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ABSTRACT Most studies of developing visual attention are conducted using screen‐based tasks in which infants move their eyes to select where to look. However, real‐world visual exploration entails active movements of both eyes and head to bring relevant areas in view. Thus, relatively little is known about how infants coordinate their eyes and heads to structure their visual experiences. Infants were tested every 3 months from 9 to 24 months while they played with their caregiver and three toys while sitting in a highchair at a table. Infants wore a head‐mounted eye tracker that measured eye movement toward each of the visual targets (caregiver's face and toys) and how targets were oriented within the head‐centered field of view (FOV). With age, infants increasingly aligned novel toys in the center of their head‐centered FOV at the expense of their caregiver's face. Both faces and toys were better centered in view during longer looking events, suggesting that infants of all ages aligned their eyes and head to sustain attention. The bias in infants’ head‐centered FOV could not be accounted for by manual action: Held toys were more poorly centered compared with non‐held toys. We discuss developmental factors—attentional, motoric, cognitive, and social—that may explain why infants increasingly adopted biased viewpoints with age. 

Abstract Eye tracking provides direct, temporally and spatially sensitive measures of eye gaze. It can capture visual attention patterns from infancy through adulthood. However, commonly used screen-based eye tracking (SET) paradigms are limited in their depiction of how individuals process information as they interact with the environment in “real life”. Mobile eye tracking (MET) records participant-perspective gaze in the context of active behavior. Recent technological developments in MET hardware enable researchers to capture egocentric vision as early as infancy and across the lifespan. However, challenges remain in MET data collection, processing, and analysis. The present paper aims to provide an introduction and practical guide to starting researchers in the field to facilitate the use of MET in psychological research with a wide range of age groups. First, we provide a general introduction to MET. Next, we briefly review MET studies in adults and children that provide new insights into attention and its roles in cognitive and socioemotional functioning. We then discuss technical issues relating to MET data collection and provide guidelines for data quality inspection, gaze annotations, data visualization, and statistical analyses. Lastly, we conclude by discussing the future directions of MET implementation. Open-source programs for MET data quality inspection, data visualization, and analysis are shared publicly. 

Abstract This primer describes research on the development of motor behavior. We focus on infancy when basic action systems are acquired—posture, locomotion, manual actions, and facial actions—and we adopt a developmental systems perspective to understand the causes and consequences of developmental change. Experience facilitates improvements in motor behavior and infants accumulate immense amounts of varied everyday experience with all the basic action systems. At every point in development, perception guides behavior by providing feedback about the results of just prior movements and information about what to do next. Across development, new motor behaviors provide new inputs for perception. Thus, motor development opens up new opportunities for acquiring knowledge and acting on the world, instigating cascades of developmental changes in perceptual, cognitive, and social domains. This article is categorized under:Cognitive Biology > Cognitive DevelopmentPsychology > Motor Skill and PerformanceNeuroscience > Development 

How can researchers best measure infants' motor experiences in the home? Body position—whether infants are held, supine, prone, sitting, or upright—is an important developmental experience. However, the standard way of measuring infant body position, video recording by an experimenter in the home, can only capture short instances, may bias measurements, and conflicts with physical distancing guidelines resulting from the COVID-19 pandemic. Here, we introduce and validate an alternative method that uses machine learning algorithms to classify infants' body position from a set of wearable inertial sensors. A laboratory study of 15 infants demonstrated that the method was sufficiently accurate to measure individual differences in the time that infants spent in each body position. Two case studies showed the feasibility of applying this method to testing infants in the home using a contactless equipment drop-off procedure.