HUMAN WHOLENESS IN LIGHT OF FIVE TYPES OF PSYCHIC DUALITY

Abstract. Five types of psychic duality are distinguished: bipolarity, bimodality, contrariety, dualism, and the coincidentia op–positorurn. Bipolarity is the basic division of the psyche into egoic and nonegoic (physico–dynamic) poles. Bimodality is the division of egoic functioning into active and receptive modes. Contrariety is the division of the nonegoic sphere into opposing sympathetic and parasympathetic systems. Dualism is the organization imposed upon the bipolar structure by primal repression. And the coincidentia opositorum is the condition of psychic integration that would emerge were dualism to be transcended and the bipolar structure (together with the bimodal and contrarietal structures) unified into a higher whole.     

SPIRITUAL INTELLIGENCE: PROCESSING DIFFERENT INFORMATION OR PROCESSING INFORMATION DIFFERENTLY?

This article introduces the concept of spiritual intelligence in terms of a natural human ability to take a different perspective on reality rather than an extraordinary ability to engage with a different/supernatural reality. From a cognitive perspective, spiritual intelligence entails a re-balancing of the two main modes of human cognition, with a prioritization of the holistic-intuitive mind over the conceptual one. From the psychological and phenomenological perspectives, it involves a different kind of engagement with information: slower, more participatory, less objectifying, and not focused entirely on problem solving. The article ends with a reflection on the theological implications of the proposed model and how such an account of spiritual intelligence as knowing differently might relate to theological anthropology and the theology of the spirit and the spiritual.     

A review and critique of the process approach in neuropsychological assessment

This paper surveys the process approach literature with an emphasis on higher level cognitive functions like attention and concentration, learning and memory, and problem solving and executive functioning. In particular, it discusses organizational strategies and kinds of errors found on a variety of tests as well as behavioral, situational, and interpersonal issues as they relate to test procedures. It critiques the process approach and suggests future directions.     

An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence

Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data, which suggests that considering an agent’s internal influences (from cognitive systems that peripherally interact with visual-perceptual processes) recognition can be improved. Then, we propose a model for visual object recognition which uses these systems’ information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Declarative working memory: A bio-inspired cognitive architecture proposal

Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory. Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events. This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes. The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes.     

Socially emotional brain-inspired cognitive architecture framework for artificial intelligence

Artificial Intelligence of the next generation needs to interact with users socially, convincing them in its ability to understand human minds, including emotions. For this to happen, an artificial emotional intelligence is needed, capable of adequate, believable behavior in social emotional interactions. Building on previous developments, the present work extends the general framework of emotional Biologically Inspired Cognitive Architecture (eBICA: Samsonovich, 2013, 2018), endowing it with fluents describing, in addition to appraisals, somatic markers, feelings, emotions, moods, emotional reactions and biases. Key building blocks that integrate them are moral schemas and semantic maps. The model describes interaction of three factors: plans and commitments, moral and ethical values, and somatic comfort. Learning in this framework includes self-organization of semantic maps that in turn may provide guidance for active humanlike learning. Implications for empirical studies and practical applications are discussed together with the expected impact.     

The TROCA Project: An autonomous transportation robot controlled by a cognitive architecture

Autonomous mobile robots emerged as an important kind of transportation system in warehouses and factories. In this work, we present the use of MECA cognitive architecture in the development of an artificial mind for an autonomous robot responsible for multiple tasks, including transportation of packages along a factory floor, environment exploration, warehouse inventory, its internal energy management, self-monitoring and dealing with human operators and other robots. The present text provides a detailed specification for the architecture and its software implementation. Future work will present the simulation results under different configurations, together with a detailed analysis of the architecture performance and its generalization for autonomous robot control.     

Love and space in the nursing home

Nursing homes and other institutionsdesigned for persons with impairments are not,in fact, designed for persons with impairments.They are typically designed for theimpairments, not the persons, and therebybecome a part of the problem by reinforcingphysical and cultural manifestations of theimpairments. In the essay that follows, Idescribe an architectural design project inwhich students were asked to make changes to anexisting nursing home for the persons who livedthere. This requires not only becoming familiarwith the spaces, but with the personsthemselves and designing space to helparticulate their voices and being. This revised version was published online in June 2006 with corrections to the Cover Date.     

Quality of Life and Neurological Illness: A Review of the Literature

This article highlights some of the conceptual and methodological problems associated with quality of life (QoL) measurement in individuals with neurological illness. It is suggested that these problems have contributed to the underdeveloped status of QoL research in neurological settings. Many of the existing QoL measures that have been used, or show potential for use with individuals with neurological illness, are reviewed in terms of their theoretical basis, content, and practicality. A large proportion of these measures fail to meet adequate psychometric standards and/or have rarely been psychometrically tested. The confusion that surrounds adequate psychometric standards is discussed, and the dynamic nature of QoL is highlighted as a factor that requires further attention. Research addressing the discrepancies between proxy and patient ratings of patient QoL is also warranted. More focused research in these areas may contribute to a clearer understanding of how to assess QoL in individuals with neurological illness.