Narrow Results

Using data from 52 countries in Wave 5 of the World Value Survey conducted during 2004 and 2008, we test two alternative approaches in spirituality measures. The first is based on the more traditional understanding that spirituality is associated with meanings, God, prayers/meditation and formal religions. The second is based on the common spiritual teachings of all the major religions that are summarized by the LIFE (Love, Insight or Wisdom, Fortitude and Engagement) framework proposed by Ho, LS (2014). Psychology and Economics of Happiness: Love, Life and Positive Living. Oxford: Routledge. It was found that this alternative approach, which focuses on the spiritual teachings rather than theology, offers better explanatory power for Total Life Satisfaction (TLS). An interesting finding is that under this specification the coefficient on meaning turned negative, suggesting that those whose lives are weak in Love, Insight, Fortitude and Engagement have yet to find meaning and are less happy. Through a series of stepwise regressions, we conclude that more spiritual people are indeed happier (H1); that religious people indeed tend to be happier mainly because religious people tend to be more spiritual (H2); that the essence of spirituality and meaning lies in Love, Insight, Fortitude and Engagement (H3). These virtues are all grounded on transcending the narrow self and on a “reverence for Life” as propounded by Schweitzer. Finally, religious attendance does promote happiness. The effect is small but stable and statistically significant. It may have to do with the social network this offers (H4).

The aim of this tutorial is to document a novel approach to brain function, in which the key to understanding is the capacity of brains for self-organization. The property that distinguishes animals from plants is the capacity for directed movement through the environment, which requires an organ capable of organizing information about the environment and predicting the consequences of self-initiated actions. The operations of predicting, planning acting, detecting, and learning comprise the process of intentionality by which brains construct meaning. The currency of brains is primarily meaning and only secondarily information. The information processing metaphor has dominated neurocognitive research for half a century. Brains certainly process information for input and output. They pre-process sensory stimuli before constructing meaning, and they post-process cognitive read-out to control appropriate action and express meaning. Neurobiologists have thoroughly documented sensory information processing bottom-up, and neuropsychologists have analyzed the later stages of cognition top-down, as they are expressed in behavior. However, a grasp of the intervening process of perception, in which meaning forms, requires detailed analysis and modeling of neural activity that is observed in brains during meaningful behavior of humans and other animals. Unlike computers, brains function hierarchically. Sensory and motor information is inferred from pulses of microscopic axons. Meaning is inferred from local mean fields of dendrites in mesoscopic and macroscopic populations. This tutorial is aimed to introduce engineers to an experimental basis for a theory of meaning, in terms of the nonlinear dynamics of the mass actions of large neural populations that construct meaning. The focus is on the higher frequency ranges of cortical oscillations. Part I introduces background on information, meaning and oscillatory activity (EEG). Part II details the properties of wave packets. Part III describes the covariance structure of the oscillations. Part IV addresses the amplitude modulations, and Part V deals with the phase modulations. The significance of a theory of meaning lies in applications using population neurodynamics, to open new approaches for treatment of clinical brain disorders, and to devise new machines with capacities for autonomy and intelligence that might approach those of simpler free-living animals.

Sensing and perceiving involve enormous numbers of widely distributed dendritic and action potentials in cortex, before, during and after stimulus arrival but with differing spatiotemporal patterns. Stimulus-activated receptors drive cortical neurons directly (olfactory) or indirectly through thalamocortical relays. The driven activity induces hemisphere-wide, self-organized patterns of neural activity called wave packets. Three levels of brain function are hypothesized to mediate transition from sensation and perception. Microscopic activity expressed by action potentials is sensory. Macroscopic activity of the whole forebrain expressed by behavior is perceptual. Mesoscopic activity bridges the gap by the formation of wave packets. They form when sensory input destabilizes the primary receiving areas by local state transitions. The sensory-driven action potentials condense into mesoscopic wave packets like molecules forming raindrops from vapor. The condensation disks sustain 2D spatial patterns of phase and amplitude of carrier waves in the beta and gamma EEG. The AM patterns correlate not with features but with the context and value of sensory stimuli for the subjects, in a word, their meaning. The wave packets from all sensory areas are broadly transmitted through the forebrain. They induce the formation of macroscopic patterns that coalesce like scintillating pools over much and perhaps all of each hemisphere. The prediction is made for clinical testing that wave packets are precursor to states of awareness. They are not by themselves accessible to experience, as may be the macroscopic states initiated by global state transitions.

This paper argues that mechanisms underlying consciousness and qualia are likely to arise from the information processing that takes place within the detailed micro-structure of the cerebral cortex. It looks at two key issues: how any information processing system can recognize its own activity; and secondly, how this behavior could lead to the subjective experience of qualia. In particular, it explores the pattern processing capabilities of attractor networks, and the way that they can attribute meaning to their input patterns and goes on to show how these capabilities can lead to self-recognition. The paper suggests that although feedforward processing of information can be effective without attractor behavior, when such behavior is initiated, it would lead to self-recognition in those networks involved. It also argues that attentional mechanisms are likely to play a key role in enabling attractor behavior to take place. The paper explores the ability of attractor networks to generate representations of the meaning they assign to input patterns. It goes on to show how the way that they interpret representations of their own activity could give rise to qualia. The paper includes an examination of some limited neurobiological evidence that supports the theory outlined.

The literature on organizational learning and knowledge management over the last decade has been extensive and far-reaching. This paper analyses the proliferation of these concepts along different disciplinary perspectives by tracing their evolution. The analysis reveals that organizational learning is a diffused; ill-defined concept with little consideration made for its practical application. Knowledge management on the other hand is a medley of different approaches but lack a unifying vision. As a result, it is difficult to establish synergistic relationship between these two concepts. An attempt is made to show how knowledge management models may be used to facilitate organizational learning. The models discussed include the Intellectual Capital Model, the Socially Constructed Model and the Knowledge Category Model. Each of these models is assessed in order to determine how they contribute towards the practical realization of organizational learning. The review shows that although these models differ in terms of how knowledge is perceived and in terms of the dynamics of learning involved, each of them has the capacity to contribute in unique ways towards organizational learning. The paper proposes that attempts to seek synergistic relationships that link organizational learning and knowledge management should be examined more closely to facilitate the practical realization of organizational learning.

Phenomenal states are generally considered the ultimate sources of intrinsic motivation for autonomous biological agents. In this article, we will address the issue of the necessity of exploiting these states for the design and implementation of robust goal-directed artificial systems. We will provide an analysis of consciousness in terms of a precise definition of how an agent "understands" the informational flows entering the agent and its very own action possibilities. This abstract model of consciousness and understanding will be based in the analysis and evaluation of phenomenal states along potential future trajectories in the state space of the agents. This implies that a potential strategy to follow in order to build autonomous but still customer-useful systems is to embed them with the particular, ad hoc phenomenality that captures the system-external requirements that define the system usefulness from a customer-based, requirements-strict engineering viewpoint.

I propose a physicalist theory of consciousness that is an extension of the theory of noémona species. The proposed theory covers the full consciousness spectrum from animal to machine and its human consciousness base is compatible with the corresponding work of Wundt, James, and Freud. The paper is organized in three sections. In the first, I briefly justify the methodology used. In Sec. 2, I state the inadequacies of the major work on the nature of consciousness and present a definitional system that adequately describes its changing nature and scope. Finally in Sec. 3, I state some of the consequences of the theory and introduce some of its future extensions.

Interdisciplinary inquiry presupposes an open worldview to enable the researcher to transcend the confinements of a specific discipline in order to become aware of aspects that are necessary to satisfyingly solve a problem. Radical constructivism offers a way of engineering such interdisciplinarity that goes beyond mere multi or pluridisciplinary approaches. In this paper I describe epistemological and methodological aspects of interdisciplinarity, discuss typical problems it faces, and carve out its relationship with knowledge and communication from a constructivist perspective. Five implications for interdisciplinary practice and science education conclude the paper.

This paper describe CAILS, an experimental Computer Assisted Iconic Language System which deals with three specific areas in communication: Cross-linguistic, visual/spatial concept representation and visual educational technique. Designed for interpersonal communication, this system functions with generally comprehensible visual referents. Each area has some specific syntax which is clear and easy to learn. CAILS takes advantage of visual memory of the user. The basic idea of this system is that an individual can, using basic visual references, compose a message, represent a concept and teach a rule. The specificity of the images eliminates ambiguity. For convenience, we classified visual references or «words» in the following categories: Hands, Movements, Expression, and Pictures.

When properly use, CAILS produce « iconic message objects» which may be presented to the intended recipient

That branch of semiotics called semantics deals with the relationships between meanings and representations. In my view meanings exist only in brains, which have no representations in them. A meaning is the focus of an activity pattern that may occupy the entire available brain. It is constructed by intentional action, that is followed by learning from the consequences of that action. Communication between brains requires that meanings be represented by construction of words, gestures, symbolse, etc., which elicit meanings in other brains. A representation, is a material object or process, that has no meaning in itself. EEG data indicate that meaning is carried by spatiotemporal patterns of neural activity in frames like a motion picture. The discrete steps occur by cortical phase transitions in the 2-D arrays of neurons interacting synaptically to form wave packets. The rapid exchanges of discrete wave packets between interactive cortical domains generate self-organized dynamics controlling behavior including making representations of meaning. The dynamics of neural arrays is described by sets of differential equations, leading to the possibility of constructing intelligent machines that have the capacity to generate and represent meanings that are comparable to those existing in small animals in machines currently under study in situated robotics.