Theories and measures of consciousness: An extended framework

Abstract

A recent theoretical emphasis on complex interactions within neural systems underlying consciousness has been accompanied by proposals for the quantitative characterization of these interactions. In this article, we distinguish key aspects of consciousness that are amenable to quantitative measurement from those that are not. We carry out a formal analysis of the strengths and limitations of three quantitative measures of dynamical complexity in the neural systems underlying consciousness: neural complexity, information integration, and causal density. We find that no single measure fully captures the multidimensional complexity of these systems, and all of these measures have practical limitations. Our analysis suggests guidelines for the specification of alternative measures which, in combination, may improve the quantitative characterization of conscious neural systems. Given that some aspects of consciousness are likely to resist quantification altogether, we conclude that a satisfactory theory is likely to be one that combines both qualitative and quantitative elements.complexity ͉ differentiation ͉ dynamic core ͉ integration

Distributed Computation using Evolutionary Consciousness : An Approach

“…It is illustrated that, neurological phenomena of consciousness and experiences are not instantaneous neuronal activities and, the retention (memory) has a role into it [3]. Furthermore, the expression of experiences has a computational basis, which can be modeled by mathematical frameworks [17,19,20].…”

Causal networks in simulated neural systems

“…As we have argued previously (Seth et al 2006), a useful quantitative measure of relevant complexity should also reflect the fact that consciousness is a dynamic process (James 1904), and not a thing or a capacity; it should also take account of causal interactions within a neural system and between a neural system and its surroundings, i.e., bodies and environments. To be practically applicable, a useful measure should also be calculable for systems composed of large numbers of interacting elements.…”

“…A fundamental property of conscious scenes is that they are both differentiated (reflecting the discriminatory capability of consciousness; every conscious scene is one among a vast repertoire of different possible scenes) and integrated (reflecting the unity of conscious experience; every conscious scene is experienced ''all of a piece'') (Edelman 2003;Edelman and Tononi 2000;Tononi and Edelman 1998). Therefore, a useful measure of complex neural interactions relevant to consciousness should reflect a balance between integration and differentiation in neural dynamics; this balance can be referred to as the relevant complexity of the system (Seth et al 2006). Searching for neural dynamics of high relevant complexity marks an important departure from standard approaches seeking to isolate anatomical correlates of conscious experience (Rees et al 2002).…”

“…In this paper, time-series analysis techniques based on Granger causality (Granger 1969) are combined with network theory in order to characterize causal networks in simulated neural systems Krichmar et al 2005b;Seth and Edelman 2007;Seth et al 2006). Because our aim is to elaborate a particular theoretical perspective on neural dynamics, and not to analyze existing empirical data, we illustrate causal network analysis using concrete simulation models.…”

“…Quantitative multivariate measures that track these properties and that are applicable to neural dynamics are therefore very useful for neural theories of consciousness, and indeed several such measures have now been proposed (Edelman 2003;Tononi and Edelman 1998;Tononi 2004). Here, we adopt a causal network perspective on the neural dynamics underlying consciousness and we analyze the proposal that a particular measure applicable to causal networks, causal density, provides a useful means of quantifying the complexity of the neural dynamics relevant to consciousness (Seth et al 2006). Causal density is argued to overcome certain limitations associated with alternative measures by (i) naturally incorporating causal interactions and (ii) providing a genuine measure of process rather than capacity.…”