Obeid Abdul Karim, Bruza Peter, Moreira Catarina, Bruns Axel, Angus Daniel
ARC Centre of Excellence for Automated Decision-Making & Society (ADM+S), Brisbane, QLD, Australia.
School of Information Systems, Queensland University of Technology, Brisbane, QLD, Australia.
Front Psychol. 2022 May 20;13:871028. doi: 10.3389/fpsyg.2022.871028. eCollection 2022.
This article extends the combinatorial approach to support the determination of contextuality amidst causal influences. Contextuality is an active field of study in Quantum Cognition, in systems relating to mental phenomena, such as concepts in human memory. In the cognitive field of study, a contemporary challenge facing the determination of whether a phenomenon is contextual has been the identification and management of disturbances. Whether or not said disturbances are identified through the modeling approach, constitute causal influences, or are disregardableas as noise is important, as contextuality cannot be adequately determined in the presence of causal influences. To address this challenge, we first provide a formalization of necessary elements of the combinatorial approach within the language of canonical causal models. Through this formalization, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences. Thereafter, we develop a protocol through which these elements may be represented within a cognitive experiment. As human cognition seems rife with causal influences, cognitive modelers may apply the extended combinatorial approach to practically determine the contextuality of cognitive phenomena.
本文扩展了组合方法，以支持在因果影响中确定情境性。情境性是量子认知领域中的一个活跃研究方向，涉及与心理现象相关的系统，例如人类记忆中的概念。在认知研究领域，确定一种现象是否具有情境性面临的一个当代挑战是干扰的识别和管理。无论这些干扰是通过建模方法识别出来的、构成因果影响，还是被视为噪声而可忽略不计，这都很重要，因为在存在因果影响的情况下无法充分确定情境性。为应对这一挑战，我们首先在规范因果模型的语言中对组合方法的必要元素进行形式化。通过这种形式化，我们扩展了组合方法以支持对干扰的测量和处理，并提供分别区分噪声和因果影响的技术。此后，我们开发了一种协议，通过该协议这些元素可以在认知实验中得到体现。由于人类认知似乎充满了因果影响，认知建模者可以应用扩展的组合方法来实际确定认知现象的情境性。
