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  • Writer's pictureNicholas Kluge

Cognition and Consciousness in Brains and Machines

Updated: Nov 11, 2021

What actually is consciousness, and how did it evolve?

The underlying idea of this project is that a conception of artificial consciousness must seriously consider the properties of a natural consciousness from a functional point of view; in other words, how did consciousness evolve and what is it for? What would be the evolutionary advantages for organisms that have this faculty?

One of several fruitful scientific approaches to understanding consciousness is to study its evolutionary origins. Once we shed all “magical thinking” about the nature of consciousness and try to understand it as a biological phenomenon, it immediately becomes important that like all other biological phenomena and like life itself, it must have evolved by gradations or by exaptation. Attempting to investigate how consciousness evolved is critical to knowing the "why" of consciousness, that is, why does consciousness exist? These approaches for consciousness refer to the theory firmly rooted in evolutionary biology.

Over the past several decades, besides the evolutionary interest, consciousness has become an exciting research topic in cognitive science, and its subfield, cognitive neuroscience. The phenomenon of consciousness is now being addressed by neuroscientists as a product of a neural activity that can be understood at the neural level. Various theories for the neural basis of consciousness have been proposed, suggesting a diversity of neural signs and mechanisms and there is a need to investigate whether to what extent this diversity is real or whether many theories share the same basic ideas with a potential for convergence towards a more unified theory of the neural basis of consciousness to proposes a model regarding the brain systems that may support the consciousness process.

A fruitful hypothesis to explore concerns the intricate link between cognition and consciousness. A large number of the models for a consciousness framework associate it with distinct cognitive architectures or special patterns of activity. Some examples are Attended Intermediate Level Representation theory (AIR) by Jesse Prinz, Global Workspace Theory (GWT) by Bernard Baars, Multiple Drafts Model (MDM) by Daniel Dennett, and more neurobiological theories like Somatic Markers Hypothesis (SMH) by Antonio Damasio and High-order Emotional Theory by Joseph Ledoux (HOTEC).

These models might provide viable strategies for an attempt to bridge the gap between the neurobiological and psychological processes. Thereby, the possibility of understanding the role of cognitive architectures for the function of consciousness may proffer insights into computational concepts about consciousness according to a theory of cognitive and functionalist hue.

The intersection between cognitive neuroscience and Artificial Intelligence (AI) research has created synergistic effects in both fields. While neuroscientific discoveries have inspired the development of AI architectures, new ideas and algorithms from AI research have produced new ways to study brain mechanisms (a well-known example is the case of reinforcement learning, which has stimulated neuroscience research on how animals learn to adjust their behavior to maximize reward).

Then, in a kind of “mutual enlightenment,” on the one hand, an artificial consciousness project requires a cognitive architecture model for consciousness to implement the theory in computers. Therefore, the main advantage of a naturalistic cognitive architecture for consciousness is that it can synthesize the various results of cognitive psychology, cognitive neuroscience, and evolutionary biology into a comprehensive computational model of artificial consciousness focused on the functional question of consciousness.

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