People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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理想化和贬值的社会推理模型。


人们经常形成两极分化的信念，将明确的积极或消极品质灌输给物体（例如，他们自己或他人）。在临床环境中，这被称为二分思维或“分裂”，是几种精神疾病的特征。在这里，我们引入了一种贝叶斯分裂模型，该模型将严格地将对象分类为完全“坏”或“好”的倾向参数化，而不是沿着连续的尺度灵活地学习处置。与之前的描述性理论不同，该模型对二分信念如何出现以及如何根据新信息进行更新进行定量预测。具体来说，该模型解决了分裂如何依赖于上下文的问题，同时又表现出随时间的稳定性。模型的一个关键特征是通过将反证据合理地归因于外部因素来巩固贬值和/或理想化的阶段。例如，当另一个人被理想化时，他们的不完美行为就会被归因于不利的外部环境。然而，足够的反证据可以触发极性转换，产生双稳态动态。我们证明该模型可以拟合经验数据，以衡量个体对关系不稳定的敏感性。例如，我们发现，在健康参与者中，潜在的绝对信念认为他人是“好人”，这使得他们对仁慈的人而不是恶意的人的性格印象不易改变，而且更加确定。相比之下，边缘型人格障碍参与者的性格印象显示出明显更高且更对称的分裂。提出的生成框架邀请在心理治疗背景下对振荡关系和情感动力学进行建模的应用。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。