New Clues about the Origins of Biological Intelligence
In the middle of his landmark book On the Origin of Species, Darwin had a crisis of faith. In a bout of honesty, he wrote, “To suppose that the eye with all its inimitable contrivances for adjusting the focus to different distances, for admitting different amounts of light, and for the correction of spherical and chromatic aberration, could have been formed by natural selection, seems, I confess, absurd in the highest degree.” While scientists are still working out the details of how the eye evolved, we are also still stuck on the question of how intelligence emerges in biology. How can a biological system ever generate coherent and goal-oriented behavior from the bottom up when there is no external designer?
In fact, intelligence—a purposeful response to available information, often anticipating the future—is not restricted to the minds of some privileged species. It is distributed throughout biology, at many different spatial and temporal scales. There are not just intelligent people, mammals, birds and cephalopods. Intelligent, purposeful problem-solving behavior can be found in parts of all living things: single cells and tissues, individual neurons and networks of neurons, viruses, ribosomes and RNA fragments, down to motor proteins and molecular networks. Arguably, understanding the origin of intelligence is the central problem in biology—one that is still wide open. In this piece, we argue that progress in developmental biology and neuroscience is now providing a promising path to show how the architecture of modular systems underlies evolutionary and organismal intelligence.
Biologists are trained to focus on the mechanisms of living systems and not on their purpose. As biologists, we are supposed to work out the “how” rather than the “why,” pursuing causality rather than goals. The “why” is not only always present but is precisely what drives specific “how”s to be chosen, enabling organisms to survive by selecting and exploiting specific mechanisms out of an astronomically large space of possibilities. In the case of the human eye, for example, the optical properties of the lens only make sense if they help focus the light on the retina. If you don’t ask why the lens is transparent, you will never understand its function, no matter how long you study how it becomes transparent.
In fact, the problem of understanding how intelligence emerges is becoming more acute with the “omics” revolution, which is generating systematic, quantitative data on genomes, transcriptomes, proteomes and connectomes. Biological systems are being dissected into their ultimate complexity, but no magic answer is appearing at the end of the tunnel. The race to big data is not providing a better explanation of living systems. If anything, it’s making it harder.
Modern biology faces a fundamental knowledge gap when trying to explain meaningful, intelligent behavior. How can a system composed of cells and electrical signals generate a well-adapted body with behavior and mental states? If cells are not intelligent, how can intelligent behavior emerge from a distributed system composed of them? This fundamental mystery permeates biology. All biological phenomena are, in a sense, “group decisions” because organisms are made of individual parts—organs, tissues, cells, organelles, molecules. What properties of living systems enable components to work together toward higher-level goals?
A common solution is emerging in two different fields: developmental biology and neuroscience. The argument proceeds in three steps. The first rests on one of natural selection’s first and best design ideas: modularity. Modules are self-contained functional units like apartments in a building. Modules implement local goals that are, to some degree, self-maintaining and self-controlled. Modules have a basal problem-solving intelligence, and their relative independence from the rest of the system enables them to achieve their goals despite changing conditions.
