A path to cognition
Five major changes in the computational capacity of brains have led to the world of intelligent life around us.
That’s the conclusion of Professor Andrew Barron from Macquarie University with Dr Marta Halina from the University of Cambridge and Professor Colin Klein from the Australian National University (ANU), in a paper published today in Proceedings of the Royal Society B.
They say that one billion years of evolution has led to five fundamentally different types of brains, each suited to its purpose.
Their work suggests we have a long way to go before we can add AI to the list. And, as we develop autonomous machines, we can still learn from the coordination of a jellyfish, the single-mindedness of worms, the rapid thinking of bees, and the complex interactions of birds in flight.
Step one is a nervous system to coordinate actions. Jellyfish have diffuse neural networks that are great for coordinating a body and can survive massive damage. But these networks are really bad at putting information together.
Step two is a centralised nervous system with a brain that can act as a master coordinator and combine information from different senses. Think worms, leeches and tardigrades.
Step three is a brain with feedback (recurrence). Bees can quickly learn different types of art, recognise abstract concepts and navigate using brains that incorporate rapid feedback on actions.
Step four is a brain with multiple recurrent systems feeding back information with and between each system. This allows birds, rats and dogs to do massive parallel processing of information, using the same information multiple different ways at the same time and to recognise relationships between different types of information. It allows monkeys to problem solve and make rudimentary tools.
Step five is reflection. Our brains can modify their own computational structure according to what is needed. A reflective brain can learn the best information flow for a specific task and modify how it processes information on the fly to complete that task in the fastest and most efficient way.
The human brain is reflective, and it has enabled our imagination, our thought processes, and our rich mental lives. It also opened the door for the use of symbolic language, and that expanded our minds even further as it helped us communicate and coordinate so efficiently with each other.
Which brain is best?
“We like to claim we are the smartest animal,” says Professor Barron. “But a bee can do things we just cannot do. A bee is fully functional from the moment its wings dry as it emerges from its cell. It can learn to navigate for kilometres around its hive. I still get lost walking home from the train.
“A jellyfish or a worm might not be Einstein, but they can tolerate a level of damage that would kill or paralyse a mammal. Different types of brains suit animals to different lifestyles. This is why we still share the planet with jellyfish and worms that seem essentially unchanged for hundreds of millions of years. Their brain is perfect for what they need to do. And we can learn from them as we attempt to create new kinds of intelligence for autonomous machines and AI,” Professor Barron says.
The research was funded by the Templeton World Charity Foundation.
For Macquarie University: Kate Symons, firstname.lastname@example.org, +61 417 489 903
Niall Byrne, email@example.com, +61 417 131 977
Transitions in cognitive evolution
Andrew B. Barron1, Marta Halina2 and Colin Klein3
1School of Natural Sciences, Macquarie University Sydney, Sydney, New South Wales, Australia Q1
2Department of History and Philosophy of Science, University of Cambridge, Cambridge, UK
3School of Philosophy, The Australian National University, Canberra, Australian Capital Territory, Australia
The evolutionary history of animal cognition appears to involve a few major transitions: major changes that opened up new phylogenetic possibilities for cognition.
Here, we review and contrast current transitional accounts of cognitive evolution. We discuss how an important feature of an evolutionary transition should be that it changes what is evolvable, so that the possible phenotypic spaces before and after a transition are different.
We develop an account of cognitive evolution that focusses on how selection might act on
the computational architecture of nervous systems. Selection for operational efficiency or robustness can drive changes in computational architecture that then make new types of cognition evolvable.
We propose five major transitions in the evolution of animal nervous systems. Each of these gave rise to a different type of computational architecture that changed the evolvability of a lineage and allowed the evolution of new cognitive capacities.
Transitional accounts have value in that they allow a big-picture perspective of macroevolution by focusing on changes that have had major consequences.
For cognitive evolution, however, we argue it is most useful to focus on evolutionary changes to the nervous system that changed what is evolvable, rather than to focus on specific cognitive capacities.