By Ethan Sim
The concept of consciousness is both exceedingly familiar to laypersons and impenetrably obscure to scientists. Despite being a relatively recent addition to the neuroscientific disciplines, the academic exploration of consciousness has provided exceptionally fertile ground for debate and discussion, with a welter of theories emerging in the past decade. These debates and theories have two main objectives: defining consciousness itself, and identifying structures responsible for causing consciousness. Ultimately, it is hoped that advances in consciousness research will lead to empirical measurements of consciousness, with potential applications in the legal and ethical domains (Overgaard, 2017).
The relatively recent inclusion of consciousness research under the twin umbrellas of psychology and cognitive neuroscience belies its centuries-long history as a predominantly philosophical pursuit. Although the first comprehensive exploration of the concept of consciousness is attributed to the Enlightenment philosopher John Locke (Gordon-Roth, 2020), consciousness research only began to receive serious scientific attention in the late 1990s, following a series of influential papers by Francis Crick and Christof Koch (LeDoux et al., 2020), who were the first to attempt an empirical determination of the neurobiological bases for consciousness (Crick and Koch, 1990). Interest in the nascent field was further stimulated by the philosopher David Chalmers, who eloquently framed the questions surrounding consciousness as “easy” and “hard” problems (Chalmers, 1995). “Easy” problems refer to the identification of mechanisms responsible for aspects of consciousness, such as the ability to focus attention and report mental states, whilst the “hard” problems refer to the identification of a basis for consciousness itself – “why and how [neurological] processes are accompanied by experience” (Chalmers, 1995). More than two decades after Chalmers’ formulation, a consciousness theory’s explanatory power is still premised on its ability to provide adequate solutions to both categories of problems.
Today, neurobiological theories of consciousness, which seek to identify Neural Correlates of Consciousness (NCCs) – the minimal neural mechanisms jointly sufficient for any specific conscious percept (Koch et al., 2016) – feature most prominently in scientific discourse. Foremost among these is the Global Neuronal Workspace Theory (GNWT), which describes the brain as a neuronal workspace, a series of modular networks working in parallel to process information in an unconscious manner (Dehaene and Naccache, 2001). Although these networks are highly interconnected, they are siloed by default, and it is only when attention is focused on a specific stimulus that these inter-network connections are activated, with the information contained in the stimulus broadcast to the entire workspace, rendering it globally accessible (Dehaene and Naccache, 2006). For information to be consciously perceived, such global access is a necessary and sufficient prerequisite, and thus constitutes an NCC. In essence, the theory predicts that conscious perception will result in patterns of coherent brain activity which are greater in magnitude and extent than those which stem from unconscious perception (Dehaene and Naccache, 2001). This prediction has been confirmed by brain-imaging experiments, most notably by a study on macaques which utilized binocular flash suppression to induce both conscious and unconscious visual perceptual states, which could then be monitored (Panagiotaropoulos et al., 2012). In addition to the GNWT, two other established neurobiological theories include Recurrent Processing Theory and Higher Order Theories. Recurrent Processing Theory holds that two-way communication between sensory areas and higher-level processing areas (which precedes global broadcasting) is sufficient for the consciousness experience, and therefore itself constitutes an NCC (Lamme, 2006). Higher Order Theories intuitively frame conscious states as mental states which require awareness of one’s presence therein (Rosenthal, 1986); it therefore considers prefrontal cortex activity, which is responsible for such higher-order representations, as an NCC (Odegaard et al., 2017) – although this is hotly debated (Carruthers and Gennaro, 2020).
Although there was initially widespread acceptance of neurobiological perspectives among consciousness researchers, a growing number of academics began to criticize the NCC concept for being too human-centric, which would hinder the development of a general theory of consciousness (Tononi and Koch, 2015). It was also argued that neurobiological approaches were essentially attempts to “distil mind out of matter”, which rendered Chalmers’ hard problem nearly impossible to solve (Tononi and Koch, 2015). This criticism resulted in the Information Integration Theory (IIT), which seeks to create an empirical and quantifiable measurement of consciousness, termed Φ, by defining properties common to all conscious experiences. These properties are used to generate postulates – a set of prerequisites for conscious perception. Φ itself quantifies the most important postulate – the integrated information carried by a system – which is positive when the effective informational content of the system is greater than the sum of the informational content of its parts (Tononi, 2012). To IIT proponents, consciousness resides within integrated information, and the NCC is thus predicted to be within the neural area with the highest local Φ; it is thought to be a “posterior hot zone” covering the parietal and occipital lobes (Siclari et al., 2017). Although it has received criticism for failing to generate significant and unique predictions (Cerullo, 2015), IIT’s contribution to the continuing discourse is a theoretical framework which underpins the NCC concept and encourages consciousness research beyond the human brain.
A promising newcomer, more general than existing neurobiological theories, yet more specific than the IIT, is the Attention Schema Theory (AST). Under the AST, consciousness arises because the brain constructs a simplified model, the attention schema, of the process of attention (Webb and Graziano, 2015). Proponents of the AST argue that, just as the brain constructs a rough mental model of the body for more efficient control (Holmes and Spence, 2004), the attention schema is constructed to provide efficient control over attention, a complex process involving inter-signal competition for the brain’s limited resources (Desimone and Duncan, 1995). The attention schema depicts a mental force which empowers one to be aware of, and thus react to, the signal, and this force is theorized to be consciousness (Webb and Graziano, 2015). This means that the attention schema itself is the NCC; any creature with the ability to construct an attention schema is therefore conscious and this includes: reptiles, birds, and mammals (Graziano, 2016). The AST is supported by studies which have dissociated attention from the attention schema, resulting in reduced control of the former. One notable experiment showed that participants who were aware of a peripheral distracting stimulus performed better at a task than those who were unaware (Tsushima et al., 2006); this was potentially because the first group were able to model the distraction with their attention schemas, consciously inhibit its signals, and more efficiently focus their attention on the task (Webb and Graziano, 2015).
Despite the plethora of consciousness theories, it is apparent that very few are capable of providing solutions to both of Chalmers’ problems. Neurobiological theories, such as the GNWT, tend to focus on identifying mechanisms which correlate with conscious perception, without explaining why these generate subjective experience. Similarly, the IIT fails to explain why experience arises, although it may permit general assessments of consciousness across both biological and artificial systems. Conversely, the AST’s equation of consciousness and the attention schema enables it to propose a mechanism for subjective experience itself, but it has yet to gain traction among consciousness researchers. Although there are no clear answers to the problem of consciousness, promising developments in the neuroscientific and psychological fields, from large-scale open data sharing (Milham et al., 2018) to neural monitoring (Xu et al., 2019), may bring researchers one step closer.
Introduction to leading consciousness theories:
Carruthers, P. & Gennaro, R. (2020) Higher-Order Theories of Consciousness. In: Zalta, E. (ed.). The Stanford Encyclopedia of Philosophy. Fall 2020 edition.
Cerullo, M. (2015) The Problem with Phi: A Critique of Integrated Information Theory. PLOS Computational Biology. 11 (9).
Chalmers, D. (1995) Facing up to the problem of consciousness. Journal of Consciousness Studies. 2 (3), 200-219.
Crick, F. & Koch, C. (1990) Towards a neurobiological theory of consciousness. Seminars in the Neurosciences. 2, 263-275.
Dehaene, S., Changeux, J., Naccache, L., Sackur, J. & Sergent, C. (2006) Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Sciences. 10 (5), 204-211.
Dehaene, S. & Naccache, L. (2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition. 79 (1-2), 1-37.
Desimone, R. & Duncan, J. (1995) Neural Mechanisms of Selective Visual Attention. Annual Review of Neuroscience. 18, 193-222.
Gordon-Roth, J. (2020) Locke on Personal Identity. In: Zalta, E. (ed.). The Stanford Encyclopedia of Philosophy. Spring 2020 edition.
Graziano, M. (2016) A New Theory Explains How Consciousness Evolved. A neuroscientist on how we came to be aware of ourselves. The Atlantic.
Holmes, N. & Spence, C. (2004) The body schema and the multisensory representation(s) of peripersonal space. Cognitive Processing. 5 (2), 94-105.
Koch, C., Massimini, M., Boly, M. & Tononi, G. (2016) Neural correlates of consciousness: progress and problems. Nature Reviews Neuroscience. 17, 307-321.
Lamme, V. (2006) Towards a true neural stance on consciousness. Trends in Cognitive Sciences. 10 (11), 494-501.
LeDoux, J., Michel, M. & Lau, H. (2020) A little history goes a long way toward understanding why we study consciousness the way we do today. Proceedings of the National Academy of Sciences of the United States of America. 117 (13), 6976-6984.
Milham, M., Craddock, R., Son, J., Fleischmann, M., Clucas, J., Xu, H., Koo, B., Krishnakumar, A., Biswal, B., Castellanos, X., Colcombe, S., di Martino, A., Zuo, X. & Klein, A. (2018) Assessment of the impact of shared brain imaging data on the scientific literature. Nature Communications. 9 (2818).
Odegaard, B., Knight, R. & Lau, H. (2017) Should a Few Null Findings Falsify Prefrontal Theories of Conscious Perception?. The Journal of Neuroscience. 37 (40), 9593-9602.
Overgaard, M. (2017) The Status and Future of Consciousness Research. Frontiers in Psychology. 8 (1719).
Panagiotaropoulos, T., Deco, G., Kapoor, V. & Logothetis, N. (2012) Neuronal Discharges and Gamma Oscillations Explicitly Reflect Visual Consciousness in the Lateral Prefrontal Cortex. Neuron. 74 (5), 924-935.
Rosenthal, D. (1986) Two Concepts of Consciousness. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition. 49 (3), 329-359.
Siclari, F., Baird, B., Perogamvros, P., Bernardi, G., LaRocque, J., Riedner, B., Boly, M., Postle, B. & Tononi, G. (2017) The neural correlates of dreaming. Nature Neuroscience. 20 (6), 872-878.
Tononi, G. (2012) Integrated information theory of consciousness: an updated account. Archives Italiennes De Biologie. 150 (4), 293-329.
Tononi, G. & Koch, C. (2015) Consciousness: here, there, and everywhere?. Philosophical Transactions of the Royal Society B. 370 (1668).
Tsushima, Y., Sasaki, Y. & Watanabe, T. (2006) Greater Disruption Due to Failure of Inhibitory Control on an Ambiguous Distractor. Science. 314 (5806), 1786-1788.
Webb, T. & Graziano, M. (2015) The attention schema theory: a mechanistic account of subjective awareness. Frontiers in Psychology. 6 500.
Xu, A., Qian, M., Tian, F., Xu, B., Friedman, R., Wang, J., Song, X., Sun, Y., Chernov, M., Cayce, J., Jansen, D., Mahadevan-Jansen, A., Zhang, X., Chen, G. & Roe, A. (2019) Focal infrared neural stimulation with high-field functional MRI: A rapid way to map mesoscale brain connectomes. Science Advances. 5 (4).