Schulz Lion, Streicher Yannick, Schulz Eric, Bhui Rahul, Dayan Peter
Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
Helmholtz Institute for Human-Centered AI, Helmholtz Munich, Munich, Germany.
PLoS Comput Biol. 2025 May 14;21(5):e1012814. doi: 10.1371/journal.pcbi.1012814. eCollection 2025 May.
From the intimate realm of personal interactions to the sprawling arena of political discourse, discerning the trustworthy from the dubious is crucial. Here, we present a novel behavioral task and accompanying Bayesian models that allow us to study key aspects of this learning process in a tightly controlled setting. In our task, participants are confronted with several different types of (mis-)information sources, ranging from ones that lie to ones with biased reporting, and have to learn these attributes under varying degrees of feedback. We formalize inference in this setting as a doubly Bayesian learning process where agents simultaneously learn about the ground truth as well as the qualities of an information source reporting on this ground truth. Our model and detailed analyses reveal how participants can generally follow Bayesian learning dynamics, highlighting a basic human ability to learn about diverse information sources. This learning is also reflected in explicit trust reports about the sources. We additionally show how participants approached the inference problem with priors that held sources to be helpful. Finally, when outside feedback was noisier, participants still learned along Bayesian lines but struggled to pick up on biases in information. Our work pins down computationally the generally impressive human ability to learn the trustworthiness of information sources while revealing minor fault lines when it comes to noisier environments and news sources with a slant.
从个人互动的私密领域到政治话语的广阔舞台，辨别可信与可疑至关重要。在此，我们提出了一种新颖的行为任务及配套的贝叶斯模型，使我们能够在严格控制的环境中研究这一学习过程的关键方面。在我们的任务中，参与者会面对几种不同类型的（错误）信息源，从说谎的信息源到有偏见报道的信息源，并且必须在不同程度的反馈下学习这些属性。我们将这种情况下的推理形式化为一个双重贝叶斯学习过程，即参与者同时了解基本事实以及报道该基本事实的信息源的质量。我们的模型和详细分析揭示了参与者通常如何遵循贝叶斯学习动态，突出了人类了解不同信息源的基本能力。这种学习也反映在对信息源的明确信任报告中。我们还展示了参与者如何利用认为信息源有帮助的先验知识来解决推理问题。最后，当外部反馈更嘈杂时，参与者仍沿着贝叶斯思路学习，但难以察觉信息中的偏差。我们的工作从计算角度确定了人类在学习信息源可信度方面普遍令人印象深刻的能力，同时揭示了在更嘈杂的环境和有倾向性的新闻源方面存在的小问题。
