认知协议组合上下文性的一种扩展
An Extension Of Combinatorial Contextuality For Cognitive Protocols
摘要：
本文扩展了组合的方法来支持因果关系中的语境性的确定。情境性是量子认知中的一个活跃的研究领域，在与心理现象有关的系统中，例如人类记忆中的概念[April et al.，2013年]。在认知研究领域，确定一种现象是否是情境性的当代挑战是对干扰的识别和管理[Dzhafarov等人，2016年]。无论所述干扰是否通过建模方法识别，构成因果影响，或作为噪声是不可分辨的，都很重要，因为在存在因果影响的情况下，无法充分确定背景性[Gleason，1957]。为了应对这一挑战，我们首先提供了一个正规化的语言规范9因果模型的组合方法的必要元素。通过这种形式化，我们扩展了组合方法，以支持测量和治疗的干扰，并提供技术，分别区分噪声和因果关系的影响。此后，我们开发了一个协议，通过这些元素可以表示在认知实验。由于人类的认知似乎充满了因果关系的影响，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上下文。
英文摘要：
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 [Aerts et al., 2013]. 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 [Dzhafarov et al., 2016]. Whether or not said disturbances are identified through the modelling approach, constitute causal influences, or are disregardableas as noise is important, as contextuality cannot be adequately determined in the presence of causal influences [Gleason, 1957]. To address this challenge, we first provide a formalisation of necessary elements of the combinatorial approach within the language of canonical9 causal models. Through this formalisation, 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 modellers may apply the extended combinatorial approach to practically determine the contextuality of cognitive phenomena.
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