In 2022, Daniel Kronauer and Asaf Gal at Rockefeller University built a system to watch an ant colony make a decision. They placed colonies on a heated platform and slowly raised the temperature. Individual ants felt the heat under their feet but carried on as usual—foraging, tending larvae, wandering with the vaguely purposeless energy of someone who forgot why they walked into a room. Then, at a specific temperature, the entire colony reversed course simultaneously. Every ant evacuated. The decision wasn’t made by any individual ant. It was made by the colony.
The expected finding: a colony of 36 workers evacuated reliably at about 34 degrees Celsius. The surprising finding: when Kronauer and Gal increased the colony from 10 to 200 individuals, the temperature required to trigger evacuation went up. Colonies of 200 held out past 36 degrees. No individual ant knows how many ants are in its colony. No ant has a thermometer or a headcount. And yet the group’s decision threshold shifted based on group size—a variable that no single member of the group can perceive.
The colony was behaving like a neural network. Not metaphorically. Structurally.
The superorganism concept
An ant colony is not a collection of individuals cooperating. It’s a single organism made of many bodies. The queen is the reproductive system. The workers are the immune system, the digestive system, the musculoskeletal system. The pheromone trails are the nervous system. No individual ant contains the information necessary to run the colony, just as no individual neuron contains the information necessary to produce a thought. The intelligence—such as it is—exists only at the level of the system.
This isn’t a cute analogy. Researchers at Arizona State University and other institutions have spent decades studying ant colonies using the same experimental methods psychologists use on individual animals—psychophysics, perceptual discrimination tasks, speed-accuracy tradeoff tests, rationality assessments—and finding that colonies exhibit cognitive properties that individual ants don’t possess. Takao Sasaki and Stephen Pratt published a comprehensive review in the Annual Review of Entomology in 2018 documenting the parallels: colonies balance speed against accuracy in decision-making using the same mathematical relationships that govern neural computation in brains. Colonies make better choices than individuals when the discrimination task is hard—a PNAS study demonstrated that colony-level decisions outperformed individual ant decisions on difficult sensory discrimination tasks but not on easy ones, the exact pattern you’d predict if the colony functions as a signal-averaging system that reduces noise through redundancy.
The superorganism concept, as Sasaki and Pratt frame it, isn’t an illustrative metaphor. It’s a research program. If a colony is functionally equivalent to an organism, then the tools developed for studying organisms should work on colonies. They do.
How decisions actually happen
Deborah Gordon, a biologist at Stanford who has studied ant behavior for over 30 years, describes the central puzzle: individual ants are, to put it charitably, not impressive. Watch a single ant trying to find food, and you’ll see an organism that frequently loses the trail, forgets which direction it was heading, and gets confused by a leaf. Gordon says she probably wouldn’t hire one. But thousands of these bumbling individuals collectively locate food sources, mobilize foraging parties, switch flexibly between tasks, defend the nest, and manage waste disposal—all without any centralized control, any chain of command, any ant that knows the plan.
The mechanism is local interaction. An ant doesn’t know what the colony needs. It knows what’s happening in its immediate vicinity—which other ants it’s bumped into recently, what pheromone concentrations it’s detecting, whether the ant it just touched with its antennae was carrying food or returning empty. From these local cues, each ant follows simple behavioral rules. The sophistication emerges from the interaction patterns, not from the individual agents.
Pheromone trails are the most studied example. When a forager finds food, it lays a chemical trail on the way back to the nest. Other ants that encounter the trail follow it to the food source and lay their own pheromone on the return trip. The trail gets stronger. More ants follow it. The trail gets stronger still. This is positive feedback—the same amplification mechanism that drives neural decision-making in brains. When two food sources exist, the colony will usually converge on one, not split evenly between both, because random early variation in ant traffic gets amplified by the feedback loop until one trail dominates. The colony has “decided” which food source to exploit, and no individual ant made that decision.
Nest site selection in Temnothorax ants is the most precisely documented example of collective decision-making. When a colony needs to relocate, scout ants explore candidate sites and assess them individually—cavity size, darkness, entrance width, structural integrity. A scout that finds a promising site recruits other scouts through tandem running, leading them to the site one by one. Once enough scouts accumulate at a site—a quorum threshold—the ants switch from slow tandem recruitment to rapid carrying, physically transporting the rest of the colony to the new home. The quorum threshold is the decision mechanism: it ensures that the colony doesn’t commit to a site until enough independent assessors have confirmed its quality. It’s a voting system that doesn’t require any ant to count votes.
Nigel Franks at the University of Bristol documented the speed-accuracy tradeoff in this system: colonies that use a lower quorum threshold decide faster but make worse choices. Colonies that use a higher threshold are slower but more accurate. The tradeoff is governed by the same mathematical relationships that describe speed-accuracy tradeoffs in primate neural decision-making. The ant colony and the primate brain are implementing the same algorithm using completely different hardware.
Where the analogy breaks
The superorganism framework is powerful but not unlimited. Colonies also encounter performance costs that individual organisms don’t. The same positive feedback that generates consensus can amplify errors—if early scouts happen to find a mediocre nest site first, the pheromone feedback can lock the colony into a suboptimal choice before better alternatives are discovered. Individual organisms can change their minds; colonies, once committed by positive feedback, have a harder time reversing course.
Gordon’s work emphasizes that the ant-colony-as-brain analogy, while productive, can overstate the degree of centralized computation involved. Ant colonies don’t have a dedicated processing center equivalent to a cortex. They operate through what Gordon calls “the ecology of collective behavior”—the interaction between the colony’s behavioral rules and the specific environmental context in which those rules play out. The same colony, using the same rules, produces different behaviors in different environments, just as the same neural architecture produces different outputs depending on sensory input. The intelligence isn’t in the rules. It’s in the fit between the rules and the world.
There are also roughly 14,000 species of ants, and they don’t all work the same way. Army ants conduct nomadic raids without stable nest sites. Leafcutter ants farm fungus in underground gardens. Harvester ants in the American Southwest manage foraging rates using interaction frequencies that Gordon has compared to TCP/IP internet protocols—the rate at which returning foragers contact outgoing foragers determines whether more foragers are sent out, the same feedback mechanism that regulates data transmission rates in computer networks. The superorganism concept applies broadly, but the specific implementations are as varied as the ecosystems ants occupy.
Why neuroscientists care about ants
The deep reason to study ant colonies isn’t entomological. It’s computational. The question that Kronauer’s evacuation experiment, Sasaki and Pratt’s psychophysics research, and Gordon’s decades of fieldwork all converge on is the same question that drives computational neuroscience: how does a system composed of simple, unreliable components produce complex, reliable behavior?
A neuron, like an ant, is not smart. It fires or it doesn’t. It has no concept of the thought it’s participating in. The intelligence of a brain, like the intelligence of an ant colony, is an emergent property of interaction patterns among components that individually can’t do much. The mathematical models that describe how ant colonies reach consensus—positive feedback, quorum thresholds, speed-accuracy tradeoffs, noise reduction through redundancy—are the same models that describe how populations of neurons reach decisions. The hardware is different. The computation is the same.
Kronauer’s evacuating ants couldn’t know how many of them there were, and yet their collective behavior changed as a function of colony size. The mechanism, he suspects, involves pheromone concentration: more ants produce more “stay” pheromone, which raises the temperature threshold for the “leave” signal to override the “stay” signal. It’s a chemical implementation of the same inhibition-excitation balance that governs decision thresholds in neural circuits. The colony isn’t thinking about whether to leave. It’s computing whether to leave, using the bodies and chemical secretions of its members as the processing substrate.
The ant that forgot why it walked into a room isn’t broken. It’s a single neuron in a brain that works just fine.
We cover ant superorganism intelligence alongside baboon politics, cuttlefish camouflage, and the full landscape of animal cognition across our Animal Culture & Knowledge course—including why the best model for understanding your brain might be 200 confused ants on a hot plate.