Moving towards Soul – an alchemical journey. Rehabilitation of women suffering from ‘chronic pain’ in a clinical and Jungian context

The aim of this paper is to explore the connection between symptom and symbol in the body of women suffering from chronic pain, diagnosed as fibromyalgia. The working hypothesis has been that the symbol that emerges from the symptom in the body can bridge the gap to a deeper meaning of pain and suffering, thereby becoming the agent of change for healing of the bodymind and the experience of pain in the physical body. To explore this subject I will introduce some recent research from the field of fibromyalgia, and the concepts of agency and affect systems in the body, which are important cornerstones in my work. I will briefly present my clinical concept of ‘Form and Freedom’. From this theoretical base I give some clinical examples of what I see as an alchemical journey towards soul, presented through vignettes, images and the words of three women – Maria, Riba and Ishtar. I conclude with how I see analytical psychology taking its rightful place alongside, informing or in conjunction with, as in my case, other psychotherapeutic modalities, working in creative ways that enhance healing in patients who suffer from chronic pain.     

Vector Semiotic Model for Visual Question Answering

In this paper, we propose a Vector Semiotic Model as a possible solution to the symbol grounding problem in the context of Visual Question Answering. The Vector Semiotic Model combines the advantages of a Semiotic Approach implemented in the Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model represents information about a scene depicted on an input image in a structured way and grounds abstract objects in an agent’s sensory input. We use the Vector Symbolic Architecture to represent the elements of the Sign-Based World Model on a computational level. Properties of a high-dimensional space and operations defined for high-dimensional vectors allow encoding the whole scene into a high-dimensional vector with the preservation of the structure. That leads to the ability to apply explainable reasoning to answer an input question. We conducted experiments are on a CLEVR dataset and show results comparable to the state of the art. The proposed combination of approaches, first, leads to the possible solution of the symbol-grounding problem and, second, allows expanding current results to other intelligent tasks (collaborative robotics, embodied intellectual assistance, etc.).