The Quantum-Like Structure of Human Emotions: A Cognitive and Neurocomputational Perspective

Diederik Aerts, Sandro Sozzo, Tomás Veloz

Yoshihiro Maruyama

Yaochen Liu, Yazhou Zhang, Dawei Song

Sarcasm, sentiment, and emotion are three typical kinds of spontaneous affective responses of humans to external events and they are tightly intertwined with each other. Such events may be expressed in multiple modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal conversations. Joint analysis of humans’ multi-modal sarcasm, sentiment, and emotion is an important yet challenging topic, as it is a complex cognitive process involving both cross-modality interaction and cross-affection correlation. From the probability theory perspective, cross-affection correlation also means that the judgments on sarcasm, sentiment, and emotion are incompatible. However, this exposed phenomenon cannot be sufficiently modelled by classical probability theory due to its assumption of compatibility. Neither do the existing approaches take it into consideration. In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis. Specifically, we propose aQUantum probabIlity driven multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET. Extensive experiments on two datasets and the results show that the effectiveness and advantages of QUIET in comparison with a wide range of the state-of-the-art baselines. We also show the great potential of QP in multi-affect analysis.

Carol Popp Weingarten, Pudugramam Murali Doraiswamy, Matthew P. A. Fisher

Tariq Mahmood, Rehan Ahmad Khan Sherwani

Abstract Perceptual memory is important for human cognition and learning because it plays an important role in forming consciousness, memories, learning, and stimulus awareness. Perceptual memory assists in the recognition of items or faces, as well as in the sorting, differentiating, and combining of new information with context and emotions. Perceptual-associative memory encodes information structurally, semantically, and phonetically. It interacts with the conscious artifact's medium-term memory (MTM), long-term memory (LTM), workspace, and emotion modules to construct meanings and connections to chosen sensory information. This research developed a quantum cognitive model of perceptual associative memory to replicate human-like perceptual cognitions in conscious artifacts. Moreover, for the quantum neural correlates of consciousness, this research has also simulated the circuit of the quantum neural network for non-linear learning of an XOR gate using the qiskit library.

Ruilin Qin, Changle Zhou

Emotions play an important role in human cognition. With the development of affective computing, there is an increasing number of studies on building emotional machines. However, today's machines are far from having emotional feelings, which are the very nature of emotions. To address this issue, we propose a quantum circuit model of emotions and feelings based on quantum theory. In this model, an emotion is always accompanied by a feeling, and their simultaneity is modeled by quantum entanglement. In addition, mixed emotions are modeled as quantum superposition states and the generation of a specific emotion is regarded as the result of quantum collapse by measurement which can be seen as cognitive activities. Experimental results on the IBM quantum simulation platform demonstrate the feasibility of the proposed model, suggesting that quantum computing is a promising way to create genuine emotional machines.