the next. They have a duration from seconds to hours and then transition to other emotions. Here, we describe the early ontology of these key aspects of emotion dynamics. In five cross-sectional studies (N = 904) combining parent surveys and ecological momentary assessment, we characterize how caregivers' perceptions of children's emotion duration and transitions change over the first 5 years of life and how they relate to children's language development. Across these ages, the duration of children's emotions increased, and emotion transitions became increasingly organized by valence, such that children were more likely to transition between similarly valenced emotions. Children with more mature emotion profiles also had larger vocabularies and could produce more emotion labels. These findings advance our understanding of emotion and communication by highlighting their intertwined nature in development and by charting how dynamic features of emotion experiences change over the first years of life.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-024-00248-y.

Visual statistical learning in preverbal infants at a higher likelihood of autism and its association with later social communication skills

Visual statistical Learning (SL) allows infants to extract the statistical relationships embedded in a sequence of elements. SL plays a crucial role in language and communication competencies and has been found to be impacted in Autism Spectrum Disorder (ASD). This study aims to investigate visual SL in infants at higher likelihood of developing ASD (HL-ASD) and its predictive value on autistic-related traits at 24-36 months. At 6 months of age, SL was tested using a visual habituation task in HL-ASD and neurotypical (NT) infants. All infants were habituated to a visual sequence of shapes containing statistically predictable patterns. In the test phase, infants viewed the statistically structured, familiar sequence in alternation with a novel sequence that did not contain any statistical information. HL-ASD infants were then evaluated at 24-36 months to investigate the associations between visual SL and ASD-related traits. Our results showed that NT infants were able to learn the statistical structure embedded in the visual sequences, while HL-ASD infants showed different learning patterns. A regression analysis revealed that SL ability in 6-month-old HL-ASD infants was related to social communication and interaction abilities at 24-36 months of age. These findings indicate that early differences in learning visual statistical patterns might contribute to later social communication skills.

Does animacy affect visual statistical learning? Revisiting the effects of selective attention and animacy on visual statistical learning

Animates receive preferential attentional processing over inanimates because, from an evolutionary perspective, animates are important to human survival. We investigated whether animacy affects visual statistical learning-the detection and extraction of regularities in visual information from our rich, dynamic, and complex environment. Participants completed a selective-attention task, in which regularities were embedded in two visual streams, an attended and an unattended visual stream. The attended visual stream always consisted of line-drawings of non-objects, while the unattended visual stream consisted of line-drawings of either animates or inanimates. Participants then completed a triplet-discrimination task, which assessed their ability to extract regularities from the attended and unattended visual streams. We also assessed participants' awareness of regularities in the visual statistical learning task, and asked if any learning strategies were used. We were specifically interested in whether the animacy status of line-drawings in the unattended visual stream would affect visual statistical learning. There were four key findings. First, selective attention modulates visual statistical learning, with greater visual statistical learning for attended than for unattended information. Second, animacy does not affect visual statistical learning, with no differences found in visual statistical learning performance between the animate and inanimate condition. Third, awareness of regularities was associated with visual statistical learning of attended information. Fourth, participants used strategies (e.g., naming or labelling stimuli) during the visual statistical learning task. Further research is required to understand whether visual statistical learning is one of the adaptive functions that evolved from ancestral environments.

Reliability of individual differences in distractor suppression driven by statistical learning

A series of recent studies has demonstrated that attentional selection is modulated by statistical regularities, even when they concern task-irrelevant stimuli. Irrelevant distractors presented more frequently at one location interfere less with search than distractors presented elsewhere. To account for this finding, it has been proposed that through statistical learning, the frequent distractor location becomes suppressed relative to the other locations. Learned distractor suppression has mainly been studied at the group level, where individual differences are treated as unexplained error variance. Yet these individual differences may provide important mechanistic insights and could be predictive of cognitive and real-life outcomes. In the current study, we ask whether in an additional singleton task, the standard measures of attentional capture and learned suppression are reliable and stable at the level of the individual. In an online study, we assessed both the within- and between-session reliability of individual-level measures of attentional capture and learned suppression. We show that the measures of attentional capture, but not of distractor suppression, are moderately stable within the same session (i.e., split-half reliability). Test-retest reliability over a 2-month period was found to be moderate for attentional capture but weak or absent for suppression. RT-based measures proved to be superior to accuracy measures. While producing very robust findings at the group level, the predictive validity of these RT-based measures is still limited when it comes to individual-level performance. We discuss the implications for future research drawing on inter-individual variation in the attentional biases that result from statistical learning.

Four fundamental dimensions underlie the perception of human actions

We evaluate the actions of other individuals based upon a variety of movements that reveal critical information to guide decision making and behavioural responses. These signals convey a range of information about the actor, including their goals, intentions and internal mental states. Although progress has been made to identify cortical regions involved in action processing, the organising principles underlying our representation of actions still remains unclear. In this paper we investigated the conceptual space that underlies action perception by assessing which qualities are fundamental to the perception of human actions. We recorded 240 different actions using motion-capture and used these data to animate a volumetric avatar that performed the different actions. 230 participants then viewed these actions and rated the extent to which each action demonstrated 23 different action characteristics (e.g., avoiding-approaching, pulling-pushing, weak-powerful). We analysed these data using Exploratory Factor Analysis to examine the latent factors underlying visual action perception. The best fitting model was a four-dimensional model with oblique rotation. We named the factors: friendly-unfriendly, formidable-feeble, planned-unplanned, and abduction-adduction. The first two factors of friendliness and formidableness explained approximately 22% of the variance each, compared to planned and abduction, which explained approximately 7-8% of the variance each; as such we interpret this representation of action space as having 2 + 2 dimensions. A closer examination of the first two factors suggests a similarity to the principal factors underlying our evaluation of facial traits and emotions, whilst the last two factors of planning and abduction appear unique to actions.

Weight status impacts children's incidental statistical learning

The expanding literature investigating the cognitive effects of childhood weight status has not included examinations of incidental statistical learning, the process by which children unintentionally acquire knowledge about patterns in their environments, despite evidence that it underlies many higher-level information processing capabilities. In the present study, we measured event-related potentials (ERPs) while school-aged participants completed a variation of an oddball task in which stimuli predicted the appearance of a target. Children were asked to respond to the target but were not given any information about the existence of predictive dependencies. We found that children with a healthy weight status had larger P3 amplitudes in response to the predictors that were most meaningful in completing the task, a finding that may suggest optimized learning mechanisms influenced by weight status. These findings offer an important first step to understanding how healthy lifestyle factors may influence incidental statistical learning.

Proactive or reactive? Neural oscillatory insight into the leader-follower dynamics of early infant-caregiver interaction

We know that infants' ability to coordinate attention with others toward the end of the first year is fundamental to language acquisition and social cognition. Yet, we understand little about the neural and cognitive mechanisms driving infant attention in shared interaction: do infants play a proactive role in creating episodes of joint attention? Recording electroencephalography (EEG) from 12-mo-old infants while they engaged in table-top play with their caregiver, we examined the communicative behaviors and neural activity preceding and following infant- vs. adult-led joint attention. Infant-led episodes of joint attention appeared largely reactive: they were not associated with increased theta power, a neural marker of endogenously driven attention, and infants did not increase their ostensive signals before the initiation. Infants were, however, sensitive to whether their initiations were responded to. When caregivers joined their attentional focus, infants showed increased alpha suppression, a pattern of neural activity associated with predictive processing. Our results suggest that at 10 to 12 mo, infants are not routinely proactive in creating joint attention episodes yet. They do, however, anticipate behavioral contingency, a potentially foundational mechanism for the emergence of intentional communication.

Down and up! Does the mu rhythm index a gating mechanism in the developing motor system?

Developmental research on action processing in the motor cortex relies on a key neural marker - a decrease in 6-12 Hz activity (coined mu suppression). However, recent evidence points towards an increase in mu power, specific for the observation of others' actions. Complementing the findings on mu suppression, this raises the critical question for the functional role of the mu rhythm in the developing motor system. We here discuss a potential solution to this seeming controversy by suggesting a gating function of the mu rhythm: A decrease in mu power may index the facilitation, while an increase may index the inhibition of motor processes, which are critical during action observation. This account may advance our conception of action understanding in early brain development and points towards critical directions for future research.

Predictive motor activation: Modulated by expectancy or predictability?

Predicting actions is a fundamental ability that helps us to comprehend what is happening in our environment and to interact with others. The motor system was previously identified as source of action predictions. Yet, which aspect of the statistical likelihood of upcoming actions the motor system is sensitive to remains an open question. This EEG study investigated how regularities in observed actions are reflected in the motor system and utilized to predict upcoming actions. Prior to measuring EEG, participants watched videos of action sequences with different transitional probabilities. After training, participants' brain activity over motor areas was measured using EEG while watching videos of action sequences with the same statistical structure. Focusing on the mu and beta frequency bands we tested whether activity of the motor system reflects the statistical likelihood of upcoming actions. We also explored two distinct aspects of the statistical structure that capture different prediction processes, expectancy and predictability. Expectancy describes participants' expectation of the most likely action, whereas predictability represents all possible actions and their relative probabilities. Results revealed that mu and beta oscillations play different roles during action prediction. While the mu rhythm reflected anticipatory activity without any link to the statistical structure, the beta rhythm was related to the expectancy of an action. Our findings support theories proposing that the motor system underlies action prediction, and they extend such theories by showing that multiple forms of statistical information are extracted when observing action sequences. This information is integrated in the prediction generated by the neural motor system of which action is going to happen next.

Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI

Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain-computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation.

The Effects of Cooperative and Competitive Situations on Statistical Learning

Devising cooperative or competitive situations is an important teaching strategy in educational practices. Nevertheless, there is still controversy regarding which situation is better for learning. This study was conducted to explore the effects of cooperative and competitive situations on statistical learning, through the alternating serial reaction time (ASRT) task. Individual cooperative and competitive situations were devised in this study, in which individual situation served as the control condition. Ninety recruited participants were randomly assigned to a cooperative, competitive, or individual group to perform the ASRT task. For general learning, cooperative and competitive situations could indeed make learners respond faster, and there was no significant difference in the RT between the cooperative and competitive groups. Moreover, statistical learning was observed in all three groups. An additional analysis of the early stage of the experiment showed that the learning effect of the competitive group was greater than those of the cooperative and individual groups, in terms of statistical learning. However, the final learning effect was not significantly different among the three groups. Overall, the cooperative and competitive situations had a positive impact on learning and enabled the students to acquire approximately the same learning effect in a shorter time period, compared with the individual situation. Specifically, the competitive situation accelerated the statistical learning process but not the general learning process.

Studying the Developing Brain in Real-World Contexts: Moving From Castles in the Air to Castles on the Ground

Most current research in cognitive neuroscience uses standardized non-ecological experiments to study the developing brain. But these approaches do a poor job of mimicking the real-world, and thus can only provide a distorted picture of how cognitive operations and brain development unfold outside of the lab. Here we consider future research avenues which may lead to a better appreciation of how developing brains dynamically interact with a complex real-world environment, and how cognition develops over time. We raise several problems faced by current mainstream methods in the field, before briefly reviewing novel promising approaches that alleviate some of these issues. First, we consider research that examines perception by measuring entrainment between brain activity and temporal patterns in naturalistic stimuli. Second, we consider research that examines our ability to parse our continuous experience into discrete events, and how this ability develops over time. Third, we consider the role of children as active agents in selecting what they sample from the environment from one moment to the next. Fourth, we consider new approaches that measure how mutual influences between children and others are instantiated in suprapersonal brain networks. Finally, we discuss how we may reduce adult biases when designing developmental studies. Together, these approaches have great potential to further our understanding of how the developing brain learns to process information, and to control complex real-world behaviors.

Visual statistical learning in deaf and hearing infants and toddlers

Congenital hearing loss offers a unique opportunity to examine the role of sound in cognitive, social, and linguistic development. Children with hearing loss demonstrate atypical performance across a range of general cognitive skills. For instance, research has shown that deaf school-age children underperform on visual statistical learning (VSL) tasks. However, the evidence for these deficits has been challenged, with mixed findings emerging in recent years. Here, we used a novel approach to examine VSL in the action domain early in development. We compared learning between deaf and hearing infants, prior to cochlear implantation (pre-CI), and a group of toddlers post implantation (post-CI). Findings revealed a significant difference between deaf and hearing infants pre-CI, with evidence for learning only in the hearing infants. However, there were no significant group differences between deaf and hearing toddlers post-CI, with both groups demonstrating learning. Further, VSL performance was positively correlated with language scores for the deaf toddlers, adding to the body of evidence suggesting that statistical learning is associated with language abilities. We discuss these findings in the context of previous evidence for group differences in VSL skills, and the role that auditory experiences play in infant cognitive development.

Dialogic Book-Sharing as a Privileged Intersubjective Space

Parental reading to young children is well-established as being positively associated with child cognitive development, particularly their language development. Research indicates that a particular, "intersubjective," form of using books with children, "Dialogic Book-sharing" (DBS), is especially beneficial to infants and pre-school aged children, particularly when using picture books. The work on DBS to date has paid little attention to the theoretical and empirical underpinnings of the approach. Here, we address the question of what processes taking place during DBS confer benefits to child development, and why these processes are beneficial. In a novel integration of evidence, ranging from non-human primate communication through iconic gestures and pointing, archaeological data on Pre-hominid and early human art, to experimental and naturalistic studies of infant attention, cognitive processing, and language, we argue that DBS entails core characteristics that make it a privileged intersubjective space for the promotion of child cognitive and language development. This analysis, together with the findings of DBS intervention studies, provides a powerful intellectual basis for the wide-scale promotion of DBS, especially in disadvantaged populations.

Sequential learning of emotional faces is statistical at 12 months of age

Infants are capable of extracting statistical regularities from continuous streams of elements, which helps them structuring their surrounding environment. The current study examines 12-month-olds' capacity to extract statistical information from a sequence of emotional faces. Using a familiarization procedure, infants were presented with videos of two actresses expressing the same facial emotion, and subsequently turning toward or away from each other. Videos displayed different emotions (i.e., anger, happiness, fear, sadness, surprise, amusement, disgust, and exasperation) and were organized sequentially, so that the transitional probabilities between videos were highly predictable in some cases, and less predictable in others. At test, infants discriminated highly predictable from low predictable transitional probabilities, suggesting that they extracted statistical regularities from the sequence of emotional faces. However, when examining the looking toward and the looking away conditions separately, infants showed evidence of statistical learning in the looking toward condition only. Together, these findings suggest that 12-month-old infants rely on statistical learning to segment a continuous sequence of emotional faces, although this ability can be modulated by the nature of the stimuli. The contribution of statistical learning to structure infants' social environment is discussed.

Social context shapes neural processing of others' actions in 9-month-old infants

From infancy, neural processes for perceiving others' actions and producing one's own actions overlap (neural mirroring). Adults and children show enhanced mirroring in social interactions. Yet, whether social context affects mirroring in infancy, a time when processing others' actions is crucial for action learning, remains unclear. We examined whether turn-taking, an early form of social interaction, enhanced 9-month-olds' neural mirroring. We recorded electroencephalography while 9-month-olds were grasping (execution) and observing live grasps (observation). In this design, half of the infants observed and acted in alternation (turn-taking condition), whereas the other half observed several times in a row before acting (blocked condition). Replicating previous findings, infants showed significant 6- to 9-Hz mu suppression (indicating motor activation) during execution and observation (n = 24). In addition, a condition (turn-taking or blocked) by time (action start or end) interaction indicated that infants engaged in turn-taking (n = 9), but not in the blocked context (n = 15), showed more mirroring when observing the action start compared with the action end. Exploratory analyses further suggest that (a) there is higher visual-motor functional connectivity in turn-taking toward the action's end, (b) mirroring relates to later visual-motor connectivity, and (c) visual attention as indexed by occipital alpha is enhanced in turn-taking compared with the blocked context. Together, this suggests that the neural processing of others' actions is modulated by the social context in infancy and that turn-taking may be particularly effective in engaging infants' action perception system.

Action predictability is reflected in beta power attenuation and predictive eye movements in adolescents with and without autism

Most theoretical accounts of autism posit difficulties in predicting others' actions, and this difficulty has been proposed to be at the root of autistic individuals' social communication differences. Empirical results are mixed, however, with autistic individuals showing reduced action prediction in some studies but not in others. It has recently been proposed that this effect might be observed primarily when observed actions are less predictable, but this idea has yet to be tested. To assess the influence of predictability on neural and behavioural action prediction, the current study employed an action observation paradigm with multi-step actions that become gradually more predictable. Autistic and non-autistic adolescents showed similar patterns of motor system activation during observation, as seen in attenuated mu and beta power compared to baseline, with beta power further modulated by predictability in both groups. Bayesian statistics confirmed that action predictability influenced beta power similarly in both groups. The groups also made similar behavioural predictions, as seen in three eye-movement measures. We found no evidence that autistic adolescents responded differently than non-autistic adolescents to the predictability of an observed action. These findings show that autistic adolescents do spontaneously predict others' actions, both neurally and behaviourally, which calls into question the role of action prediction as a key mechanism underlying autism.

Learning in Infancy Is Active, Endogenously Motivated, and Depends on the Prefrontal Cortices

A common view of learning in infancy emphasizes the role of incidental sensory experiences from which increasingly abstract statistical regularities are extracted. In this view, infant brains initially support basic sensory and motor functions, followed by maturation of higher-level association cortex. Here, we critique this view and posit that, by contrast and more like adults, infants are active, endogenously motivated learners who structure their own learning through flexible selection of attentional targets and active interventions on their environment. We further argue that the infant brain, and particularly the prefrontal cortex (PFC), is well equipped to support these learning behaviors. We review recent progress in characterizing the function of the infant PFC, which suggests that, as in adults, the PFC is functionally specialized and highly connected. Together, we present an integrative account of infant minds and brains, in which the infant PFC represents multiple intrinsic motivations, which are leveraged for active learning.

Social context influences infants' ability to extract statistical information from a sequence of gestures

Infants' social environment is rich of complex sequences of events and actions. This study investigates whether 12-month-old infants are able to learn statistical regularities from a sequence of human gestures and whether this ability is affected by a social vs non-social context. Using a visual familiarization task, infants were familiarized to a continuous sequence of eight videos in which two women imitated each other performing arm gestures. The sequence of videos in which the two women performed imitative gestures was organized into 4 different gesture units. Videos within a gesture unit had a highly predictable transitional probability, while such transition was less predictable between gesture units. The social context was manipulated varying the mutual gaze of the actors and their body orientation. At test, infants were able to discriminate between the high- and low-predictable gesture units in the social, but not in the non-social condition. Results demonstrate that infants are capable to detect statistical regularities from a sequence of human gestures performed by two different individuals. Moreover, our findings indicate that salient social cues can modulate infants' ability to extract statistical information from a sequence of gestures.

Action prediction during real-time parent-infant interactions

Social interactions provide a crucial context for early learning and cognitive development during infancy. Action prediction-the ability to anticipate an observed action-facilitates successful, coordinated interaction and is an important social-cognitive skill in early development. However, current knowledge about infant action prediction comes largely from screen-based laboratory tasks. We know little about what infants' action prediction skills look like during real-time, free-flowing interactions with a social partner. In the current study, we used head-mounted eyetracking to quantify 9-month-old infants' visual anticipations of their parents' actions during free-flowing parent-child play. Our findings reveal that infants do anticipate their parents' actions during dynamic interactions at rates significantly higher than would be expected by chance. In addition, the frequency with which they do so is associated with child-led joint attention and hand-eye coordination. These findings are the first to reveal infants' action prediction behaviors in a more naturalistic context than prior screen-based studies, and they support the idea that action prediction is inherently linked to motor development and plays an important role in infants' social-cognitive development. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=9HrmcicfiqE.

Electrophysiological signatures of visual statistical learning in 3-month-old infants at familial and low risk for autism spectrum disorder

Visual statistical learning (VSL) refers to the ability to extract associations and conditional probabilities within the visual environment. It may serve as a precursor to cognitive and social communication development. Quantifying VSL in infants at familial risk (FR) for Autism Spectrum Disorder (ASD) provides opportunities to understand how genetic predisposition can influence early learning processes which may, in turn, lay a foundation for cognitive and social communication delays. We examined electroencephalography (EEG) signatures of VSL in 3-month-old infants, examining whether EEG correlates of VSL differentiated FR from low-risk (LR) infants. In an exploratory analysis, we then examined whether EEG correlates of VSL at 3 months relate to cognitive function and ASD symptoms at 18 months. Infants were exposed to a continuous stream of looming shape pairs with varying probability that the shapes would occur in sequence (high probability-deterministic condition; low probability-probabilistic condition). EEG was time-locked to shapes based on their transitional probabilities. EEG analysis examined group-level characteristics underlying specific components, including the late frontal positivity (LFP) and N700 responses. FR infants demonstrated increased LFP and N700 response to the probabilistic condition, whereas LR infants demonstrated increased LFP and N700 response to the deterministic condition. LFP at 3 months predicted 18-month visual reception skills and not ASD symptoms. Our findings thus provide evidence for distinct VSL processes in FR and LR infants as early as 3 months. Atypical pattern learning in FR infants may lay a foundation for later delays in higher level, nonverbal cognitive skills, and predict ASD symptoms well before an ASD diagnosis is made.

Getting a grip on early intention understanding: The role of motor, cognitive, and social factors

This chapter considers various factors that facilitate infants' understanding of other people's intentions. As adults, we view the actions people perform around us as intentional, to achieve a goal, rather than idle movements. For example, when observing another person perform a simple grasping action, such as picking up a slice of pizza, we perceive this action as goal-directed. Due to our understanding of the person's intention, we focus more so on the relation between the person and their goal, rather than the motion involved in the action. Infants develop an understanding of intentional agents and their goals within the first year of life. This chapter reviews multiple factors that are at play in facilitating infants' learning about the intentions of others' actions. We consider this from various perspectives, including the role of active experience, sensitivity to behavioral cues, cognitive factors, and social factors. We first review evidence concerning infants' learning of intentional actions from active experience. We then go on to evaluate how this learning could also come about via comparison processes, statistical learning, and use of behavioral cues such as object labeling and action effects. We also review social factors such as infant-directed actions and triadic engagement within social interactions that emerging evidence suggests are helpful in facilitating infants' understanding of other people's actions. Finally, we consider the extent to which these factors interact with one another in different contexts, as well as implications and future directions.

The prevalence and importance of statistical learning in human cognition and behavior

Statistical learning, the ability to extract regularities from the environment over time, has become a topic of burgeoning interest. Its influence on behavior, spanning infancy to adulthood, has been demonstrated across a range of tasks, both those labeled as tests of statistical learning and those from other learning domains that predated statistical learning research or that are not typically considered in the context of that literature. Given this pervasive role in human cognition, statistical learning has the potential to reconcile seemingly distinct learning phenomena and may be an under-appreciated but important contributor to a wide range of human behaviors that are studied as unrelated processes, such as episodic memory and spatial navigation.

A decade of infant neuroimaging research: What have we learned and where are we going?

The past decade has seen the emergence of neuroimaging studies of infant populations. Incorporating imaging has resulted in invaluable insights about neurodevelopment at the start of life. However, little has been enquired of the experimental specifications and study characteristics of typical findings. This review systematically screened empirical studies that used electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) on infants (max. age of 24 months). From more than 21,000 publications, a total of 710 records were included for analyses. With the exception of EEG studies, infant studies with MEG, fNIRS, and fMRI were most often conducted around birth and at 12 months. The vast majority of infant studies came from North America, with very few studies conducted in Africa, certain parts of South America, and Southeast Asia. Finally, longitudinal neuroimaging studies were inclined to adopt EEG, followed by fMRI, fNIRS, and MEG. These results show that there is compelling need for studies with larger sample sizes, studies investigating a broader range of infant developmental periods, and studies from under- and less-developed regions in the world. Addressing these shortcomings in the future will provide a more representative and accurate understanding of neurodevelopment in infancy.

Music predictability and liking enhance pupil dilation and promote motor learning in non-musicians

Humans can anticipate music and derive pleasure from it. Expectations facilitate the learning of movements associated with anticipated events, and they are also linked with reward, which may further facilitate learning of the anticipated rewarding events. The present study investigates the synergistic effects of predictability and hedonic responses to music on arousal and motor-learning in a naïve population. Novel melodies were manipulated in their overall predictability (predictable/unpredictable) as objectively defined by a model of music expectation, and ranked as high/medium/low liked based on participants' self-reports collected during an initial listening session. During this session, we also recorded ocular pupil size as an implicit measure of listeners' arousal. During the following motor task, participants learned to play target notes of the melodies on a keyboard (notes were of similar motor and musical complexity across melodies). Pupil dilation was greater for liked melodies, particularly when predictable. Motor performance was facilitated in predictable rather than unpredictable melodies, but liked melodies were learned even in the unpredictable condition. Low-liked melodies also showed learning but mostly in participants with higher scores of task perceived competence. Taken together, these results highlight  the effects of stimuli predictability on learning, which can be however overshadowed by the effects of stimulus liking or task-related intrinsic motivation.

Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning

Statistical learning is a learning mechanism based on transition probability in sequences such as music and language. Recent computational and neurophysiological studies suggest that the statistical learning contributes to production, action, and musical creativity as well as prediction and perception. The present study investigated how statistical structure interacts with tonalities in music based on various-order statistical models. To verify this in all 24 major and minor keys, the transition probabilities of the sequences containing the highest pitches in Bach's Well-Tempered Clavier, which is a collection of two series (No. 1 and No. 2) of preludes and fugues in all of the 24 major and minor keys, were calculated based on nth-order Markov models. The transition probabilities of each sequence were compared among tonalities (major and minor), two series (No. 1 and No. 2), and music types (prelude and fugue). The differences in statistical characteristics between major and minor keys were detected in lower- but not higher-order models. The results also showed that statistical knowledge in music might be modulated by tonalities and composition periods. Furthermore, the principal component analysis detected the shared components of related keys, suggesting that the tonalities modulate statistical characteristics in music. The present study may suggest that there are at least two types of statistical knowledge in music that are interdependent on and independent of tonality, respectively.

Depth and the Uncertainty of Statistical Knowledge on Musical Creativity Fluctuate Over a Composer's Lifetime

Brain models music as a hierarchy of dynamical systems that encode probability distributions and complexity (i.e., entropy and uncertainty). Through musical experience over lifetime, a human is intrinsically motivated in optimizing the internalized probabilistic model for efficient information processing and the uncertainty resolution, which has been regarded as rewords. Human's behavior, however, appears to be not necessarily directing to efficiency but sometimes act inefficiently in order to explore a maximum rewards of uncertainty resolution. Previous studies suggest that the drive for novelty seeking behavior (high uncertain phenomenon) reflects human's curiosity, and that the curiosity rewards encourage humans to create and learn new regularities. That is to say, although brain generally minimizes uncertainty of music structure, we sometimes derive pleasure from music with uncertain structure due to curiosity for novelty seeking behavior by which we anticipate the resolution of uncertainty. Few studies, however, investigated how curiosity for uncertain and novelty seeking behavior modulates musical creativity. The present study investigated how the probabilistic model and the uncertainty in music fluctuate over a composer's lifetime (all of the 32 piano sonatas by Ludwig van Beethoven). In the late periods of the composer's lifetime, the transitional probabilities (TPs) of sequential patterns that ubiquitously appear in all of his music (familiar phrase) were decreased, whereas the uncertainties of the whole structure were increased. Furthermore, these findings were prominent in higher-, rather than lower-, order models of TP distribution. This may suggest that the higher-order probabilistic model is susceptible to experience and psychological phenomena over the composer's lifetime. The present study first suggested the fluctuation of uncertainty of musical structure over a composer's lifetime. It is suggested that human's curiosity for uncertain and novelty seeking behavior may modulate optimization and creativity in human's brain.

Mu rhythm desynchronization is specific to action execution and observation: Evidence from time-frequency and connectivity analysis

Mu desynchronization is the attenuation of EEG power in the alpha frequency range recorded over central scalp locations thought to reflect motor cortex activation. Mu desynchronization during observation of an action is believed to reflect mirroring system activation in humans. However, this notion has recently been questioned because, among other reasons, the potential contamination of mu rhythm and occipital alpha activity induced by attention processes following presentation of visual stimuli in observation conditions. This study examined the validity of mu desynchronization as a measure of mirroring system activation in infants and further investigated the pattern of functional connectivity between the central and occipital regions during execution and observation of movement. EEG was recorded while 46 9-month-old infants executed grasping actions and observed an experimenter grasping. Current source density (CSD) was applied to EEG data and, time-frequency and connectivity analyses were performed in CSD transformed data. Mu desynchronization was evident over central regions during both execution and observation of movements. Independent alpha desynchronization over occipital region was also present in both conditions. The connectivity analyses revealed that central-occipital areas were functionally more connected compared to other areas of the brain during observation of movements. Collectively, the results demonstrate the validity of mu desynchronization as an index of infant mirroring system activity and support the proposal of a functional connection between distinct mirroring and attention processes during observation of action.