Affiliation:
1. Department of Psychology Harvard University
2. Graduate School of Education Harvard University
3. Department of Psychology Arizona State University
Abstract
AbstractModels 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.
Funder
James S. McDonnell Foundation
American Association of University Women
Subject
Artificial Intelligence,Cognitive Neuroscience,Human-Computer Interaction,Linguistics and Language,Experimental and Cognitive Psychology