Differentiating mental models of self and others: A hierarchical framework for knowledge assessment.

Abstract

Developing an accurate model of another agent's knowledge is central to communication and cooperation between agents. In this article, we propose a hierarchical framework of knowledge assessment that explains how people construct mental models of their own knowledge and the knowledge of others. Our framework posits that people integrate information about their own and others' knowledge via Bayesian inference. To evaluate this claim, we conduct an experiment in which participants repeatedly assess their own performance (a metacognitive task) and the performance of another person (a type of theory of mind task) on the same image classification tasks. We contrast the hierarchical framework with simpler alternatives that assume different degrees of differentiation between mental models of self and others. Our model accurately captures participants' assessment of their own performance and the performance of others in the task: Initially, people rely on their own self-assessment process to reason about the other person's performance, leading to similar self- and other-performance predictions. As more information about the other person's ability becomes available, the mental model for the other person becomes increasingly distinct from the mental model of self. Simulation studies also confirm that our framework explains a wide range of findings about human knowledge assessment of themselves and others. (PsycInfo Database Record (c) 2024 APA, all rights reserved).