In the last century, we have made great strides towards understanding natural cognition and recreating it artificially. However, most cognitive research is still guided by an inadequate theoretical framework that equates cognition to a computer system executing a data processing task. Cognition, whether natural or artificial, is not a data processing system; it is a control system. At cognition's core is a value system that allows it to evaluate current conditions and decide among two or more courses of action. Memory, learning, planning, and deliberation, rather than being essential cognitive abilities, are features that evolved over time to support the primary task of deciding "what to do next". I used digital evolution to recreate the early stages in the evolution of natural cognition, including the ability to learn. Interestingly, I found cognition evolves in a predictable manner, with more complex abilities evolving in stages, by building upon previous simpler ones. I initially investigated the evolution of dynamic foraging behaviors among the first animals known to have a central nervous system, Ediacaran microbial mat miners. I then followed this up by evolving more complex forms of learning. I soon encountered practical limitations of the current methods, including exponential demand of computational resources and genetic representations that were not conducive to further scaling. This type of complexity barrier has been a recurrent issue in digital evolution. Nature, however, is not limited in the same ways; through evolution, it has created a language to express robust, modular, and flexible control systems of arbitrary complexity and apparently open-ended evolvability. The essential features of this language can be captured in a digital evolution platform. As an early demonstration of this, I evolved biologically plausible regulatory systems for virtual cyanobacteria. These systems regulate the cells' growth, photosynthesis and replication given the daily light cycle, the cell's energy reserves, and levels of stress. Although simple, this experimental system displays dynamics and decision-making mechanisms akin to biology, with promising potential for open-ended evolution of cognition towards general intelligence.
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