Adapting studies typically run in the lab, preschool, or museum to online data collection presents a variety of challenges. The solutions to those challenges depend heavily on the specific questions pursued, the methods used, and the constraints imposed by available technology. We present a partial sample of solutions, discussing approaches we have developed for adapting studies targeting a range of different developmental populations, from infants to school-aged children, and utilizing various online methods such as high-framerate video presentation, having participants interact with a display on their own computer, having the experimenter interact with both the participant and an actor, recording free-play with physical objects, recording infant looking times both offline and live, and more. We also raise issues and solutions regarding recruitment and representativeness in online samples. By identifying the concrete needs of a given approach, tools that meet each of those individual needs, and interfaces between those tools, we have been able to implement many (but not all) of our studies using online data collection during the COVID-19 pandemic. This systematic review aligning available tools and approaches with different methods can inform the design of future studies, in and outside of the lab.

Affective states, exploration, and learning are tightly inter- twined. For example, research has connected surprise to play and learning in early development (Stahl & Feigenson, 2015), but less is known about the potential impact of other affec- tive states and how they might influence exploration and sub- sequent discovery. Given that past research has suggested that awe may increase feelings of uncertainty and lead to pursuit of cognitive accommodation in adults (Valdesolo & Graham, 2014), we posit that awe-induced uncertainty may similarly lead children to think-outside-the-box and explore more during play. In Experiment 1, we modify emotion-inducing videos (Awe, Happy and Calm) and validate them on adult participants using the perceived self-size Circle Task (Bai et al., 2017). In Experiment 2, children were presented with one of the three videos and their exploratory play with a novel toy was recorded. Results revealed both a significant effect of the manipulation (children associated with smaller selves in the Awe condition) and also an influence of the videos on play. Children in the Awe condition played more and explored more variably than children in the control conditions. These results suggest that awe influences motivation that increases variability and discovery in exploration.

Despite sometimes noisy evidence (e.g., perceptual processing errors), young children are capable of predicting and evaluating events based on complex causal representations. Children rapidly revise their beliefs and learn scientific conceptssometimes without prior knowledge of an underlying causal system. What might we need in our computational models of belief revision to similarly simulate children's behaviors when learning such causal systems? Building from experimental data of elementary school children's intuitive beliefs and predictions of water displacement, we propose three aspects of human inference and belief revision that warrant attention within the subfield of computational cognition. Each aspect is described by identifying the gaps between empirical findings and current computational implementations. Then, specific implementations of these aspects are built using models of Theory-based Bayesian inference. First, we construct children's prior beliefs at the individual level based on their prior behavior. Second, we approximate children's learning using an "optimal" Bayesian model, revealing the dynamics of belief revision trial-by-trial. Third, we investigate the role prediction may have in facilitating learning. By performing these key computational steps, we find support for contemporary claims that children may be approximately "Bayesian" learners and increase awareness of the importance of generating predictions in active learning.

Lieder and Griffiths present the computational framework “resource-rational analysis” to address the reverse-engineering problem in cognition. Here we discuss how developmental psychology affords a unique and critical opportunity to employ this framework, but which is overlooked in this piece. We describe how developmental change provides an avenue for ongoing work as well as inspiration for expansion of the resource-rational approach.

There exists a rich literature describing how social context influences decision making. Here, we propose a novel framing of social influences, the Intentional Selection Assumption. This framework proposes that, when a person is presented with a set of options by another social agent, people may treat the set of options as intentionally selected, reflecting the chooser's inferences about the presenter and the presenter's goals. To describe our proposal, we draw analogies to the cognition literature on sampling inferences within concept learning. This is done to highlight how the Intentional Selection Assumption accounts for both normative (e.g., comparing perceived utilities) and subjective (e.g., consideration of context relevance) principles in decision making, while also highlighting how analogous findings in the concept learning literature can aid in bridging these principles by drawing attention to the importance of potential sampling assumptions within decision making paradigms. We present the two behavioral experiments that provide support to this proposal and find that social-contextual cues influence choice behavior with respect to the induction of sampling assumptions. We then discuss a theoretical framework of the Intentional Selection Assumption alongside the possibility of its potential relationships to contemporary models of choice. Overall, our results emphasize the flexibility of decision makers with respect to social-contextual factors without sacrificing systematicity regarding the preference for specific options with a higher value or utility.