This is intelligence in action: the ability to reach a particular goal or solve a problem by undertaking new steps in the face of changing circumstances. It’s evident not just in intelligent people and mammals and birds and cephalopods, but also cells and tissues, individual neurons and networks of neurons, viruses, ribosomes and RNA fragments, down to motor proteins and molecular networks. Across all these scales, living things solve problems and achieve goals by flexibly navigating different spaces – metabolic, physiological, genetic, cognitive, behavioural.
But how did intelligence emerge in biology? The question has preoccupied scientists since Charles Darwin, but it remains unanswered. The processes of intelligence are so intricate, so multilayered and baroque, no wonder some people might be tempted by stories about a top-down Creator. But we know evolution must have been able to come up with intelligence on its own, from the bottom up.
Darwin’s best shot at an explanation was that random mutations changed and rearranged genes, altered the structure and function of bodies, and so produced adaptations that allowed certain organisms to thrive and reproduce in their environment. (In technical terms, they are selected for by the environment.) In the end, somehow, intelligence was the result. But there’s plenty of natural and experimental evidence to suggest that evolution doesn’t just select hardwired solutions that are engineered for a specific setting. For example, lab studies have shown that perfectly normal frog skin cells, when liberated from the instructive influence of the rest of the embryo, can reboot their cooperative activity to produce a novel proto-organism, called a ‘xenobot’. Evolution, it seems, doesn’t come up with answers so much as generate flexible problem-solving agents that can rise to new challenges and figure things out on their own.
The urgency of understanding intelligence in biological terms has become more acute with the ‘omics’ revolution, where new techniques are amassing enormous amounts of fresh data on the genes, proteins and connections within each cell. Yet the deluge of information about cellular hardware isn’t yielding a better explanation of the intelligent flexibility we observe in living systems. Nor is it yielding sufficient practical insights, for example, in the realm of regenerative medicine. We think the real problem is not one of data, but of perspective. Intelligence is not something that happened at the tail end of evolution, but was discovered towards the beginning, long before brains came on the scene.
From the earliest metabolic cycles that kept microbes’ chemical parameters within the right ranges, biology has been capable of achieving aims. Yet generation after generation of biologists have been trained to avoid questions about the ultimate purpose of things. Biologists are told to focus on the ‘how’, not the ‘why’, or risk falling prey to theology.
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Modularity provides stability and robustness, and is the first part of the answer to how intelligence arose. When changes occur to one part of the body, its evolutionary history as a nested doll of competent, problem-solving cells means subunits can step up and modify their activity to keep the organism alive. This isn’t a separate capacity that evolved from scratch in complex organisms, but instead an inevitable consequence of the ancient ability of cells to look after themselves and the networks of which they form a part.
But just how are these modules controlled? The second step on the road to the emergence of intelligence lies in knowing how modules can be manipulated. Encoding information in networks requires the ability to catalyse complex outcomes with simple signals. This is known as pattern completion: the capacity of one particular element in the module to activate the entire module. That special element, which serves as a ‘trigger’, starts the activity, kicking the other members of the module into action and completing the pattern. In this way, instead of activating the entire module, evolution needs only to activate that trigger.
Pattern completion is an essential aspect of modularity which we’re just beginning to understand, thanks to work in developmental biology and neuroscience. For example, an entire eye can be created in the gut of a frog embryo by briefly altering the bioelectric state of some cells. These cells are triggered to complete the eye pattern by recruiting nearby neighbours (which were not themselves bioelectrically altered) to fill in the rest of the eye. Similar outcomes can be achieved by genetic or chemical ‘master regulators’, such as the Hox genes that specify the body plan of most bilaterally symmetrical animals. In fact, one could relabel these regulator genes as pattern completion genes, since they enable the coordinated expression of a suite of other genes from a simple signal. The key is that modules, by continuing to work until certain conditions are met, can fill in a complex pattern when given only a small part of the pattern. In doing so, they translate a simple command – the activation of the trigger – and amplify it into an entire program.
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We have sketched a set of approaches to biology that rely heavily on concepts from cybernetics, computer science, and engineering. But there’s still a lot of work to do in reconciling these approaches. Despite recent advances in molecular genetics, our understanding of the mapping between the genome on the one hand, and the (changeable) anatomy and physiology of the body on the other, is still at a very early stage. Much like computer science, which moved from rewiring hardware in the 1940s to a focus on algorithms and software that could control the device’s behaviour, biological sciences now need to change tack.
The impact of understanding nested intelligence across multiple scales cuts across numerous fields, from fundamental questions about our evolutionary origins to practical roadmaps for AI, regenerative medicine and biorobotics. Understanding the control systems implemented in living tissue could lead to major advances in biomedicine. If we truly grasp how to control the setpoints of bodies, we might be able to repair birth defects, induce regeneration of organs, and perhaps even defeat ageing (some cnidarians and planarian flatworms are essentially immortal, demonstrating that complex organisms without a lifespan limit are possible, using the same types of cells of which we are made). Perhaps cancer can also be addressed as a disease of modularity: the mechanisms by which body cells cooperate can occasionally break down, leading to a reversion of cells to their unicellular past – a more selfish mode in which they treat the rest of the body as an environment within which they reproduce maximally.
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The idea of modular cognition is beautiful and I almost fell head-over-heels for it.
But... once again, people conveniently forget that we are dealing with complex systems.
Just a cursory second reading of this piece will expose the "know" missing pieces. Microbiomes for starters and there is this thing called "exposome" which covers those "little" things namely the environmental factors. And of-course there are myriads of unknowns.
Nevertheless, the "why" question they ask - "why biology acts this way" is extremely important.
I am convinced our generation and many generations to come will fail to answer this question only because people are not used to asking the why question at the micro level. Maybe, someday this question will be answered and should be answered. The hypothesis of "Pattern Completion" is small step forward and kudos to those who are working on such hard problems.
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