Cognitive frameworks best represent cognitive information and knowledge. Several classical probability theory (CPT)-based cognitive frameworks were proposed in the literature. Recently, CPT has failed in explaining certain cognitive processes while quantum theories were successful in explaining the same. In this work, we integrate two cognitive frameworks namely conceptual spaces and 3-way formal concept analysis (3WFCA) to propose a high dimensional conceptual space (HDCS). Our new insights into the analysis of this proposal reveal its quantum characteristics. Among different cognitive processes that can be modelled, our interest is on phenomenal consciousness. Accordingly, we have proposed a formal method to achieve consciousness. We have also proposed an algorithm for the conceptual scaling of a cognitive scenario. HDCS represents a cognitive state using quality dimensions, attributes and their relations. Subsequently, the proposed model makes novel use of agent-environment interaction paradigm for guiding the interaction of HDCS with conceptually scaled cognitive scenario. Cognitive-state representation in HDCS is analogous to quantum state representation in a N-qubit system. Cognitive states are learnt through the parallel accumulation of evidences against all the attributes of multiple dimensions. The interaction between the HDCS and the scenario facilitates the identification and removal of uncertainties. The identified uncertainty coupled with its resolution time resembles Heisenberg-like uncertainty. The analogous of quantum two-slit self-interference would be comparison of evidence accumulation in HDCS with the preceding version of itself. Consequently, this research reveals that modelling consciousness requires a high dimensional space rather than set theoretic structures and support from quantum theories.

中文翻译：

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高维概念空间的量子方面：实现意识的模型

认知框架最能代表认知信息和知识。文献中提出了几种基于经典概率论（CPT）的认知框架。最近，CPT未能解释某些认知过程，而量子理论则成功地解释了它们。在这项工作中，我们集成了两个认知框架，即概念空间和3维形式概念分析（3WFCA），以提出高维概念空间（HDCS）。我们对这项提议进行分析的新见解揭示了其量子特征。在可以建模的不同认知过程中，我们的兴趣在于现象意识。因此，我们提出了一种实现意识的形式化方法。我们还提出了一种用于认知情景概念缩放的算法。HDCS使用质量维度，属性及其关系来表示认知状态。随后，提出的模型利用代理人与环境的交互范式新颖地指导了HDCS与概念性认知场景之间的交互。HDCS中的认知状态表示类似于N量子位系统中的量子状态表示。认知状态是通过针对多个维度的所有属性的证据的并行积累而学习的。HDCS与场景之间的交互作用有助于识别和消除不确定性。识别出的不确定性及其解决时间类似于海森堡式不确定性。量子两狭缝自干扰的相似之处是将HDCS中的证据积累与其自身的先前版本进行比较。