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.

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 ( = 420; = 4.

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 ( = 127, = 7.50, = 2.

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.

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 .