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Online testing holds great promise for infant scientists. It could increase participant diversity, improve reproducibility and collaborative possibilities, and reduce costs for researchers and participants. However, despite the rise of platforms and participant databases, little work has been done to overcome the challenges of making this approach available to researchers across the world. In this paper, we elaborate on the benefits of online infant testing from a global perspective and identify challenges for the international community that have been outside of the scope of previous literature. Furthermore, we introduce ManyBabies-AtHome, an international, multi-lab collaboration that is actively working to facilitate practical and technical aspects of online testing and address ethical concerns regarding data storage and protection, and cross-cultural variation. The ultimate goal of this collaboration is to improve the method of testing infants online and make it globally available. [ABSTRACT FROM AUTHOR]

• Prior studies used behavioral data to infer infants' sense of agency. • The addition of EEG data allowed for testing infants' underlying causal model. • Neural indicator of action-effect prediction was related to limb-specific movement. • We provide neurobehavioral evidence for an emerging sense of agency. Research into the developing sense of agency has traditionally focused on sensitivity to sensorimotor contingencies, but whether this implies the presence of a causal action-effect model has recently been called into question. Here, we investigated whether 3- to 4.5-month-old infants build causal action-effect models by focusing on behavioral and neural measures of violation of expectation. Infants had time to explore the causal link between their movements and audiovisual effects before the action-effect contingency was discontinued. We tested their ability to predict the consequences of their movements and recorded neural (EEG) and movement measures. If infants built a causal action-effect model, we expected to observe their violation of expectation in the form of a mismatch negativity (MMN) in the EEG and an extinction burst in their movement behavior after discontinuing the action-effect contingency. Our findings show that the group of infants who showed an MMN upon cessation of the contingent effect demonstrated a more pronounced limb-specific behavioral extinction burst, indicating a causal action-effect model, compared to the group of infants who did not show an MMN. These findings reveal that, in contrast to previous claims, the sense of agency is only beginning to emerge at this age. [ABSTRACT FROM AUTHOR]

The development of a sense of agency is indispensable for a cognitive entity (biological or artificial) to become a cognitive agent. In developmental psychology, researchers have taken inspiration from adult cognitive psychology and neuroscience literature and use the comparator model to assess the presence of a sense of agency in early infancy. Similarly, robotics researchers have taken components of the proposed mechanism in attempts to build a sense of agency into artificial systems. In this article, we identify an invalidating theoretical flaw in the reasoning underlying this conversion from adult studies to developmental science and cognitive systems research, rooted in an oversight in the conceptualization of the comparator model as currently used in experimental practice. In these experiments, the emphasis has been put solely on testing for a match between predicted and observed sensory consequences. We argue that the match by itself can exclusively generate a simple categorization or a representation of equality between predicted and observed sensory consequences, both of which are insufficient to generate the causal representations required for a sense of agency. Consequently, the comparator model, as it has been described in the context of the sense of agency and as it is commonly used in experimental designs, is insufficient to generate the sense of agency: infants and robots require more than developing the ability to match predicted and observed sensory consequences for a sense of agency. We conclude with outlining possible solutions and future directions for researchers in developmental science and artificial intelligence. [ABSTRACT FROM AUTHOR]

Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.

At present, most developmental psychology experiments use participants from a mere subsection of the world's population. Moreover, like other fields of psychology, many studies in developmental psychology suffer from low statistical power due to small samples and limited observations. Online testing holds promise as a way to achieve more representative and robust, better powered experiments. As participants do not have to visit in person, it is easier to access populations living further away from a developmental lab, enabling testing of more diverse populations (e.g., urban vs rural areas, various different nationalities or geographies), both within and beyond the researcher's home country. Furthermore, due to the codified nature of browser-based online testing, it is possible for multiple labs to carry out the exact same study, allowing for better replications. Because of these advantages, developmental researchers have started to move experiments online so that caregivers and their children can participate from their home environments. However, the transition from traditional lab testing to remote online testing brings many challenges. Laboratory studies of infant and child development are typically conducted under highly standardized conditions to control factors, such as distractors, distance to the screen, movement, and lighting, and often rely on specialized equipment for measuring behavior. In this chapter, we provide a guide for researchers considering online testing of a developmental population. The different sections comprise an overview of the decision-making processes and the state-of-the-art advances associated with, as well as tangible recommendations for, online data collection., (Copyright © 2022 Elsevier Inc. All rights reserved.)