论文标题
认知协议的组合背景性的扩展
An Extension Of Combinatorial Contextuality For Cognitive Protocols
论文作者
论文摘要
本文扩展了组合方法,以支持因果影响中的上下文性的确定。情境性是与心理现象有关的系统中的量子认知的积极研究领域,例如人类记忆中的概念[Aerts等,2013]。在认知研究领域中,确定现象是否为上下文的当代挑战是对干扰的识别和管理[Dzhafarov等,2016]。无论是通过建模方法构成因果影响,还是无视噪声,因为噪声很重要,因为噪声很重要,因为在存在因果影响的情况下无法充分确定上下文性[Gleason,1957年]。为了应对这一挑战,我们首先在Canonical9因果模型的语言中对组合方法的必要要素进行形式化。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,我们开发了一个方案,通过该方案可以通过该方案在认知实验中表示这些元素。由于人类认知似乎对因果影响泛滥,因此认知模块可能会采用扩展的组合方法实际上确定认知现象的背景性。
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.