We constantly interpret and predict others’ decisions and behaviors when interacting with the world. Do our predictions of others’ decisions differ depending on whose behavior we are trying to predict? In four experiments, we probed how adults and children (N = 144, 6- to 8-years old) predict different agents’ intuitive numerical decisions. We varied the phylogenetic and ontogenetic history of the agents: an adult, a child, an infant, a chimpanzee, and an ant. We found that both adult and child participants predicted the adult and child agents to reliably choose the larger amount. Both adult and child participants predicted the infant and ant to choose at random. However, while adult participants attribute numerical decisions similar to adults and children to chimpanzees, children seem to think chimpanzees behave similarly to infants and ants. Overall, these results suggest that adults and children share similar intuitions about others’ numerical decisions based on agent identity.

Detecting young learners’ internal uncertainty is key for understanding underlying cognitive processes and designing Intelligent Tutoring Systems (ITS). Although young children may experience more uncertainty and learn faster, they are also more limited in the ability to express themselves verbally. Therefore, it is important to understand how uncertainty can be expressed in different ways. In this work, for the first time, we annotate and model different levels of uncertainty in young children performing a numerical discrimination task. We identify potential cues of uncertainty presented in different modalities (e.g., spontaneous verbal responses, hand gestures, facial expressions) and specifically examine the relationship between task difficulty, task performance, and exhibited levels of uncertainty. Our results show a positive correlation between the level of uncertainty expression across all modalities and task difficulty. This work informs research on cognitive coordination between human-human and human-AI collaboration and the design of personalized ITS systems.