Infants segment continuous events using transitional probabilities

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.

Finding Structure in Modern Dance

Research has shown that both adults and children organize familiar activity into discrete units with consistent boundaries, despite the dynamic, continuous nature of everyday experiences. However, less is known about how observers segment unfamiliar event sequences. In the current study, we took advantage of the novelty that is inherent in modern dance. Modern dance features natural human motion but does not contain canonical goals-therefore, observers cannot recruit prior goal-related knowledge to segment it. Our main aims were to identify whether observers segment modern dance into the steps intended by the dancers, and what types of cues contribute to segmentation under these circumstances. Experiment 1 used a classic event segmentation task and found that adults were able to consistently identify only a few of the dancers' intended steps. Experiment 2 tested adults in an offline labeling task. Results showed that steps which could more easily be labeled offline in Experiment 2 were more likely to be segmented online in Experiment 1.

Dynamic Motion and Human Agents Facilitate Visual Nonadjacent Dependency Learning

Many events that humans and other species experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co-occurrences between temporally adjacent stimuli, others involve tracking the co-occurrences between temporally distant stimuli (i.e., nonadjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here, we report on seven experiments that examined NAD learning from various types of visual stimuli, exploring the effects of dynamic motion on adults' NAD learning from visual sequences involving human and nonhuman agents. We tested adults' NAD learning from visual sequences of human actions, object transformations, static images of human postures, and static images of an object in different postures. We found that dynamic motion aids the acquisition of NADs. We also found that learning NADs in sequences involving human agents is more robust compared to sequences involving nonhuman objects. We propose that dynamic motion and human agents both independently result in richer representations that provide a stronger signal for NAD learning.

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.

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.

The Development of Learning, Performing, and Controlling Repeated Sequential Actions in Young Children

Our daily lives are composed of several sequential actions that we perform routinely, such as making breakfast, taking a train, and changing clothes. Previous research has demonstrated that a routine system plays a role in performing and controlling repeated sequential actions in familiar situations, and a top-down control system involves the control of the routine system in novel situations. Specifically, most developmental studies have focused on the top-down control system (e.g., executive functions) as a factor enabling the control of goal-directed actions in novel situations. Yet, it has not been thoroughly examined how young children learn, perform, and control repeated sequential actions in familiar contexts. In this review, based on recent computational accounts for adults, we highlight two critical aspects of the routine system from a developmental perspective: (1) automatic flexible changes of contextual representations, which enables humans to select context-dependent actions appropriately; and (2) detection of deviant situations, which signals the need for control to avoid errors. In addition, we propose the developmental mechanism underlying the routine system and its potential driving factors such as statistical regularities and executive functions. Finally, we suggest that an investigation into the interplay between routine and executive functions can form foundations for understanding learning, performing, and controlling repeated sequential actions in young children and discuss future directions in this area.

Learning non-adjacent rules and non-adjacent dependencies from human actions in 9-month-old infants

Seven month old infants can learn simple repetition patterns, such as we-fo-we, and generalize the rules to sequences of new syllables, such as ga-ti-ga. However, repetition rule learning in visual sequences seems more challenging, leading some researchers to claim that this type of rule learning applies preferentially to communicative stimuli. Here we demonstrate that 9-month-old infants can learn repetition rules in sequences of non-communicative dynamic human actions. We also show that when primed with these non-adjacent repetition patterns, infants can learn non-adjacent dependencies that involve memorizing the dependencies between specific human actions-patterns that prior research has shown to be difficult for infants in the visual domain and in speech. We discuss several possible mechanisms that account for the apparent advantage stimuli involving human action sequences has over other kinds of stimuli in supporting non-adjacent dependency learning. We also discuss possible implications for theories of language acquisition.

Adaptive rule learning of event sequences during the A-not-B task in 9-month-old infants

Prior work indicates that infants can use social information to organize simple audiovisual inputs into predictable rules by 8 months of age. However, it is unclear whether infants can use social information to organize more complex events into predictable rules that can be used to guide motor action. To examine these issues, we tested 9-month-old infants using a modified version of an A-not-B task, in which hiding event sequences were paired with different experimenters, who could be used to organize the events into rules that guide action. We predicted that infants' reaching accuracy would be better when the experimenter changes when the toy's hiding location changes, relative to when the experimenter stays the same, as this should cue a novel rule used to guide action. Experiments 1 and 2 validated this prediction. Experiment 3 showed that reaching accuracy was better when the toy's hiding location switched but was consistent with the rule associated with the experimenter, relative to when the toy's hiding location repeated but was inconsistent with the rule associated with the experimenter. These data suggest that infants can use the identities of experimenters to organize events into predictable rules that guide action in the A-not-B task.

Learning From Others: The Effects of Agency on Event Memory in Young Children

Little is known about the influence of social context on children's event memory. Across four studies, we examined whether learning that could occur in the absence of a person was more robust when a person was present. Three-year-old children (N = 125) viewed sequential events that either included or excluded an acting agent. In Experiment 1, children who viewed an agent recalled more than children who did not. Experiments 2a and 2b utilized an eye tracker to demonstrate this effect was not due to differences in attention. Experiment 3 used a combined behavioral and event-related potential paradigm to show that condition effects were present in memory-related components. These converging results indicate a particular role for social knowledge in supporting memory for events.

Specificity of representations in infants' visual statistical learning

Past work has demonstrated infants' robust statistical learning across visual and auditory modalities. However, the specificity of representations produced via visual statistical learning has not been fully explored. The current study addressed this by investigating infants' abilities to identify previously learned object sequences when some object features (e.g., shape, face) aligned with prior learning and other features did not. Experiment 1 replicated past work demonstrating that infants can learn statistical regularities across sequentially presented objects and extended this finding to 16-month-olds. In Experiment 2, infants viewed test sequences in which one object feature (e.g., face) had been removed but the other feature (e.g., shape) was maintained, resulting in failure to identify familiar sequences. We further probed learning specificity by assessing infants' recognition of sequences when one feature was altered rather than removed (Experiment 3) and when one feature was uncorrelated with the original sequence structure (Experiment 4). In both cases, infants failed to identify sequences in which object features were not identical between learning and test. These findings suggest that infants are limited in their ability to generalize the statistical structure of an object sequence when the objects' features do not align between learning and test.

Network architectures supporting learnability

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit-the brain-that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human's knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

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.

Violations of Core Knowledge Shape Early Learning

Research on cognitive development has revealed that even the youngest minds detect and respond to events that adults find surprising. These surprise responses suggest that infants have a basic set of "core" expectations about the world that are shared with adults and other species. However, little work has asked what purpose these surprise responses serve. Here we discuss recent evidence that violations of core knowledge offer special opportunities for learning. Infants and young children make predictions about the world on the basis of their core knowledge of objects, quantities, and social entities. We argue that when these predictions fail to match the observed data, infants and children experience an enhanced drive to seek and retain new information. This impact of surprise on learning is not equipotent. Instead, it is directed to entities that are relevant to the surprise itself; this drive propels children-even infants-to form and test new hypotheses about surprising aspects of the world. We briefly consider similarities and differences between these recent findings with infants and children, on the one hand, and findings on prediction errors in humans and non-human animals, on the other. These comparisons raise open questions that require continued inquiry, but suggest that considering phenomena across species, ages, kinds of surprise, and types of learning will ultimately help to clarify how surprise shapes thought.

When learning goes beyond statistics: Infants represent visual sequences in terms of chunks

Much research has documented infants' sensitivity to statistical regularities in auditory and visual inputs, however the manner in which infants process and represent statistically defined information remains unclear. Two types of models have been proposed to account for this sensitivity: statistical models, which posit that learners represent statistical relations between elements in the input; and chunking models, which posit that learners represent statistically-coherent units of information from the input. Here, we evaluated the fit of these two types of models to behavioral data that we obtained from 8-month-old infants across four visual sequence-learning experiments. Experiments examined infants' representations of two types of structures about which statistical and chunking models make contrasting predictions: illusory sequences (Experiment 1) and embedded sequences (Experiments 2-4). In all four experiments, infants discriminated between high probability sequences and low probability part-sequences, providing strong evidence of learning. Critically, infants also discriminated between high probability sequences and statistically-matched sequences (illusory sequences in Experiment 1, embedded sequences in Experiments 2-3), suggesting that infants learned coherent chunks of elements. Experiment 4 examined the temporal nature of chunking, and demonstrated that the fate of embedded chunks depends on amount of exposure. These studies contribute important new data on infants' visual statistical learning ability, and suggest that the representations that result from infants' visual statistical learning are best captured by chunking models.

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'.

Infants' Motor Proficiency and Statistical Learning for Actions

Prior research has shown that infants learn statistical regularities in action sequences better than they learn non-action event sequences. This is consistent with current theories claiming that the same mechanism guides action observation and action execution. The current eye-tracking study tested the prediction, based on these theories, that infants' ability to learn statistical regularities in action sequences is modulated by their own motor abilities. Eight- to eleven-month-old infants observed an action sequence containing two deterministic action pairs (i.e., action A always followed by action B) embedded within an otherwise random sequence. One pair was performed with a whole-hand grasp. The second pair was performed with a pincer grasp, a fine motor skill that emerges around 9 months of age. Infants were then categorized into groups according to which grasp was dominant in their motor repertoire. Predictive looks to correct upcoming actions during the deterministic pairs were analyzed to measure whether infants learned and anticipated the sequence regularities. Findings indicate that infants learned the statistical regularities: across motor groups, they made more correct than incorrect predictive fixations to upcoming actions. Overall, learning was not significantly modulated by their dominant grasping abilities. However, infants with a dominant pincer grasp showed an earlier increase in correct predictions for the pincer grasp pair and not the whole-hand grasp. Likewise, infants with a dominant whole-hand grasp showed an early increase in correct predictions for the pair performed with a whole-hand grasp, and not the pincer grasp. Together, these findings suggest that infants' ability to learn action sequences is facilitated when the observed action matches their own action repertoire. However, findings cannot be explained entirely by motor accounts, as infants also learned the actions less congruent with their own abilities. Findings are discussed in terms of the interplay between the motor system and additional non-motor resources during the acquisition of new motor skills in infancy.

Infant Statistical Learning

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories.

Statistical learning in social action contexts

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Expectancy violations promote learning in young children

Children, including infants, have expectations about the world around them, and produce reliable responses when these expectations are violated. However, little is known about how such expectancy violations affect subsequent cognition. Here we tested the hypothesis that violations of expectation enhance children's learning. In four experiments we compared 3- to 6-year-old children's ability to learn novel words in situations that defied versus accorded with their core knowledge of object behavior. In Experiments 1 and 2 we taught children novel words following one of two types of events. One event violated expectations about the spatiotemporal or featural properties of objects (e.g., an object appeared to magically change locations). The other event was almost identical, but did not violate expectations (e.g., an object was visibly moved from one location to another). In both experiments we found that children robustly learned when taught after the surprising event, but not following the expected event. In Experiment 3 we ruled out two alternative explanations for our results. Finally, in Experiment 4, we asked whether surprise affects children's learning in a targeted or a diffuse way. We found that surprise only enhanced children's learning about the entity that had behaved surprisingly, and not about unrelated objects. Together, these experiments show that core knowledge - and violations of expectations generated by core knowledge - shapes new learning.

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.

Infants' observation of tool-use events over the first year of life

How infants observe a goal-directed instrumental action provides a unique window into their understanding of others' behavior. In this study, we investigated eye-gaze patterns while infants observed events in which an actor used a tool on an object. Comparisons among 4-, 7-, 10-, and 12-month-old infants and adults reveal changes in infants' looking patterns with age; following an initial face bias, infants' scan path eventually shows a dynamic integration of both the actor's face and the objects on which they act. This shift may mark a transition in infants' understanding of the critical components of tool-use events and their understanding of others' behavior.

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.