Edwards Darren J, McEnteggart Ciara, Barnes-Holmes Yvonne
Department of Public Health, Policy, and Social Sciences, Swansea University, Swansea, United Kingdom.
Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium.
Front Psychol. 2022 Mar 2;13:745306. doi: 10.3389/fpsyg.2022.745306. eCollection 2022.
Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining the emergence of background knowledge processes. In order to explain background knowledge, we present a non-similarity-based post-Skinnerian theory of human language called Relational Frame Theory (RFT) which is rooted in a philosophical world view called functional contextualism (FC). This theory offers a very different interpretation of how categories emerge through the functions of behavior and through contextual cues, which may be of some benefit to existing categorization theories. Specifically, RFT may be able to offer a novel explanation of how background knowledge arises, and we provide some mathematical considerations in order to identify a formal model. Finally, we discuss much of this work within the broader context of general semantic knowledge and artificial intelligence research.
心理学受益于大量有关认知过程与大脑如何组织信息的知识。在分类文献中，这种行为通常通过称为范例理论和原型理论的记忆构建理论来解释，这些理论通常基于相似性或规则函数，作为类别如何出现的解释。尽管这些理论在对实验室环境中高度受控的刺激进行建模时效果很好，但在这些环境之外它们的表现往往较差，比如在解释背景知识过程的出现时。为了解释背景知识，我们提出了一种基于后斯金纳主义的非相似性人类语言理论，称为关系框架理论（RFT），它植根于一种称为功能情境主义（FC）的哲学世界观。该理论对类别如何通过行为功能和情境线索出现提供了一种非常不同的解释，这可能对现有的分类理论有所帮助。具体而言，RFT或许能够对背景知识如何产生提供一种新颖的解释，并且我们提供了一些数学考量以确定一个形式模型。最后，我们在一般语义知识和人工智能研究的更广泛背景下讨论这项工作的许多内容。
