A dyadic model of consciousness

The proposed model for consciousness, called a dyadic model, is based upon reexamination of traditional thought structures in the light of modern experimental evidence from a number of scientific fields. It is an evolutionary cosmological model using energy and information as fundamental concepts. It proposes that the antecedent attributes of anthropic consciousness find their roots in the field of zero point quantum potential which gave rise to the Big Bang. In this model consciousness has both a fundamental aspect and an evolutionary aspect in the same sense that quantized energy manifests fundamentally as wave/particles and is observed in more complex form as molecular matter. Physical existence evolved through natural process into ever more complex organizations of matter; so also must anthropic consciousness have evolved from more fundamental antecedent characteristics. The dyadic model proposes a scenario for this evolution that corresponds to the appearance of the universe we seem to inhabit.     

A software agent model of consciousness.

Baars (1988, 1997) has proposed a psychological theory of consciousness, called global workspace theory. The present study describes a software agent implementation of that theory, called "Conscious" Mattie (CMattie). CMattie operates in a clerical domain from within a UNIX operating system, sending messages and interpreting messages in natural language that organize seminars at a university. CMattie fleshes out global workspace theory with a detailed computational model that integrates contemporary architectures in cognitive science and artificial intelligence. Baars (1997) lists the psychological "facts that any complete theory of consciousness must explain" in his appendix to In the Theater of Consciousness; global workspace theory was designed to explain these "facts." The present article discusses how the design of CMattie accounts for these facts and thereby the extent to which it implements global workspace theory.     

A global workspace model for phenomenal and access consciousness

Both the global workspace theory and Block’s distinction between phenomenal and access consciousness, are central in the current debates about consciousness and the neural correlates of consciousness. In this article, a unifying global workspace model for phenomenal and access consciousness is proposed. In the model, recurrent neural interactions take place in distinct yet interacting access and phenomenal brain loops. The effectiveness of feedback signaling onto sensory cortical maps is emphasized for the neural correlates of phenomenal consciousness. Two forms of top-down attention, attention for perception and attention for access, play differential roles for phenomenal and access consciousness. The model is implemented in a neural network form, with the simulation of single and multiple visual object processing, and of the attentional blink.     

Against the spotlight model of consciousness

The spotlight metaphor for the characterization of consciousness is examined. It is argued that, in effect, models based on this metaphor are not explanatory. In crucial respects, the models are not informative and they can account neither for the specific qualities of human consciousness nor for its function. In the spirit of pragmatic analyses in language, it is suggested that consciousness be viewed as an act. With this, a shift is made from a selectional to a generative perspective of consciousness. The proposal is grounded in a general critique of cognitive modelling in terms of underlying mental representations.     

A model of the hierarchy of behaviour, cognition, and consciousness

Processes comparable in important respects to those underlying human conscious and non-conscious processing can be identified in a range of species and it is argued that these reflect evolutionary precursors of the human processes. A distinction is drawn between two types of processing: (1) stimulus-based and (2) higher-order. For 'higher-order,' in humans the operations of processing are themselves associated with conscious awareness. Conscious awareness sets the context for stimulus-based processing and its end-point is accessible to conscious awareness. However, the mechanics of the translation between stimulus and response proceeds without conscious control. The paper argues that higher-order processing is an evolutionary addition to stimulus-based processing. The model's value is shown for gaining insight into a range of phenomena and their link with consciousness. These include brain damage, learning, memory, development, vision, emotion, motor control, reasoning, the voluntary versus involuntary debate, and mental disorder.     

A theory of consciousness

Consciousness is the subjective awareness of momentary experience interpreted in the context of personal memory and present state. I propose that two brain systems converge to create consciousness. The exogenous system sends information about multimodal external stimuli to basal dendrites in widely dispersed pyramidal cell ensembles in the cortex of the brain; this system produces synchronized fragments of sensations. The endogenous system distributes readout of representations of memories, activated in systems established by associative learning, diffusely to apical dendrites. Convergence of exogenous and endogenous inputs enhances cellular excitability, and fragmented sensations are thereby converted to fragments of perception. Local field potentials periodically modulate all cortical membrane potentials and facilitate synchronous discharge of these excited elements. Feedback between the cortex and the thalamus results in a cortico-thalamo-cortical reverberation, binding the fragments into a unified global percept. Sustained reverberation produces a resonating electromagnetic field of synchronized elements. The momentary content of information in the brain is this distributed coherence, which is negative entropy. Consciousness is a physical property of this field, producing the subjective awareness of this information.     

Stained glass as a model for consciousness

Contemporary phenomenal externalists are motivated to a large extent by the transparency of experience and by the related doctrine of representationalism. On their own, however, transparency and representationalism do not suffice to establish externalism. Hence we should hesitate to dismiss phenomenal internalism, a view shared by many generations of competent philosophers. Rather, we should keep both our options open, internalism and externalism. It is hard, however, to see how to keep open the internalist option, for although transparency and representationalism have not yet definitively established externalism, they have indeed made it quite intuitive. Internalism, by comparison, comes across at first sight as antiquated and ridden with difficulties. This is why I propose the Stained Glass model of consciousness. I do so with the following two aims: first, to make internalism intuitive in the age of transparency, and second, to show how to resist the many recent anti-internalist arguments. In particular, I argue that phenomenal internalism need not be epistemically worrisome, that it is compatible at once with transparency, representationalism, and content externalism, and that although it requires an error theory, this error theory is a harmless one.     

A general latent trait model for response processes

The purpose of the current paper is to propose a general multicomponent latent trait model (GLTM) for response processes. The proposed model combines the linear logistic latent trait (LLTM) with the multicomponent latent trait model (MLTM). As with both LLTM and MLTM, the general multicomponent latent trait model can be used to (1) test hypotheses about the theoretical variables that underlie response difficulty and (2) estimate parameters that describe test items by basic substantive properties. However, GLTM contains both component outcomes and complexity factors in a single model and may be applied to data that neither LLTM nor MLTM can handle. Joint maximum likelihood estimators are presented for the parameters of GLTM and an application to cognitive test items is described.This research was partially supported by the National Institute of Education grant number NIE-6-7-0156 to Susan Embretson (Whitely), principal investigator. However the optinions expressed herein do not necessarily reflect the position or policy of the National Institute of Education, and no official endorsement by the National Institute of Education should be inferred.     

A general model for the analysis of multilevel data

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of the model include repeated measures designs, multiple matrix samples, multilevel latent variable models, multiple time series, and variance and covariance component models.We would like to acknowledge the helpful comments of Ruth Silver. We also wish to thank the referees for helping to clarify the paper. This work was partly carried out with research funds provided by the Economic and Social Research Council (U.K.).     

A general parametric model of similarity and subjective difference

The field of multidimensional scaling is dominated by models that lack inherent parameters. Correcting parameters have been introduced, e.g. INDSCAL, to increase power of prediction. Although a nonparametirc model with correcting parameters may exhibit a very good fit to data, a parametric model is intrinsically superior. The general parametric model proposed here yields measures of both absolute and relative subjective differences (dissimilarity) in addition to similarity. It is basically unidimensioanal. Rules for combining values of attributes into a single multidimensional value may be applied either to the input or to the output of the model. One of the resulting functions is a generalization of the Eisler-Ekman similarity function. A special case of another function is identical to the Minkowski class of distance functions (including INDSCAL). The model is not limited to pairwise relations. It yields unitary measures for any number of objects.     

Perception: a model comprising two modes of consciousness

A model of human perception is proposed in which conscious awareness is assumed to be the result of two separate mechanisms each involving consciousness, one outer, sensori-produced, and one inner, conceptual. By mediation of flexible memory representations the sensory data of the outer consciousness give rise to a matched "copy" in the inner consciousness which conceptually organizes the former and also serves as input to the memory store. The model is applied to some perceptual problems in vision such as ambiguous stimuli, subjective contours, space perception, a case of metacontrast, and subliminal perception.     

A field theory of consciousness.

This article summarizes a variety of current as well as previous research in support of a new theory of consciousness. Evidence has been steadily accumulating that information about a stimulus complex is distributed to many neuronal populations dispersed throughout the brain and is represented by the departure from randomness of the temporal pattern of neural discharges within these large ensembles. Zero phase lag synchronization occurs between discharges of neurons in different brain regions and is enhanced by presentation of stimuli. This evidence further suggests that spatiotemporal patterns of coherence, which have been identified by spatial principal component analysis, may encode a multidimensional representation of a present or past event. How such distributed information is integrated into a holistic precept constitutes the binding problem. How a precept defined by a spatial distribution of nonrandomness can be subjectively experienced constitutes the problem of consciousness. Explanations based on a discrete connectionistic network cannot be reconciled with the relevant facts. Evidence is presented herein of invariant features of brain electrical activity found to change reversibly with loss and return of consciousness in a study of 176 patients anesthetized during surgical procedures. A review of relevant research areas, as well as the anesthesia data, leads to a postulation that consciousness is a property of quantum-like processes, within a brain field resonating within a core of structures, which may be the neural substrate of consciousness. This core includes regions of the prefrontal cortex, the frontal cortex, the pre- and paracentral cortex, thalamus, limbic system, and basal ganglia.     

A role for the anterior insular cortex in the global neuronal workspace model of consciousness

According to the global neuronal workspace model of consciousness, consciousness results from the global broadcast of information throughout the brain. The global neuronal workspace is mainly constituted by a fronto-parietal network. The anterior insular cortex is part of this global neuronal workspace, but the function of this region has not yet been defined within the global neuronal workspace model of consciousness. In this review, I hypothesize that the anterior insular cortex implements a cross-modal priority map, the function of which is to determine priorities for the processing of information and subsequent entrance in the global neuronal workspace.     

A general diagnostic model applied to language testing data

Probabilistic models with one or more latent variables are designed to report on a corresponding number of skills or cognitive attributes. Multidimensional skill profiles offer additional information beyond what a single test score can provide, if the reported skills can be identified and distinguished reliably. Many recent approaches to skill profile models are limited to dichotomous data and have made use of computationally intensive estimation methods such as Markov chain Monte Carlo, since standard maximum likelihood (ML) estimation techniques were deemed infeasible. This paper presents a general diagnostic model (GDM) that can be estimated with standard ML techniques and applies to polytomous response variables as well as to skills with two or more proficiency levels. The paper uses one member of a larger class of diagnostic models, a compensatory diagnostic model for dichotomous and partial credit data. Many well‐known models, such as univariate and multivariate versions of the Rasch model and the two‐parameter logistic item response theory model, the generalized partial credit model, as well as a variety of skill profile models, are special cases of this GDM. In addition to an introduction to this model, the paper presents a parameter recovery study using simulated data and an application to real data from the field test for TOEFL^® Internet‐based testing.