Is it personal or is it social? The interaction of knowledge domain and statistical evidence in U.S. and Chinese preschoolers' social generalizations.

Children make inferences about the social world by observing human actions. However, human actions can be ambiguous: They can be sources of information about personal, idiosyncratic characteristics of individuals or socially shared knowledge. In two cross-cultural studies (N = 420; Mage = 4.05 years, SD = 0.77, 47% female), we ask if U.S. and Chinese children's inferences about whether an action is personal or social vary by domain, statistical evidence, and culture. We did this with a generalization method: Preschoolers learn about one agent's actions and then are asked what they think a new agent will do. Low rates of generalization suggest children inferred something unique to an individual, while high rates suggest that children inferred that the action represented socially shared knowledge. In a mixed between- and within-participant design, children observed agents demonstrate sequences of statistically random (or nonrandom, between participants) actions that were verbally framed as relevant to a particular domain (agent's personal preferences, labels, object functions, or game rules). We found that children's social generalizations about actions were on a continuum: with linguistic conventions (e.g., labels) being the most social, preferences being the most personal, and nonlinguistic conventions (i.e., object functions, game rules) falling somewhere in between. Furthermore, the influence of statistical evidence and cultural variation varied for each domain. These findings highlight how children combine knowledge and evidence to infer social meaning from actions and have implications for rational constructivist accounts of cultural learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

School-age children are more skeptical of inaccurate robots than adults.

We expect children to learn new words, skills, and ideas from various technologies. When learning from humans, children prefer people who are reliable and trustworthy, yet children also forgive people's occasional mistakes. Are the dynamics of children learning from technologies, which can also be unreliable, similar to learning from humans? We tackle this question by focusing on early childhood, an age at which children are expected to master foundational academic skills. In this project, 168 4-7-year-old children (Study 1) and 168 adults (Study 2) played a word-guessing game with either a human or robot. The partner first gave a sequence of correct answers, but then followed this with a sequence of wrong answers, with a reaction following each one. Reactions varied by condition, either expressing an accident, an accident marked with an apology, or an unhelpful intention. We found that older children were less trusting than both younger children and adults and were even more skeptical after errors. Trust decreased most rapidly when errors were intentional, but only children (and especially older children) outright rejected help from intentionally unhelpful partners. As an exception to this general trend, older children maintained their trust for longer when a robot (but not a human) apologized for its mistake. Our work suggests that educational technology design cannot be one size fits all but rather must account for developmental changes in children's learning goals.

The minds of machines: Children's beliefs about the experiences, thoughts, and morals of familiar interactive technologies.

Children are developing alongside interactive technologies that can move, talk, and act like agents, but it is unclear if children's beliefs about the agency of these household technologies are similar to their beliefs about advanced, humanoid robots used in lab research. This study investigated 4-11-year-old children's (N = 127, Mage = 7.50, SDage = 2.27, 53% females, 75% White; from the Northeastern United States) beliefs about the mental, physical, emotional, and moral features of two familiar technologies (Amazon Alexa and Roomba) in comparison to their beliefs about a humanoid robot (Nao). Children's beliefs about the agency of these technologies were organized into three distinct clusters-having experiences, having minds, and deserving moral treatment. Children endorsed some agent-like features for each technology type, but the extent to which they did so declined with age. Furthermore, children's judgment of the technologies' freedom to "act otherwise" in moral scenarios changed with age, suggesting a development shift in children's understanding of technologies' limitations. Importantly, there were systematic differences between Alexa, Roomba, and Nao, that correspond to the unique characteristics of each. Together these findings suggest that children's intuitive theories of agency are informed by an increasingly technological world. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Constrained Choice: Children's and Adults' Attribution of Choice to a Humanoid Robot.

Young children, like adults, understand that human agents can flexibly choose different actions in different contexts, and they evaluate these agents based on such choices. However, little is known about children's tendencies to attribute the capacity to choose to robots, despite increased contact with robotic agents. In this paper, we compare 5- to 7-year-old children's and adults' attributions of free choice to a robot and to a human child by using a series of tasks measuring agency attribution, action prediction, and choice attribution. In morally neutral scenarios, children ascribed similar levels of free choice to the robot and the human, while adults were more likely to ascribe free choice to the human. For morally relevant scenarios, however, both age groups considered the robot's actions to be more constrained than the human's actions. These findings demonstrate that children and adults hold a nuanced understanding of free choice that is sensitive to both the agent type and constraints within a given scenario.

The Community-Engaged Lab: A Case-Study Introduction for Developmental Science.

Due to the closing of campuses, museums, and other public spaces during the pandemic, the typical avenues for recruitment, partnership, and dissemination are now unavailable to developmental labs. In this paper, we show how a shift in perspective has impacted our lab's ability to successfully transition to virtual work during the COVID-19 shut-down. This begins by recognizing that any lab that relies on local communities to engage in human research is itself a community organization. From this, we introduce a community-engaged lab model, and explain how it works using our own activities during the pandemic as an example. To begin, we introduce the vocabulary of mission-driven community organizations and show how we applied the key ideas of mission, vision, and culture to discussions of our own lab's identity. We contrast the community-engaged lab model with a traditional bi-directional model of recruitment from and dissemination to communities and describe how the community-engaged model can be used to reframe these and other ordinary lab activities. Our activities during the pandemic serve as a case study: we formed new community partnerships, engaged with child "citizen-scientists" in online research, and opened new avenues of virtual programming. One year later, we see modest but quantifiable impact of this approach: a return to pre-pandemic diversity in our samples, new engagement opportunities for trainees, and new sustainable partnerships. We end by discussing the promise and limitations of the community-engaged lab model for the future of developmental research.