Predictive Processing (PP) has been suggested to account for early cognitive development (Köster et al., 2020). In this paper, we extend its application to early social coordination in parent-infant interactions. Interpersonal neural synchrony in parent-child interactions is hypothesized to be a function of the coupling of internal generative models for mutual prediction. By aligning these internal models and reducing prediction errors, social uncertainty is reduced, and interpersonal neural synchrony is enhanced. Support for this hypothesis is provided by assessing neural synchrony during mother-infant interaction.

In crowded and cluttered environments, infants can reduce visualclutter by using manual actions to bring objects closer to the eyes,what we refer to as hand-eye coordination. Hand-eye coordinationis therefore hypothesized to be an important ability for controllingand distributing attention. Little is known about how the emergingability to integrate both gaze and manual actions onto objectsimpacts how attention is distributed. Twenty-five infantsparticipated in a naturalistic toy play session that included 24 toys.Overall, infants generated distributions of attention that were right-skewed, reflecting coherence: a composition of selectivity of a fewhighly-frequent toys and exploration of many less-frequent toys. Weobserved that individual differences in hand-eye coordinationimpacted distributions of attention, with infants displaying lowhand-eye coordination having dramatically less coherentdistributions of visual attention during bouts of hand-eyecoordination. These results suggest that hand-eye coordination is acritical pathway for visual attention.

Human interaction involves the organization of a collection ofsensorimotor systems across space and time. The study ofhow coordination develops in child-parent interaction hasprimarily focused on understanding the development ofspecific coordination patterns from individual modalities.However, less work has taken a systems view andinvestigated the development of coordination among multipleinterdependent behaviors. In the present work, we usedGranger causality as a mathematical model to constructdyadic causal networks of multimodal data collected from alongitudinal study of child-parent interaction. At a group-level, we observed increases in the number of causal links andin the strength of such links in dyadic interaction from 9-months to 12-months. At an individual-level, we observedhigh variability in the types of causal links that emergedacross developmental ages. We discuss these results in termsof a multicausality hypothesis for the development of humancoordination.

The study of the coordination of attention, a term called jointattention (JA), has resulted in a better understanding of thedynamics and development of communication. Despite theimportant insights gained from studying JA, there is littleconsensus regarding the specific components that are included inoperationalizing JA. The present work explored a parameter spaceof JA during a dyadic naturalistic toy play task between 9-month-old infants and their parents. We systematically measured thetemporal properties of two components commonly used tooperationalize JA: the duration of continuous alignment of parentand infant visual fixations and the flexibility of fluctuations ofattention. The results show that very brief bouts of JA areimportant predictors for vocabulary development. The results fromthis work provide new insights into the specific properties used tooperationalize JA and point to the importance of consideringmultiple timescales of behavior that make up JA.

The study of vocal coordination between infants and adults has led to important insights into the development of social, cognitive, emotional and linguistic abilities. We used an automatic system to identify vocalizations produced by infants and adults over the course of the day for fifteen infants studied longitudinally during the first two years of life. We measured three different types of vocal coordination: coincidence-based, rate-based, and cluster-based. Coincidence-based and rate-based coordination are established measures in the developmental literature. Cluster-based coordination is new and measures the strength of matching in the degree to which vocalization events occur in hierarchically nested clusters. We investigated whether various coordination patterns differ as a function of vocalization type, whether different coordination patterns provide unique information about the dynamics of vocal interaction, and how the various coordination patterns each relate to infant age. All vocal coordination patterns displayed greater coordination for infant speech-related vocalizations, adults adapted the hierarchical clustering of their vocalizations to match that of infants, and each of the three coordination patterns had unique associations with infant age. Altogether, our results indicate that vocal coordination between infants and adults is multifaceted, suggesting a complex relationship between vocal coordination and the development of vocal communication.

Traditionally different approaches to the study of cognition have been viewed as competing explanatory frameworks. An alternative view, explanatory pluralism, regards different approaches to the study of cognition as complementary ways of studying the same phenomenon, at specific temporal and spatial scales, using appropriate methodological tools. Explanatory pluralism has been often described abstractly, but has rarely been applied to concrete cases. We present a case study of explanatory pluralism. We discuss three separate ways of studying the same phenomenon: a perceptual decision-making task (Bahrami et al., 2010), where pairs of subjects share information to jointly individuate an oddball stimulus among a set of distractors. Each approach analyzed the same corpus but targeted different units of analysis at different levels of description: decision-making at the behavioral level, confidence sharing at the linguistic level, and acoustic energy at the physical level. We discuss the utility of explanatory pluralism for describing this complex, multiscale phenomenon, show ways in which this case study sheds new light on the concept of pluralism, and highlight good practices to critically assess and complement approaches.

In auditory statistical learning, children are sensitive to the transitional structure of their language environment. Vari-ability and stability of utterances in the language environment are important properties of statistical learning but are currentlyunderstudied across laboratory and naturalistic research contexts. In this study, we quantify variability and stability in the lan-guage environment of children as measured by amount of information within the temporal structure of caregivers’ utterances. Inthis work we present a new method for understanding information signatures in the temporal structure of parent-child free playcontexts and document information signatures of caregiver utterances at multiple timescales. Our results suggest informationsignatures of parental utterances increase across development (9-24 months), but decrease within individual play sessions (5-6minutes). We speculate that the dynamics of information signatures varies across multiple timescales. Possible implications ofthe observed information signatures inherent in caregivers’ naming of objects to their young children are explored.

Infants are powerful learners. A large corpus of experimental paradigms demonstrate that infants readily learn distributional cues of name-object co-occurrences. But infants’ natural learning environment is cluttered: every heard word has multiple competing referents in view. Here we ask how infants start learning name-object co-occurrences in naturalistic learning environments that are cluttered and where there is much visual ambiguity. The framework presented in this paper integrates a naturalistic behavioral study and an application of a machine learning model. Our behavioral findings suggest that in order to start learning object names, infants and their parents consistently select a set of a few objects to play with during a set amount of time. What emerges is a frequency distribution of a few toys that approximates a Zipfian frequency distribution of objects for learning. We find that a machine learning model trained with a Zipf-like distribution of these object images outperformed the model trained with a uniform distribution. Overall, these findings suggest that to overcome referential ambiguity in clutter, infants may be selecting just a few toys allowing them to learn many distributional cues about a few name-object pairs.