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Slime Moulds and the Bitter Debate Over the Nature of Intelligence
Forty years ago, as an undergraduate in Hokkaido, Toshiyuki Nakagaki came upon a lemon-yellow stain in a petri dish. That encounter turned him into a doyen of slime mould research – and fed a storm of biological and philosophical debate about the meanings of life and intelligence that persists to this day. Before, no one would have called a slime mould "intelligent"; the popular adjective, in fact, was "simple". "A simple mass of protoplasm," one 1895 description ran; "among the most simple of organisms", said another. And it was hard to argue otherwise. A slime mould can appear to be just a small cell, capable only of feeding and reproducing. Simple is as simple does.
Of that first meeting, Nakagaki said: "I was just impressed with how fast it moved." Physarum polycephalum, the slime mould species in that petri dish, has a top speed of 1cm an hour. For amoebic organisms, that's gold-medal standard. At first glance, Physarum seems to spread as irregularly as ink on blotting paper, but in fact it senses its meals of bacteria or other microbes and reaches for them with a long, thin finger of protoplasm – which not only digests its food but then forages further or pulls back into the rest of the mould. It's impossible to watch a slime mould over many days without sensing some deliberation at play.
Physarum is often found as a glistening patch creeping over leaf litter in forests; hence its nickname: "dog vomit mould". Even at its most expansive, occupying a couple of square metres, it is a single cell containing many nuclei – the tiny microdots holding its DNA. (Technically, this kind of structure is called a "syncytium".) The fact that you can see it with the naked eye, and even perceive the swells and contractions of the protoplasm within, makes it ideal for experiments. So Nakagaki got to work.
In 2000, his astonishing research began to surface in the media. "Slime mould solves maze puzzle," one headline ran [abc.net.au], summarising an experiment in which Nakagaki induced a Physarum to find the shortest route through a maze in search of food. (In labs, slime moulds are reared on oat flakes, a convenient diet that can double as a researcher's breakfast.) In 2010, the New York Times wrote [nytimes.com] about Nakagaki's most famous experiment – the canonical study that scientists offhandedly call "the Tokyo railway one". Nakagaki placed oat flakes to resemble the map of Tokyo and 35 of its satellite towns. Then he let the Physarum loose from the centre of "Tokyo". From that one metropolitan oat, the mould spread outwards seeking the others, stretching towards them with its tubules. Then, putting out more branches, it linked up with itself, from one suburban oat flake to the next.
In doing all this, the mould displayed such a verve for efficiency – no wasted motion, an optimal pursuit of its food – that it ended up nearly retracing the route map of the greater Tokyo area's railway system. Nearly, Nakagaki said – because, in fact, free of human bias or politicking, Physarum may have improved the design of the network. Later, when Nakagaki twice won the Ig Nobel – the prize for research that is both amusing and thought-provoking – the first award cited his maze paper in the field of "cognitive science", but the second, cheekily, hailed the Tokyo railway study as a breakthrough in "transportation planning".
Over the past four decades, Nakagaki's hair and stubble have greyed, but he still betrays a sense of wonder while discussing Physarum's "intelligence", a word that biologists invoke carefully. After the maze experiment, he said: "I thought we'd have to rewrite the dictionary." Others have since justified the use of the word "intelligence", as well as other fraught ones such as "learning" and "memory", in describing Physarum. Which poses a mystery: how is Physarum, this cell without even a semblance of a brain, capable of it all – the decision-making, the memory, the processing of information?
[...] Over the past three decades, as the study of intelligence has broadened beyond humans, slime-mould research has gathered speed. We know now that Physarum is made up of thousands of tiny oscillators that sense their environment. If they pick up the chemical markers of a meal, these units oscillate faster, in turn causing the cell's protoplasm to flow more thickly towards that area. This is how the mould moves.
But how these oscillators, so minuscule and seemingly so rudimentary, manage to sense so much – proteins, salts, acidity, light, heat, humidity, perhaps even gravitational and magnetic fields, and masses looming at a distance – is still unclear. In 2008, Nakagaki and his colleagues subjected a Physarum to cold, dry air streamed at regular hourly intervals, prompting it to slow its locomotion. After three such gusts, the mould spontaneously slowed on the top of the fourth hour – even when the mistral didn't blow. No one can figure out how the slime mould keeps time.
It's natural, then, for a layperson to wonder: if it's so difficult to thoroughly decipher a relatively basic being such as the slime mould, how could we ever understand the human brain? If it's a mystery how the Physarum remembers, is it hopeless to look upon the other end of the spectrum and attempt to grasp how, say, a middle-aged man calls to mind a tune from his childhood and plays it on a flute?
The questions that divide neuropurists and their biogenic rivals, though, are different: they argue about whether these abilities – recognising caffeine and recollecting music – are comparable, and whether slime moulds and human beings belong on the same spectrum of cognition at all.
[...] Even among slime-mould enthusiasts, Nakagaki's maze study doesn't convince everyone. The maze as we know it – all right-angled turns and clean routes – doesn't resemble anything the Physarum may encounter in its own, natural environment; it's an artifice premised on the fact that we, as humans, regard the solving of a maze as intelligent behaviour. The Physarum doesn't even "solve" the maze, strictly speaking, as a person would do. "It grows across all of the empty spaces, and then withdraws from everything but the shortest route," Pringle said. The way she described it put me in mind of large language models and their techniques of brute pattern-matching – an incomplete simulation of human thought that is described increasingly as intelligence.
[...] In deciding what intelligence is, humans are easily influenced by the religions, politics and technologies that surround us. It's been a wild ride. In the 17th century, Descartes believed all non-human animals were automata, lacking the immaterial mind that gives humans thought and reason. That view held even after Charles Darwin proved earthworms could learn and described the root tip of a growing plant as an analogue to the human brain. In both cases, he didn't hesitate to use the word "intelligence". Through the first half of the 20th century, the rise of behaviourism led scientists to disregard internal processes of thought altogether, to focus on an organism's actions and reflexes only. For a while, even asking questions about cognition was frowned upon.
More tumult followed. Midway through the last century, computers became the new point of comparison for the mind, and it followed that humans too, like their new machines, thought by building internal models and systems of mathematical rules. This "cognitive revolution" only extended to organisms with brains – with neurons, the wetware version of the microprocessor. Then, in the 1990s, a roboticist named Rodney Brooks built a series of small robots that could trundle through the halls of MIT, dodging obstacles or collecting soda cans, all without constructing any mental models or simulations. This was a revolution in its own way – but as a philosopher named Fred Keijzer told me, even this was unsatisfying, because it failed to approximate the sheer complexity of, say, a jellyfish moving in the ocean. "Cognitive science is still at the stage that chemistry was before the periodic table," he said. "It's a mess."
In this choppy way, we've come into the AI age, which exerts its own perverse pressures. Silicon Valley loves the biogenic camp – loves any move to categorise intelligence as a data and computation problem, since that would validate the I in AI too. No better way, one scientist remarked sardonically, to treat humans as machines than by starting to treat machines as humans. But placing humans and machines on a continuum of intelligence doesn't in itself feel like a problem to Michael Levin, who has argued extensively that even single cells can learn and solve problems. "For Michael," one scientist told me, "it's just cognition all the way down."
[...] Broad though the term "cognition" is, scientists still gain from applying it across the kingdom of life, Levin claims. "Drawing hard boundaries – 'This is cognitive, this is not cognitive' – prevents you from moving concepts from one field to another," he said. "If Newton, worried that people wouldn't understand, had decided to call the force that makes an apple fall from a tree 'gravity' and the force that keeps the moon going around the Earth 'shmavity', maybe it would've been simpler," Levin said. "But he'd have missed out on a great conceptual unification."
It would have been impossible, though, to unify "gravity" and "shmavity" without a widely accepted definition of "force". Biology is struggling with definitions: clear terms of reference, even if they are works in progress, for fundamental concepts such as "intelligence". Perhaps this is unavoidable for a species that has long used itself as a measure of intelligence – without even knowing how its own intelligence comes about. But ambiguity will hobble us as we try to understand what life is and how many different ways it has found to do what it needs to do. Reproduce. Survive. Find its way to food through a literal or metaphorical maze. Stay in the shade, unless the sun is more beneficial; stay where it's dry, unless the wet is better. Sense the world, and find a place in it.
