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) 2023 APA, all rights reserved).

中文翻译：

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区分自我和他人的心理模型：知识评估的分层框架。

开发另一个智能体知识的准确模型是智能体之间沟通与合作的核心。在本文中，我们提出了一个知识评估的分层框架，它解释了人们如何构建自己的知识和他人的知识的心智模型。我们的框架假设人们通过贝叶斯推理整合有关自己和他人知识的信息。为了评估这一说法，我们进行了一项实验，参与者在相同的图像分类任务上反复评估自己的表现（元认知任务）和另一个人的表现（一种心理理论任务）。我们将分层框架与更简单的替代方案进行对比，这些替代方案假设自我和他人的心理模型之间存在不同程度的差异。我们的模型准确地捕捉了参与者在任务中对自己表现和他人表现的评估：最初，人们依靠自己的自我评估过程来推理他人的表现，从而导致类似的自我和他人表现预测。随着有关他人能力的更多信息的出现，他人的心理模型与自我的心理模型变得越来越不同。模拟研究还证实，我们的框架解释了有关人类对自己和他人的知识评估的广泛发现。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。