The HI Theory of Conscious Awareness
John Cochrane, 7 October 2025
Abstract
The HI Theory of Conscious Awareness proposes that conscious awareness evolved through successive layers of sensory and cognitive complexity, culminating in the integrative functions supported by the neocortex. The theory situates consciousness as a continuously emerging quality, arising from multi-layered simulations of internal and external reality. While conceptually compatible with the Global Workspace Theory (Baars, 1988; Dehaene and Naccache, 2001) and the Thousand Brains Model of Intelligence (Hawkins, 2021), the HI Theory emphasizes the evolutionary continuity of sensory processing, attention, curiosity, and self-awareness as interacting mechanisms that scaffold human consciousness.
Theoretical Context
The HI Theory of Conscious Awareness is the third of four interrelated theories addressing distinct aspects of human consciousness. While each component theory can stand independently, together they contribute to a broader integrative model of conscious function. The present theory focuses on the emergence of awareness from primitive sensory and attentional processes.
This approach aligns with contemporary frameworks that treat consciousness as a product of distributed brain activity—particularly those emphasizing integration and access (Dehaene and Changeux, 2011; Tononi et al., 2016). It also relates to recent hemispheric models of cognition (McGilchrist, 2019) and the Thousand Brains Model of cortical computation (Hawkins, 2021), both of which highlight parallel, modular, and integrative processing across cortical regions.
Evolutionary Roots of Awareness
Sensory Foundations
The earliest precursors to conscious awareness likely involved rudimentary sensory discriminations—such as the detection of light and dark—which facilitated basic survival behaviours like phototaxis and circadian regulation (Nilsson, 2013). These processes were initially subcortical and reflexive, providing a foundation for later perceptual complexity.
Pattern Recognition
As nervous systems evolved, sensory information began to support higher-order representations of shape, motion, and spatial relations. The emergence of pattern recognition—supported in modern brains by ventral and dorsal visual streams (Goodale and Milner, 1992)—allowed organisms to distinguish between safe and threatening stimuli, integrating sensory input with memory and motivational systems (Rolls, 2014).
Attention and the Growth of Representational Depth
The Emergence of Attention
The evolution of attentional systems represented a major cognitive milestone. Attention serves as a selective filter that amplifies relevant stimuli and suppresses irrelevant ones (Posner and Petersen, 1990). Neural evidence indicates that attention modulates perceptual processing through frontoparietal networks and thalamocortical loops (Buschman and Kastner, 2015), enabling both the assessment and prediction of environmental contingencies.
Concentrated Attention and Executive Control
With increasing neural complexity, attentional mechanisms evolved toward focused or concentrated attention, integrating perceptual evaluation with motor planning. This form of sustained attention corresponds to executive control processes mediated by the prefrontal cortex and anterior cingulate cortex (Petersen and Posner, 2012). Functionally, it allows organisms to sustain goal-directed focus—what may be experienced phenomenologically as curiosity-driven concentration.
The Emergence of Self-Awareness
Self-awareness likely arose as a regulatory extension of attentional and evaluative processing. By monitoring and managing internal states—both physiological and cognitive—the brain could model its own activity, supporting metacognitive awareness (Fleming and Dolan, 2012). Modern neuroimaging associates this capacity with midline cortical structures, including the medial prefrontal cortex and posterior cingulate cortex, components of the default mode network (Qin et al., 2020).
Self-awareness enables organisms not only to act upon the environment but also to evaluate the self as an actor within it. This recursive capability transforms perception and action into subjective experience—what Edelman (2003) called “higher-order consciousness.”
Intelligence and the Neocortical Substrate
The Thousand Brains Model (Hawkins, 2021) provides a framework for understanding how distributed cortical columns generate complex, model-based intelligence. In this view, general intelligence arises from the capacity of neocortical structures to learn and predict through hierarchical and parallel modelling. Intelligence, conceptualized as the capacity to learn adaptively and apply knowledge resourcefully, thus feeds consciousness with increasingly abstract and predictive representations of reality (Friston, 2010).
Curiosity as a Motivational Driver of Awareness
Curiosity serves as a motivational mechanism that transforms passive perception into active exploration. It engages dopaminergic reward systems, including the ventral tegmental area (VTA) and nucleus accumbens (Kang et al., 2009; Gruber et al., 2014), linking the anticipation of information with intrinsic reward. Curiosity enhances learning by facilitating hippocampal-dependent memory encoding (Gruber and Ranganath, 2019) and by bridging affective and cognitive systems to sustain attention and exploration.
Within the HI Mind framework, curiosity functions as a bridge between sensory processing, attention, and higher-order awareness. It fuels the continuous refinement of internal simulations and underpins self-reflective thought—an essential feature of what may be termed core consciousness.
Emergent Consciousness and Narrative Integration
Contemporary theories often distinguish between emergent and narrative models of consciousness. Emergent models (e.g., Tononi, 2008; Friston, 2010) emphasize the integration of distributed neural activity into unified awareness, whereas narrative models (e.g., Dennett, 1991; Graziano, 2013) view consciousness as a dynamic construction—a continuously updated “story” generated by the brain to maintain coherence over time.
The HI Theory of Consciousness posits that both processes are complementary: emergent consciousness supplies the raw experiential substrate through integrated processing, while narrative consciousness organizes this content into temporally coherent representations. Conscious awareness thus reflects a feedback loop linking sensory simulation, predictive modelling, attention, and self-referential narration.
Conclusion
Conscious awareness is proposed here as an evolutionary continuum arising from progressively complex layers of sensory processing, attentional control, and self-representation. These processes are coordinated by neocortical systems that integrate distributed sensory, emotional, and cognitive information into a coherent experiential stream. Curiosity plays a central role as the motivational engine that drives exploration, learning, and the ongoing construction of conscious reality.
References
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press.
Buschman, T. J., and Kastner, S. (2015). From behaviour to neural dynamics: An integrated theory of attention. Neuron, 88(1), 127–144.
Dehaene, S., and Changeux, J.-P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227.
Dehaene, S., and Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79(1–2), 1–37.
Dennett, D. C. (1991). Consciousness explained. Little, Brown and Company.
Edelman, G. M. (2003). Naturalizing consciousness: A theoretical framework. Proceedings of the National Academy of Sciences, 100(9), 5520–5524.
Fleming, S. M., and Dolan, R. J. (2012). The neural basis of metacognitive ability. Philosophical Transactions of the Royal Society B, 367(1594), 1338–1349.
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.
Goodale, M. A., and Milner, A. D. (1992). Separate visual pathways for perception and action. Trends in Neurosciences, 15(1), 20–25.
Gruber, M. J., Gelman, B. D., and Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486–496.
Gruber, M. J., and Ranganath, C. (2019). How curiosity enhances hippocampus-dependent memory: The prediction, appraisal, curiosity, and exploration (PACE) framework. Trends in Cognitive Sciences, 23(12), 1014–1025.
Hawkins, J. (2021). A thousand brains: A new theory of intelligence. Basic Books.
Kang, M. J., Hsu, M., Krajbich, I. M., Loewenstein, G., McClure, S. M., Wang, J. T., and Camerer, C. F. (2009). The wick in the candle of learning: Epistemic curiosity activates reward circuitry and enhances memory. Psychological Science, 20(8), 963–973.
McGilchrist, I. (2019). The master and his emissary: The divided brain and the making of the Western world (2nd ed.). Yale University Press.
Nilsson, D.-E. (2013). Eye evolution and its functional basis. Visual Neuroscience, 30(1–2), 5–20.
Petersen, S. E., and Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89.
Qin, P., Wang, M., and Northoff, G. (2020). Linking bodily, environmental and mental states in the self—A three-level model based on a meta-analysis. Neuroscience and Biobehavioural Reviews, 115, 77–95.
Rolls, E. T. (2014). Emotion and decision-making explained. Oxford University Press.
Tononi, G. (2008). Consciousness as integrated information: A provisional manifesto. Biological Bulletin, 215(3), 216–242.
Tononi, G., Boly, M., Massimini, M., and Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461.