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.

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.

Visual statistical learning in infancy: Discrimination of fine-grained regularities depends on early test trials

Infants' ability to detect statistical regularities between visual objects has been demonstrated in previous studies (e.g., Kirkham et al., Cognition, 83, 2002, B35). The extent to which infants extract and learn the actual values of the transitional probabilities (TPs) between these objects nevertheless remains an open question. In three experiments providing identical learning conditions but contrasting different types of sequences at test, we examined 8-month-old infants' ability to discriminate between familiar sequences involving high or low values of TPs, and new sequences that involved null TPs. Results showed that infants discriminate between these three types of sequences, supporting the existence of a statistical learning mechanism by which infants extract fine-grained statistical information from a stream of visual stimuli. Interestingly, the expression of this statistical knowledge varied between experiments and specifically depended on the nature of the first two test trials. We argue that the predictability of this early test arrangement-namely whether the first two test items were either predictable or unexpected based on the habituation phase-determined infants' looking behaviors.

Events structure information accessibility less in children than adults

To manage the onslaught of continuously unfolding information in our complex environments, we adults are known to carve up our continuous experience into meaningful events, a process referred to as event segmentation. This segmentation directly shapes how our everyday experiences are construed: content experienced within an event is held mentally in an accessible state, which is then dropped after an event boundary. The greater accessibility of event-specific information has been shown to influence-at its most basic level-how information is processed and remembered. However, it is as yet unknown if accessibility is similarly influenced by event boundaries in children, who are still developing the working memory capacity and semantic knowledge thought to support event segmentation. Here, we tested seven- to nine-year-old children's and adults' recognition of objects experienced either within or across event boundaries of two cartoons. We found that children and adults were both more accurate and faster to correctly recognize objects that last occurred within events versus across event boundaries. We, however, additionally observed an interaction such that children's access to recent experience was less influenced by event boundaries than adults'. Thus, while the spontaneous segmentation of complex events emerges by middle childhood, event structure shapes the active contents of children's minds less reliably than adults'.

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.

Translating visual information into action predictions: Statistical learning in action and nonaction contexts

Humans are sensitive to the statistical regularities in action sequences carried out by others. In the present eyetracking study, we investigated whether this sensitivity can support the prediction of upcoming actions when observing unfamiliar action sequences. In two between-subjects conditions, we examined whether observers would be more sensitive to statistical regularities in sequences performed by a human agent versus self-propelled 'ghost' events. Secondly, we investigated whether regularities are learned better when they are associated with contingent effects. Both implicit and explicit measures of learning were compared between agent and ghost conditions. Implicit learning was measured via predictive eye movements to upcoming actions or events, and explicit learning was measured via both uninstructed reproduction of the action sequences and verbal reports of the regularities. The findings revealed that participants, regardless of condition, readily learned the regularities and made correct predictive eye movements to upcoming events during online observation. However, different patterns of explicit-learning outcomes emerged following observation: Participants were most likely to re-create the sequence regularities and to verbally report them when they had observed an actor create a contingent effect. These results suggest that the shift from implicit predictions to explicit knowledge of what has been learned is facilitated when observers perceive another agent's actions and when these actions cause effects. These findings are discussed with respect to the potential role of the motor system in modulating how statistical regularities are learned and used to modify behavior.

The case of CAUSE: neurobiological mechanisms for grounding an abstract concept

How can we understand causal relationships and how can we understand words such as 'cause'? Some theorists assume that the underlying abstract concept is given to us, and that perceptual correlation provides the relevant hints towards inferring causation from perceived real-life events. A different approach emphasizes the role of actions and their typical consequences for the emergence of the concept of causation and the application of the related term. A model of causation is proposed that highlights the family resemblance between causal actions and postulates that symbols are necessary for binding together the different partially shared semantic features of subsets of causal actions and their goals. Linguistic symbols are proposed to play a key role in binding the different subsets of semantic features of the abstract concept. The model is spelt out at the neuromechanistic level of distributed cortical circuits and the cognitive functions they carry. The model is discussed in light of behavioural and neuroscience evidence, and questions for future research are highlighted. In sum, taking causation as a concrete example, I argue that abstract concepts and words can be learnt and grounded in real-life interaction, and that the neurobiological mechanisms realizing such abstract semantic grounding are within our grasp.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'.

Prelinguistic foundations of verb learning: Infants discriminate and categorize dynamic human actions

Action categorization is necessary for human cognition and is foundational to learning verbs, which label categories of actions and events. In two studies using a nonlinguistic preferential looking paradigm, 10- to 12-month-old English-learning infants were tested on their ability to discriminate and categorize a dynamic human manner of motion (i.e., way in which a figure moves; e.g., marching). Study 1 results reveal that infants can discriminate a change in path and actor across instances of the same manner of motion. Study 2 results suggest that infants categorize the manner of motion for dynamic human events even under conditions in which other components of the event change, including the actor's path and the actor. Together, these two studies extend prior research on infant action categorization of animated motion events by providing evidence that infants can categorize dynamic human actions, a skill foundational to the learning of motion verbs.

Language Development in the First Year of Life: What Deaf Children Might Be Missing Before Cochlear Implantation

OBJECTIVES

Language development is a multifaceted, dynamic process involving the discovery of complex patterns, and the refinement of native language competencies in the context of communicative interactions. This process is already advanced by the end of the first year of life for hearing children, but prelingually deaf children who initially lack a language model may miss critical experiences during this early window. The purpose of this review is twofold. First, we examine the published literature on language development during the first 12 months in typically developing children. Second, we use this literature to inform our understanding of the language outcomes of prelingually deaf children who receive cochlear implants (CIs), and therefore language input, either before or after the first year.

CONCLUSIONS

During the first 12 months, typically developing infants exhibit advances in speech segmentation, word learning, syntax acquisition, and communication, both verbal and nonverbal. Infants and their caregivers coconstruct a communication foundation during this time, supporting continued language growth. The language outcomes of hearing children are robustly predicted by their experiences and acquired competencies during the first year; yet these predictive links are absent among prelingually deaf infants lacking a language model (i.e., those without exposure to sign). For deaf infants who receive a CI, implantation timing is crucial. Children receiving CIs before 12 months frequently catch up with their typically developing peers, whereas those receiving CIs later do not. Explanations for the language difficulties of late-implanted children are discussed.

Statistical learning of movement

The environment is dynamic, but objects move in predictable and characteristic ways, whether they are a dancer in motion, or a bee buzzing around in flight. Sequences of movement are comprised of simpler motion trajectory elements chained together. But how do we know where one trajectory element ends and another begins, much like we parse words from continuous streams of speech? As a novel test of statistical learning, we explored the ability to parse continuous movement sequences into simpler element trajectories. Across four experiments, we showed that people can robustly parse such sequences from a continuous stream of trajectories under increasingly stringent tests of segmentation ability and statistical learning. Observers viewed a single dot as it moved along simple sequences of paths, and were later able to discriminate these sequences from novel and partial ones shown at test. Observers demonstrated this ability when there were potentially helpful trajectory-segmentation cues such as a common origin for all movements (Experiment 1); when the dot's motions were entirely continuous and unconstrained (Experiment 2); when sequences were tested against partial sequences as a more stringent test of statistical learning (Experiment 3); and finally, even when the element trajectories were in fact pairs of trajectories, so that abrupt directional changes in the dot's motion could no longer signal inter-trajectory boundaries (Experiment 4). These results suggest that observers can automatically extract regularities in movement - an ability that may underpin our capacity to learn more complex biological motions, as in sport or dance.

Action Interrupted: Processing of Movement and Breakpoints in Toddlers and Adults

From early in development, segmenting events unfolding in the world in meaningful ways renders input more manageable and facilitates interpretation and prediction. Yet, little is known about how children process action structure in events comprised of multiple coarse-grained actions. More importantly, little is known about the time-course of action processing in young children or about the specific features that recruit attention. This is particularly true when we consider action that pauses unexpectedly-as actions sometimes do-violating the expectation of a continuous unfolding of motion. We assessed visual preference to intact and disrupted actions embedded within a multi-action event in toddlers and adults. In one condition, pauses were inserted at intact action boundaries whereas in the other condition they disrupted action. Attention in both groups was recruited to the disrupted relative to intact events. Time-course analyses, however, revealed developmental differences in sensitivity to the movement features (e.g., motion, pauses, and transitions) of disrupted events.

Listening through voices: Infant statistical word segmentation across multiple speakers

To learn from their environments, infants must detect structure behind pervasive variation. This presents substantial and largely untested learning challenges in early language acquisition. The current experiments address whether infants can use statistical learning mechanisms to segment words when the speech signal contains acoustic variation produced by changes in speakers' voices. In Experiment 1, 8- and 10-month-old infants listened to a continuous stream of novel words produced by 8 different female voices. The voices alternated frequently, potentially interrupting infants' detection of transitional probability patterns that mark word boundaries. Infants at both ages successfully segmented words in the speech stream. In Experiment 2, 8-month-olds demonstrated the ability to generalize their learning about the speech stream when presented with a new, acoustically distinct voice during testing. However, in Experiments 3 and 4, when the same speech stream was produced by only 2 female voices, infants failed to segment the words. The results of these experiments indicate that low acoustic variation may interfere with infants' efficiency in segmenting words from continuous speech, but that infants successfully use statistical cues to segment words in conditions of high acoustic variation. These findings contribute to our understanding of whether statistical learning mechanisms can scale up to meet the demands of natural learning environments.

Infants' statistical learning: 2- and 5-month-olds' segmentation of continuous visual sequences

Past research suggests that infants have powerful statistical learning abilities; however, studies of infants' visual statistical learning offer differing accounts of the developmental trajectory of and constraints on this learning. To elucidate this issue, the current study tested the hypothesis that young infants' segmentation of visual sequences depends on redundant statistical cues to segmentation. A sample of 20 2-month-olds and 20 5-month-olds observed a continuous sequence of looming shapes in which unit boundaries were defined by both transitional probability and co-occurrence frequency. Following habituation, only 5-month-olds showed evidence of statistically segmenting the sequence, looking longer to a statistically improbable shape pair than to a probable pair. These results reaffirm the power of statistical learning in infants as young as 5 months but also suggest considerable development of statistical segmentation ability between 2 and 5 months of age. Moreover, the results do not support the idea that infants' ability to segment visual sequences based on transitional probabilities and/or co-occurrence frequencies is functional at the onset of visual experience, as has been suggested previously. Rather, this type of statistical segmentation appears to be constrained by the developmental state of the learner. Factors contributing to the development of statistical segmentation ability during early infancy, including memory and attention, are discussed.

Emotional engagements predict and enhance social cognition in young chimpanzees

Social cognition in infancy is evident in coordinated triadic engagements, that is, infants attending jointly with social partners and objects. Current evolutionary theories of primate social cognition tend to highlight species differences in cognition based on human-unique cooperative motives. We consider a developmental model in which engagement experiences produce differential outcomes. We conducted a 10-year-long study in which two groups of laboratory-raised chimpanzee infants were given quantifiably different engagement experiences. Joint attention, cooperativeness, affect, and different levels of cognition were measured in 5- to 12-month-old chimpanzees, and compared to outcomes derived from a normative human database. We found that joint attention skills significantly improved across development for all infants, but by 12 months, the humans significantly surpassed the chimpanzees. We found that cooperativeness was stable in the humans, but by 12 months, the chimpanzee group given enriched engagement experiences significantly surpassed the humans. Past engagement experiences and concurrent affect were significant unique predictors of both joint attention and cooperativeness in 5- to 12-month-old chimpanzees. When engagement experiences and concurrent affect were statistically controlled, joint attention and cooperation were not associated. We explain differential social cognition outcomes in terms of the significant influences of previous engagement experiences and affect, in addition to cognition. Our study highlights developmental processes that underpin the emergence of social cognition in support of evolutionary continuity.

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function.

Infants segment continuous events using transitional probabilities

Throughout their 1st year, infants adeptly detect statistical structure in their environment. However, little is known about whether statistical learning is a primary mechanism for event segmentation. This study directly tests whether statistical learning alone is sufficient to segment continuous events. Twenty-eight 7- to 9-month-old infants viewed a sequence of continuous actions performed by a novel agent in which there were no transitional movements that could have constrained the possible upcoming actions. At test, infants distinguished statistically intact units from less predictable ones. The ability to segment events using statistical structure may help infants discover other cues to event boundaries, such as intentions, and carve up the world of continuous motion in meaningful ways.

Learning to look: probabilistic variation and noise guide infants' eye movements

Young infants have demonstrated a remarkable sensitivity to probabilistic relations among visual features (Fiser & Aslin, 2002; Kirkham et al., 2002). Previous research has raised important questions regarding the usefulness of statistical learning in an environment filled with variability and noise, such as an infant's natural world. In an eye-tracking experiment, 8-month-old infants viewed sequences of spatio-temporal events with three different transitional probabilities (1.0-Deterministic, 0.75-High probability, and 0.5-Low probability). Across two between-subjects conditions, the sequences were presented with or without competing visual distracters. Results show that as transitional probability decreased, infants distributed less attention to the predictable locations and their anticipations were less often correct. With no distraction, infants had faster saccadic latencies to the high probability events; however, with distracters present in the stimulus environment, infants' eye movements shifted to favour the deterministic relations. These findings suggest that infants integrate multiple sources of variability to guide visual attention and facilitate the detection and learning of statistically reliable events.

Expectancy learning from probabilistic input by infants

Across the first few years of life, infants readily extract many kinds of regularities from their environment, and this ability is thought to be central to development in a number of domains. Numerous studies have documented infants' ability to recognize deterministic sequential patterns. However, little is known about the processes infants use to build and update representations of structure in time, and how infants represent patterns that are not completely predictable. The present study investigated how infants' expectations fora simple structure develope over time, and how infants update their representations with new information. We measured 12-month-old infants' anticipatory eye movements to targets that appeared in one of two possible locations. During the initial phase of the experiment, infants either saw targets that appeared consistently in the same location (Deterministic condition) or probabilistically in either location, with one side more frequent than the other (Probabilistic condition). After this initial divergent experience, both groups saw the same sequence of trials for the rest of the experiment. The results show that infants readily learn from both deterministic and probabilistic input, with infants in both conditions reliably predicting the most likely target location by the end of the experiment. Local context had a large influence on behavior: infants adjusted their predictions to reflect changes in the target location on the previous trial. This flexibility was particularly evident in infants with more variable prior experience (the Probabilistic condition). The results provide some of the first data showing how infants learn in real time.

Learning about causes from people and about people as causes: probabilistic models and social causal reasoning

A major challenge children face is uncovering the causal structure of the world around them. Previous research on children's causal inference has demonstrated their ability to learn about causal relationships in the physical environment using probabilistic evidence. However, children must also learn about causal relationships in the social environment, including discovering the causes of other people's behavior, and understanding the causal relationships between others' goal-directed actions and the outcomes of those actions. In this chapter, we argue that social reasoning and causal reasoning are deeply linked, both in the real world and in children's minds. Children use both types of information together and in fact reason about both physical and social causation in fundamentally similar ways. We suggest that children jointly construct and update causal theories about their social and physical environment and that this process is best captured by probabilistic models of cognition. We first present studies showing that adults are able to jointly infer causal structure and human action structure from videos of unsegmented human motion. Next, we describe how children use social information to make inferences about physical causes. We show that the pedagogical nature of a demonstrator influences children's choices of which actions to imitate from within a causal sequence and that this social information interacts with statistical causal evidence. We then discuss how children combine evidence from an informant's testimony and expressed confidence with evidence from their own causal observations to infer the efficacy of different potential causes. We also discuss how children use these same causal observations to make inferences about the knowledge state of the social informant. Finally, we suggest that psychological causation and attribution are part of the same causal system as physical causation. We present evidence that just as children use covariation between physical causes and their effects to learn physical causal relationships, they also use covaration between people's actions and the environment to make inferences about the causes of human behavior.