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The perennial philosophical puzzle of consciousness has, in recent decades, become the central scientific challenge for neuroscience. While tools like fMRI (functional Magnetic Resonance Imaging) and EEG (Electroencephalography) have proven instrumental in mapping the brain’s correlates of consciousness (NCCs)—the minimal neural mechanisms sufficient for any specific conscious experience—they have ultimately failed to bridge the explanatory gap between physical brain activity and subjective, felt experience. The difficulty lies in moving from identifying where and when consciousness occurs to understanding how matter generates the sensation of ‘self.’

A dominant contemporary theory attempting to tackle this problem is the Integrated Information Theory (IIT), initially proposed by Giulio Tononi. IIT postulates that consciousness is equivalent to the amount of integrated information generated by a physical system. Crucially, the theory asserts that consciousness is an intrinsic property of a system that possesses a high degree of complexity and interconnectedness, quantified by a mathematical value known as $\Phi$ (Phi). This framework makes a strong claim: consciousness is not limited to biological organisms but could potentially exist in any system—from a sophisticated computer network to a rudimentary neural circuit—provided its integrated information value exceeds zero.

This perspective, however, is met with significant scepticism. Critics argue that IIT is highly reductive and that its mathematical formalisms, while elegant, do not truly capture the qualitative qualia of human experience—the ‘redness’ of red, or the pain of a headache. Furthermore, a substantial methodological challenge exists: calculating the $\Phi$ value for a system as vast and dynamic as the human brain is practically impossible with current technology. I believe that while IIT provides a valuable heuristic framework for testing neural integration, treating $\Phi$ as the definition of consciousness risks mistaking a measurable symptom for the underlying phenomenon.

Another influential, yet arguably less ambitious, approach focuses on the Global Neuronal Workspace (GNW) model. Developed by Bernard Baars and Stanislas Dehaene, GNW suggests that consciousness arises when specific information is broadcast widely to a network of diverse, specialized processing modules across the cortex. In this view, the “workspace” is a bottleneck that selects and amplifies information, making it globally available for deliberation and action. Unlike IIT, GNW does not attempt to solve the hard problem of qualia but instead provides a functional, testable mechanism for accessing and sharing non-conscious information. Proponents of GNW rightly point out that their model has successfully predicted which brain regions activate when non-conscious stimuli cross the threshold into awareness.

The implications of successfully mapping consciousness are vast and stretch far beyond the confines of academic debate. If IIT is correct, then constructing an artificial consciousness is merely a matter of engineering a system with sufficiently high $\Phi$. This raises serious ethical questions regarding the rights and responsibilities owed to a non-biological conscious entity. If GNW is the correct model, efforts would instead focus on designing systems capable of global information broadcasting. My primary concern is that the rapid advancement in brain mapping technologies, such as optogenetics and high-density electrode arrays, may soon give us the power to manipulate or even synthesize NCCs before we fully understand the moral consequences of such intervention. Ultimately, while neuroscience continues to refine its maps of the brain, the fundamental territory of subjective experience remains largely uncharted.