When people make decisions, they act in a way that is either automatic (“rote”), or more thoughtful (“reflective”). But do people notice when others are behaving in a rote way, and do they care? We examine the detection of rote behavior and its consequences in U.S. adults, focusing specifically on pedagogy and learning. We establish repetitiveness as a cue for rote behavior (Experiment 1), and find that rote people are seen as worse teachers (Experiment 2). We also find that the (...) more a person's feedback seems similar across groups (indicating greater rote-ness), the more negatively their teaching is evaluated (Experiment 3). A word-embedding analysis of an open-response task shows people naturally cluster rote and reflective teachers into different semantic categories (Experiment 4). We also show that repetitiveness can be decoupled from perceptions of rote-ness given contextual explanation (Experiment 5). Finally, we establish two additional cues to rote behavior that can be tied to quality of teaching (Experiment 6). These results empirically show that people detect and care about scripted behaviors in pedagogy, and suggest an important extension to formal frameworks of social reasoning. (shrink)

Models of the explore–exploit problem have explained how children's decision making is weighed by a bias for information (directed exploration), randomness, and generalization. These behaviors are often tested in domains where a choice to explore (or exploit) is guaranteed to reveal an outcome. An often overlooked but critical component of the assessment of explore–exploit decisions lies in the expected success of taking actions in the first place—and, crucially, how such decisions might be carried out when learning from others. Here, we (...) examine how children consider an informal teacher's beliefs about the child's competence when deciding how difficult a task they want to pursue. We present a simple model of this problem that predicts that while learners should follow the recommendation of an accurate teacher, they should exploit easier games when a teacher overestimates their abilities, and explore harder games when she underestimates them. We tested these predictions in two experiments with adults (Experiment 1) and 6- to 8-year-old children (Experiment 2). In our task, participants' performance on a picture-matching game was either overestimated, underestimated, or accurately represented by a confederate (the “Teacher”), who then presented three new matching games of varying assessed difficulty (too easy, too hard, just right) at varying potential reward (low, medium, high). In line with our model's predictions, we found that both adults and children calibrated their choices to the teacher's representation of their competence. That is, to maximize expected reward, when she underestimated them, participants chose games the teacher evaluated as being too hard for them; when she overestimated them, they chose games she evaluated as being too easy; and when she was accurate, they chose games she assessed as being just right. This work provides insight into the early-emerging ability to calibrate explore–exploit decisions to others' knowledge when learning in informal pedagogical contexts. (shrink)