Scientific Understanding of Consciousness
Consciousness and Complexity
Science 4 December 1998: Vol. 282. no. 5395, pp. 1846 - 1851
Consciousness and Complexity
Giulio Tononi, Gerald M. Edelman
The Neurosciences Institute, 10640 John J. Hopkins Drive, San Diego, CA 92121, USA.
Consciousness requires two salient attributes -- conscious experience is integrated (each conscious scene is unified) and at the same time it is highly differentiated (within a short time, one can experience any of a huge number of different conscious states). We first consider neurobiological data indicating that neural processes associated with conscious experience are highly integrated and highly differentiated. We then provide tools for measuring integration (called functional clustering) and differentiation (called neural complexity) that are applicable to actual neural processes. This leads us to formulate operational criteria for determining whether the activity of a group of neurons contributes to conscious experience. These criteria are incorporated into the dynamic core hypothesis, a testable proposal concerning the neural substrate of conscious experience.
Integration is a property shared by every conscious experience irrespective of its specific content: Each conscious state comprises a single "scene" that cannot be decomposed into independent components.
The unity of conscious experience is also evidenced by our inability to perform multiple tasks, unless some tasks are highly automatic and impinge less on consciousness. Moreover, we cannot make more than a single conscious decision within an interval of a few hundreds of milliseconds, the so-called psychological refractory period. Furthermore, we cannot be aware of two incongruent scenes at the same time, as indicated by the bistability of ambiguous figures (e.g. optical illusions) and the phenomenon of perceptual rivalry. Unity also entails that conscious experience is private, that is, it is always experienced from a particular point of view and cannot fully be shared.
While each conscious state is an integrated whole, perhaps the most remarkable property of conscious experience is its extraordinary differentiation or complexity. The number of different conscious states that can be accessed over a short time is exceedingly large. For example, even if we just consider visual images, we can easily discriminate among innumerable scenes within a fraction of a second. More generally, the occurrence of a given conscious state implies an extremely rapid selection among a repertoire of possible conscious states that is, in fact, as large as one's experience and imagination. Differentiation among a repertoire of possibilities constitutes information, in the specific sense of reduction of uncertainty. Although this is often taken for granted, the occurrence of one particular conscious state over billions of others therefore constitutes a correspondingly large amount of information. Furthermore, it is information that makes a difference, in that it may lead to different consequences in terms of either thought or action.
Distributed neural activity, particularly in the thalamocortical system, is almost certainly essential for determining the contents of conscious experience. We suggested previously that a key neural mechanism underlying conscious experience are the reentrant interactions between posterior thalamocortical areas involved in perceptual categorization and anterior areas related to memory, value, and planning for action. Such interactions among neuronal groups in distributed brain areas may be necessary in order to generate a unified neural process corresponding to a multimodal conscious scene. Experimental findings are consistent with this hypothesis and suggest some generalizations about the neural processes that underlie conscious experience.
A change in the degree to which neural activity is distributed within the brain may accompany the transition between conscious, controlled performance and unconscious, automated performance. When tasks are novel, brain activation related to the task is widely distributed; when the task has become automatic, activation is more localized and may shift to a different set of areas. In animal studies, neural activity related to sensory stimuli can be recorded in many brain regions before habituation. After habituation sets in (a time when humans report that stimuli tend to fade from consciousness), the same stimuli evoke neural activity exclusively along their specific sensory pathways. These observations suggest that when tasks are automatic and require little or no conscious control, the spread of signals that influence the performance of a task involves a more restricted and dedicated set of circuits that become "functionally insulated." This produces a gain in speed and precision, but a loss in context-sensitivity, accessibility, and flexibility.
Substantial evidence indicates that the integration of distributed neuronal populations through reentrant interactions is required for conscious experience. Research studies indicate that various kinds of cognitive tasks are accompanied by the occurrence of short-term temporal correlations among distributed populations of neurons in the thalamocortical system. The magnetoencephalographic study of binocular rivalry indicates that awareness of a stimulus is associated with increased coherence among distant brain regions.
Evidence for a correlation between conscious experience and sustained neural activity also comes from tasks involving visuospatial working memory--the ability to rehearse or "keep in mind" a spatial location. Working memory is used to bring or keep some item in consciousness or close to conscious access. In working memory tasks, sustained neural activity is found in prefrontal cortex of monkeys, and it is apparently maintained by reentrant interactions between frontal and parietal regions. Sustained neural activity may facilitate the integration of the activity of spatially segregated brain regions into a coherent, multimodal neural process that is stable enough to permit decision-making and planning.
Functional Clustering (Integration)
We have suggested that a subset of distributed elements within a system gives rise to a single, integrated process if, at a given time scale, these elements interact much more strongly among themselves than with the rest of the system -- for example, if they form a functional cluster. This criterion has been formalized by introducing a direct measure of functional clustering.
We have applied these measures of functional clustering both to simulated datasets and to positron emission tomography (PET) data obtained from schizophrenic subjects performing cognitive task. It would appear that the rapid establishment of synchronous firing in the network among cortical regions and between cortex and thalamus should be considered as an indirect indicator of functional clustering, since it implies strong and fast neural interactions among the neural populations involved. The mechanisms of rapid functional clustering among distributed populations of neurons in the thalamocortical system have been studied with the help of large-scale computer simulations. These have shown that the emergence of high-frequency synchronous firing in the thalamocortical system depends critically on the dynamics of corticothalamic and corticocortical reentrant circuits and on the opening of voltage-dependent channels in the horizontal corticocortical connections.
Neural Complexity (Differentiation)
Measures of complexity, like measures of functional clustering, can also be applied to neurophysiological data to evaluate the degree to which a neural process is both integrated and differentiated. This opens the way to comparisons of the values of neural complexity in different cognitive and arousal states and to empirical tests of the relationships between brain complexity and conscious experience.
We propose that a large cluster of neuronal groups that together constitute, on a time scale of hundreds of milliseconds, a unified neural process of high complexity be termed the "dynamic core," in order to emphasize both its integration and its constantly changing activity patterns. The dynamic core is a functional cluster -- its participating neuronal groups are much more strongly interactive among themselves than with the rest of the brain. The dynamic core must also have high complexity -- its global activity patterns must be selected within less than a second out of a very large repertoire.
The dynamic core would typically include posterior corticothalamic regions involved in perceptual categorization interacting reentrantly with anterior regions involved in concept formation, value-related memory, and planning, although it would not necessarily be restricted to the thalamocortical system. The term "dynamic core" deliberately does not refer to a unique, invariant set of brain areas (be they prefrontal, extrastriate, or striate cortex), and the core may change in composition over time. Because our hypothesis highlights the role of the functional interactions among distributed groups of neurons rather than their local properties, the same group of neurons may at times be part of the dynamic core and underlie conscious experience, while at other times it may not be part of it and thus be involved in unconscious processes. Furthermore, since participation in the dynamic core depends on the rapidly shifting functional connectivity among groups of neurons rather than on anatomical proximity, the composition of the core can transcend traditional anatomical boundaries. Finally, as suggested by imaging studies, the exact composition of the core related to particular conscious states is expected to vary significantly across individuals.
A strong prediction based on our hypothesis is that the complexity of the dynamic core should correlate with the conscious state of the subject. For example, we predict that neural complexity should be much higher during waking and REM sleep than during the deep stages of slow-wave sleep, and that it should be extremely low during epileptic seizures despite the overall increase in brain activity. We also predict that neural processes underlying automatic behaviors, no matter how sophisticated, should have lower complexity than neural processes underlying consciously controlled behaviors. Finally, a systematic increase in the complexity of coherent neural processes is expected to accompany cognitive development.
The outcome of such tests should indicate whether conscious phenomenology can indeed be related, as we suggest, to a distributed neural process that is both highly integrated and highly differentiated. The evidence available so far supports the belief that a scientific explanation of consciousness is becoming increasingly feasible.
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