Theories and models of consciousness in neuroscience.

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1 Theories and models of consciousness in neuroscience. Abstract: Over the past decade, neuroscientific studies of consciousness have produced a rapidly growing body of experimental data. Theories and models of consciousness are required to account for this data and to provide explanatory links between features of neuronal activity and features of conscious experience. Here, we outline several major properties of consciousness that require explanation and, in this light, we review current theoretical approaches and modeling strategies. Keywords: global workspace, neural Darwinism, thalamocortical system, dynamic core, frontoparietal system, neural fields, explanatory correlate. Introduction There are only a few basic ways to study conscious brain events. One is to compare conscious and unconscious states, and here we can look to classical experiments on brainstem and basal nuclei like the reticular formation, zona incerta, and parts of the thalamus. There is a large literature on neuromodulation and cortical activation in waking, sleep, REM dreams, coma-like states, general anesthesia, and so on. Conscious contents are commonly indexed by voluntary reports in humans, and by accurate match-to-sample responses in related species. In humans the simplest approach is to ask about easily described events, like a visual coffee cup or a mental image of a rotating kitchen chair. Conscious contents like this can be reported with good accuracy under the proper conditions, such as immediate report, freedom from distraction, and independent verification. These conditions apply to numerous standard experiments. Traditional sensory science compares conscious stimuli to each other, a method that dates back to Newton's prism experiments. However, in a recent wave of research conscious events are compared to unconscious events which appear to be physiologically similar --- though they cannot be reported as conscious even under optimal conditions. Many experimental paradigms allow for such comparisons. Such experiments may be said to isolate the issue of conscious content as such, since they treat it as an experimental variable. A classic example is binocular rivalry, in which two different images are shown simultaneously to the two eyes, and conscious perception alternates between them. As long as the input to the two eyes cannot be integrated into a single, coherent visual event, they will compete against each other for conscious dominance. The brain differences between conscious perception and unconscious input registration provides evidence about visual consciousness as such. With the advent of this burgeoning new literature, the idea that conscious cognition cannot be studied scientifically has largely faded. Instead there is now a growing need for theoretical frameworks and neuroscience-based models able to integrate the rapidly

2 growing body of experimental data. Theories and models of consciousness are also needed to guide future experiments and to make testable predictions. Major features. Much of the evidence to be accounted for is summarized in Table 1 and below. Needless to say, this is an open set that will expand as we learn more. Conscious events recruit widespread brain activity. Many studies suggest that conscious contents mobilize frontal and parietal brain regions, even if they originate posteriorly, as in the case of sensory events. For example,. This is in fact predicted by several current theories (See below) An alternative hypothesis is that, since any conscious event is presumed to be reportable, the prefrontal-parietal component may be related to readiness to report rather than conscious experience as such. This is a difficult alternative to test conclusively. As we gain more detailed understanding of specific functional regions of the cortex, it may become possible to do so. Many sources of evidence suggest that conscious events show wider cortical activity than matched comparison cases. A major theoretical question, therefore, concerns the contribution of frontoparietal activity in conscious perception. Integration and dissemination of focal information. Visual cortex is clearly involved in the integration of relatively simple visual features into objects and scenes. Since humans in a natural environment are conscious of objects and scenes rather than isolated features (such as contrast or spatial frequency), it is plausible to think that there is a close link between these end-products of visual integration and visual consciousness. N. Logothetis and colleagues recorded single-cell activity in response to different kinds of binocular rivalry from area V1/V2, MT/MST (motion), V4 (color), and finally IT/STS, the object perception region of the macaque temporal lobe. Only 20 to 40% of neurons sampled in early visual regions responded to the reported (conscious) percept, with a roughly equal number responding to the unreported percept. However, in the temporal lobe, 90% of the neurons responded only to the conscious percept. Since object recognition combines many basic features like color, motion and location into a single percept, this region is a plausible place for conscious objects to arise. One hypothesis is that visual integration settles into a stable equilibrium when object and scenes are identified, and is then disseminated to other parts of the brain.

3 In this view conscious contents would represent the result of integration, but the integrated percept would then interact with numerous regions elsewhere in the brain. V. Lamme and others have raised the question whether visual consciousness can be said to arise from visual cortex alone, or whether it requires selective attention and other top-down influences from other regions. For example, dorsal visual stream activity is widely considered to be unconscious in itself, but may provide allocentric and egocentric spatial maps for objects defined in the ventral visual stream. Thus dorsal parietal regions may be contextual with respect to conscious object and scene perception. Parietal maps may be necessary for conscious visual perception, but they may not enter into experience directly. Lamme has proposed that reentrant activity in visual cortex may be the key to visual consciousness. Cortical reentry, like other forms of reentry, will have the effect of stabilizing and strengthening activity in particular neural pathways. F. Crick and C. Koch have suggested that consciousness may require competition among coalitions of neurons, in which winning coalitions determine the contents of consciousness at a given time. Such neuronal coalitions bear similarities to Hebbian cell assemblies on a very large and dynamic scale. They also suggest that unconscious processing may consist largely of feed-forward cortical waves, whereas consciousness may involve standing waves created by bi-directional signal propagation. Direct brain imaging and electrophysiological evidence for such phenomena has been found by W. Freeman, R. Srinivasan, P. Nunez, and others. A distinctive aspect of Crick and Koch s view is the reintroduction of the controversial notion of an inner homunculus. They suggest that conscious perception may involve anterior brain regions looking at posterior activity. Fast cortical interactions (gamma - theta). A conscious task, like pointing to a coffee cup, involves a great many cortical and subcortical regions. A range of evidence and theory suggests that synchronized or coherent firing of large numbers of neurons plays a part. Gamma and alpha-range synchrony have been implicated in the conscious interpretation of ambiguous stimuli. However, coherent oscillatory activity of large populations of cells has been observed in other tasks as well, including theta activity in episodic retrieval (involving MTL and higher cortex), mu oscillations in motor regions, during prepration of motor acts (?) and possibly 10 Hz coherence during endogenous mentation in posterior cortex. These circa 10 Hz oscillations may serve to group faster rhythms like beta and gamma. Only delta oscillations (<2.5 Hz) are generally associated with unconscious states. Several studies suggest that synchronous activity during conscious perception is widely distributed in the brain. However, not all brain areas are firing in synchrony --- if they did, the result might resemble an epileptic seizure, where one single rhythm entrains large regions of cortex. Rather, there seem to be rapid moments of synchrony between

4 different brain regions that need to work together to accomplish the task of conscious perception. In deep sleep and other unconscious conditions, the individual firing rate of cortical neurons may not change, but rapid interactive linking between regions seems to be disrupted. Current theories and models. Consciousness is often viewed as an architectural aspect of the nervous system, since it is involved with a variety of specific contents and functions. Such architectures can be expressed neurocomputationally, or in terms of symbolic computations. Neural network mathematics has broad flexibility and explanatory potential. Formally, even symbolic information processing models are expressible in neurocomputational terms. In practice, however, it is a challenge to scale neural networks up to the massive complexity of real brains. Qualitative and hybrid architectures. B. Baars has summarized a body of cognitive and brain evidence for a globalist view of conscious cognition, interpreted in a theoretical framework called global workspace theory (GWT). GWT assumes the existence of multiple "knowledge sources" that work to identify vague, ambiguous or unpredictable stimuli, in a cooperative and competitive fashion, by posting hypotheses about the identity of the input in a common memory space, a "blackboard" or "global workspace." This approach was surprisingly successful when first applied in the 1970s to the difficult problem of computer-based speech recognition in a noisy acoustical environment. Today, global workspace architectures are widely used in robotics and artificial intelligence. They can be implemented in both symbolic and neural network formats. S. Dehaene, J.-P. Changeaux and colleagues have proposed a neuronal implementation of a global workspace architecture, the so-called neuronal global workspace. In this model, sensory stimuli mobilize excitatory neurons with long-range cortico-cortical axons, leading to the genesis of a global activity pattern among workspace neurons. Any such global pattern can inhibit alternative activity patterns among workspace neurons, thus preventing the conscious processing of alternative stimuli. The global neuronal workspace model predicts that conscious presence is a nonlinear function of stimulus salience; i.e., a gradual increase in stimulus visibility should be accompanied by a sudden transition of the neuronal workspace into a corresponding activity pattern. Such ignition of the workspace is consistent with the notion that consciousness is an all-or-none, as opposed to a graded process. The neuronal global workspace has been used to model a number of experimental phenomena in consciousness. In one study, spontaneous workspace activity blocked external sensory processing, in a manner analogous to inattentional blindness in which

5 normal subjects engaged in effortful mental activity fail to notice salient but irrelevant stimuli. A similar phenomenon is the attentional blink, where subjects are unable to report a stimulus S2 presented within a short time interval ( ms) following presentation of a first stimulus S1. In a neuronal global workspace model of this paradigm, presentation of S1 evoked both bottom-up and top-down signal flow, leading to the sort of sustained activity that might underlie conscious reportability in human subjects. However, presentation of S2 immediately after S1 evoked only bottom-up activity because of the global activity constraints imposed by S1. Plausibly, this absence of sustained activity relating to S2 corresponds to the lack of conscious access apparent in the attentional blink. An advantage of the GWT approach is that it is clearly functional: That is, it describes a known architecture for integrating and distributing focal information in a large-scale, massively parallel, brain-like organ. However, scaling neural net architectures up to the level of real brains present major challenges. Current neuronal GWT models make use of simplified network architectures where entire brain areas are represented by single neurons. Several other groups are currently applying GW-based ideas to the question of conscious cognition. In another approach to circumventing the difficulty of capturing high-level and low-level features in a single model, S. Franklin and colleagues,have made use of a hybrid computational approach, combining in their LIDA model several types of existing computational methods using a quasi-neuronal activation-passing design. Highlevel conceptual models such as LIDA can provide insights into the processes implemented by the neural mechanisms underlying consciousness, without necessarily specifying the mechanisms themselves While it is difficult to derive experimentally testable predictions from large-scale architectures, this hybrid architecture approach is broadly consistent with the major empirical features discussed in this article. It predicts, for example, that consciousness may play a central role in the classical notion of cognitive working memory, selective attention, learning and retrieval. A variety of neurocomputational models have been proposed to deal with aspects of consciousness. Some of these make global or qualitative claims, while others make quite specific experimental predictions. Dynamical and field theories Several theorists have advanced nonlinear dynamical theories that are thought to be able to model larger-scale neuronal architectures and time-locked non-local effects among brain regions.. Some have used formalisms borrowed from physics, such as electromagnetic fields, wave mechanisms (W. Freeman, P. Nunez, R. Srinivasan, R. Wallace), and even quantum mechanics (K. Pribram, S. Hameroff,;). The notion that quantum effects play a critical role in consciousness has been widely criticized on the

6 grounds that significant quantum effects seem unlikely in neurophysiological environments, unless body-temperature versions can be found. E. R. John has proposed that consciousness emerges from global negative entropy which is a property of an electrical field resonating in a critical mass of brain regions. John suggests that sensory stimuli can evoke localized synchronous neuronal activation causing deviations from a baseline brain state of maximum entropy. These deviations establish spatially distributed islands of local negative entropy which correspond to fragments of sensation. These fragments can be bound into a unified global percept via integrative interactions within thalamocortical loops. In similar vein, P. Nunez and R. Srinivisan have described large-scale cortical dynamics in terms of synaptic action fields which are continuous mathematical functions representing the density of active synapses. According to these authors, consciousness depends on resonance in these fields within characteristic frequency ranges (e.g., 4-15 Hz). Neural field theories may account well for the unified nature of conscious experience, as well as for associated properties including seriality and metastability. Although they have inspired empirical work using quantitative EEG, detailed modeling of the generation of conscious neural fields is still needed. Neural Darwinism Because the thalamocortical (T-C) complex has extraordinarily interactive connectivity compared other brain structures, like the cerebellum for example, it is attractive to suppose that the different modalities of consciousness make use of the T-C complex as a common anatomical substrate. This is not to deny the need for contentspecific cortical regions. An extensively developed T-C theory of consciousness is provided by G. Edelman s Neural Darwinism (ND). ND suggests that brain development and dynamics are selectionist in nature, not instructionist, in contrast to computers, which carry out explicit symbolic instructions. Selectionist processes have four features: 1. A set of elements are characterized by diversity, such as populations of neuronal groups in the brain. 2. These elements can reproduce or amplify. 3. A process of selection operates on the products of diversity. For example, differential reproductive success serves to select some offspring and not others. 4. Finally, inherent in such systems is degeneracy, the ability of different combinations of elements to perform the same function.

7 In the brain, selectionism applies both to neural development and to moment-tomoment functioning. Selection in the brain is directed by value, which reflects the salience of an event and can be positive or negative. Value is analogous to selective pressure in evolution; the magnitude of a value signal corresponds to fitness. In the brain it involves pleasure, pain, and emotional salience networks. Finally, according to ND, spatiotemporal coordination in the brain is enabled by reentry: the recursive exchange of signals among neural areas across massively parallel reciprocal connections. According to ND, the neural systems underlying consciousness arose in evolution as a result of their ability to integrate a large number of sensory inputs and motor outputs occurring in parallel. This integration allows the discrimination of sensorimotor signals in a high dimensional space yielding adaptive behavior. Primary (sensory) consciousness is associated with an interaction between perceptual categorization and memory, where primary consciousness refers to the presence of a multimodal scene of perceptual and motor events (William James specious present, or Edelman s remembered present ). Primary consciousness, by this view, provides an animal with increased discriminatory selectivity, flexibility, and planning capacity when responding to complex environments, as compared to its preconscious ancestors. Edelman, along with many other theorists, distinguishes between primary consciousness and higher-order consciousness which in humans is associated with verbal reportability, the attribution of sensory scenes to a temporally stable self, and the ability to project conscious experiences both forward and backward in time. Recent extensions of ND have focused on informational aspects of consciousness. In the dynamic core hypothesis (DCH), Edelman and colleagues suggest that the occurrence of any particular conscious scene constitutes a highly informative discrimination, for the reason that conscious scenes are at once integrated (every conscious scene is experienced all of a piece ) and differentiated (every conscious scene is distinct from others). The DCH claims that conscious qualia are these discriminations, and that the neural mechanisms underlying consciousness consist of a functional cluster in the thalamocortical system, within which reentrant neuronal interactions yield a succession of differentiated yet unitary metastable states. The boundaries of the dynamic core are suggested to shift over time, with some neuronal groups leaving and others being incorporated, these transitions occurring under the influence of internal and external signals. According to the DCH, high values of a measure called neural complexity accompany consciousness. Neural complexity measures the extent to which the dynamics of a neural system are both integrated and differentiated. The component parts of a neurally complex system are differentiated; however, as larger and larger subsets of elements are considered they become increasingly integrated. Modelling studies suggest that the distinctive reentrant anatomy of the thalamocortical system is ideally suited to producing dynamics of high neural complexity.

8 Similar to the DCH, and proposed by one of its originators (G. Tononi), the information integration theory of consciousness (IITC) claims that consciousness corresponds to the capacity of a system to integrate information. A system is deemed capable of information integration to the extent that it has available a large repertoire of states and that the states of each element are causally dependent on the states of other elements. Like the DCH, the IITC is based on the notion that the occurrence of any conscious scene simultaneously rules out the occurrence of a vast number of alternatives and therefore constitutes a highly informative discrimination. Also like the DCH, the IITC proposes that the thalamocortical system provides the neuroanatomical substrate for the neural processes that underlie consciousness. The IITC proposes a novel measure of the quantity of consciousness generated by a system. This measure, Φ, is defined as the amount of causally effective information that can be integrated across the weakest link of a system. Unlike neural complexity, Φ measures directed, causal interactions within a system. According to the IITC, consciousness as measured by Φ is characterized as a disposition or potentiality. The contents of any given conscious scene are specified by the value, at any given time, of the variables mediating informational interactions within the system. A distinguishing feature of the IITC is that Φ is proposed to be a sufficient condition for consciousness, so that any system with sufficiently high Φ would be conscious. Both neural complexity and Φ can be considered to measure the spatial aspect of the balance between differentiation and integration in the dynamics of a neural system. A. Seth and colleagues have suggested that useful additional measures of neural dynamics will measure other aspects of this balance, including complexity across time (temporal complexity) and across different levels of description (recursive complexity). Temporal complexity reflects the fact that consciousness extends over time in several ways. Many experiments suggest that it takes ~100ms for sensor stimuli to be incorporated into a conscious scene, and neuronal readiness potentials can appear several hundred ms before awareness of an intention to act. Recursive complexity reflects the fact that brains show rich organization at multiple levels, from molecular interactions within individual synapses to reentrant interactions among distinct brain regions. The phenomenal structure of consciousness also appears to be recursive; individual features of conscious scenes are Gestalts and share organizational features with the conscious scene as a whole. Therefore, a combination of distinct measures may be needed to adequately characterize the neural dynamics relevant to consciousness. Other approaches Since theoretical approaches to consciousness remain in their infancy, there is a large and growing body of alternative approaches which we are unable to describe fully here. These include the Interplay of conscious experience with attention, immediate memory, and other functions. Conscious experience, working memory, selective attention, and input to an executive self.

9 Other approaches have focused on neuroanatomical constraints. S. Zeki has proposed that consciousness arises at a micro features from local cortical circuits. This micro-consciousness view is at variance with much of the evidence summarized above and is a minority position. B. Merker challenges the dominant view that conscious contents depend on cortex and has proposed an alternative model in which basic consciousness arises from the activity of subcortical areas including thalamus, superior colliculus, and the zona incerta. Summary What should we expect from a neural theory of consciousness? One thing we shouldn t ask for is that the theory produce consciousness: after all, a theory of hurricanes is not itself windy. Rather, a useful theory should help move from establishing correlations between brain activity and conscious experience towards developing explanations that link features of brain activity with features of conscious phenomenology, as well as accounting for the relevant experimental evidence. Because consciousness is a rich biological phenomenon, the theories surveyed in this article vary in emphasis, level of abstraction, and the extent to which they provide satisfying explanations of conscious phenomena. At present no single effort accounts for all the evidence, but we have seen marked progress in the last few decades. Bernard J. Baars The Neurosciences Institute San Diego, Calif Anil K. Seth Department of Informatics University of Sussex Sussex, UK Relevant Websites: Further Reading

10 Baars, B. J. (1988). A cognitive theory of consciousness. New York, NY, Cambridge University Press. Baars, B. J. (2002). "The conscious access hypothesis: origins and recent evidence." Trends Cogn Sci 6(1): Cosmelli, D., O. David, et al. (2004). "Waves of consciousness: Ongoing cortical patterns during binocular rivalry." Neuroimage. Crick, F. and C. Koch (2003). "A framework for consciousness." Nature Neuroscience 6(2): Dehaene, S., L. Naccache, et al. (2001). "Cerebral mechanisms of word masking and unconscious repetition priming." Nat Neurosci 4(7): Dehaene, S., C. Sergent, et al. (2003). "A neuronal network model linking subjective reports and objective physiological data during conscious perception." Proc Natl Acad Sci U S A 100(14): Edelman, G. M. (1989). The remembered present. New York, NY, Basic Books. Edelman, G. M. and G. Tononi (2000). A universe of consciousness : how matter becomes imagination. New York, NY, Basic Books. Franklin, S. and A. Graesser (1999). "A software agent model of consciousness." Conscious Cogn 8(3): John, E. R. (2001). "A field theory of consciousness." Conscious Cogn 10(2): Lamme, V. and P. Roelfsema (2000). "The distinct modes of vision offered by feedforward and recurrent processing." Trends Neurosci 23(11): Llinas, R. R., U. Ribary, et al. (1998). "The neuronal basis for consciousness." Philos Trans R Soc Lond B Biol Sci 353: Merker, B. ((in press)). "Consciousness without a cerebral cortex: A challenge for neuroscience and medicine." Behavioral and Brain Sciences. Nunez, P. L. and R. Srinivasan (2006). "A theoretical basis for standing and travelling brain waves measured with human EEG with implications for an integrated consciousness." Clin Neurophysiol 117: Seth, A. K. and B. J. Baars (2005). "Neural Darwinism and consciousness." Consciousness and Cognition 14(1): Seth, A. K., E. Izhikevich, et al. (2006). "Theories and measures of consciousness: An extended framework." Proc Natl Acad Sci U S A 103(28): Sheinberg, D. L. and N. K. Logothetis (1997). "The role of temporal cortical areas in perceptual organization." Proc Natl Acad Sci U S A 94(7): Srinivasan, R., D. P. Russell, et al. (1999). "Increased synchronization of magnetic responses during conscious perception." Journal of Neuroscience 19: Tononi, G. (2004). "An information integration theory of consciousness." BMC Neurosci 5(1): 42.

11 Table 1. Some basic features of conscious brain events. (Adapted from Seth et al, 200x). 1 Consciousness is characterized by irregular, low-amplitude, fast (12-70Hz) electrical field activity 2 Consciousness is associated with activity in the thalamocortical complex, modulated by subcortical nuclei. 3 Consciousness involves distributed cortical activity related to conscious contents 4 Conscious events are integrated into unitary, consistent scenes 5 Integrated conscious scenes occur serially; ony one is experienced at a time. 6 Conscious scenes are metastable and reflect rapidly adapting discrimination in perception and memory. 7 Conscious scenes comprise a wide multimodal range of contents and involve multimodal sensory binding. 8 Conscious scenes have a focus/fringe structure; focal conscious contents are modulated by attention. 9 Consciousness is subjective and private and is often attributed to an experiencing self, which may involve distinctive brain regions. 10 Conscious experience is reportable by humans, verbally and nonverbally. 11 Consciousness facilitates various forms of learning. Even implicit learning initially requires consciousness of the stimuli from which regularities are unconsciously inferred. 12 Conscious scenes have an allocentric (world-centered) character, yet are shaped by egocentric (subject-centered) frameworks. They generally show intentionality (aboutness). 13 Consciousness is needed for much decision making, adaptive planning, and voluntary control.