Learning and intelligence in humans and other primates is interesting from both a scientific and an engineering perspective because we primates learn more than other classes of animals. Our understanding of natural intelligence is enhanced when we build models of it because we can test whether our theories really generate the behaviour we predict, and whether that matches what we see in nature. If we don’t understand the biological origins of cognition, then we can’t really understand what computation is for, how it benefits an individual or a population. Without understanding this, we can’t say what AI should look like, nor what the appropriate role is of AI in society. The work on this page ranges from basic primate cognition and task learning, through general social behaviour, and into the specifics of human culture and its origins. My group takes a computational perspective on both cognition and culture: culture can be thought of as cognition/computation distributed across a population.
Non-human primates are a little easier to model than humans because:
We have more complete data about how they spend their time. Non-human primates don’t seem to mind being observed every minute of the day (provided the observers are familiar and well-behaved) so we can get the kind of complete, quantitative statistical data on their social interactions that is impossible to get from humans.
Non-human primates acquire significantly less behaviour culturally, partly because they don’t have language. That means their behaviour changes more slowly, so it is easier to keep up with long enough to model.
This research program began during my 2001 PhD dissertation work on Systems AI. Since 2002, I have been working with colleagues in AmonI studying the interaction between individual learning and thinking, and the intelligence provided by either evolution (in nature) or a developer (in AI). Note that in nature “provided” intelligence can come from genetics or memetics — it can come either via biology or from culture. AI too increasingly mines our biology and culture for intelligence, but it has the advantage of human programmers as well.
General and Specialized Learning of Tasks
These are pictures of a monkey working in a test apparatus on transitive inference (TI), the first task learning we’ve modelled. Pictures and original data come from Brendan McGonigle, collected at his lab in Edinburgh in the 1970s. The subject is a Squirrel Monkey, Saimiri sciureus. TI is a much-researched task and serves well as a benchmark for theories of skill-learning. Originally it was thought only humans can do it, but now we know even rats and pigeons can, although they seem to do it differently from primates. Like most learning tasks, the best way to tell which theory is right is to look at how well they account for the mistakes the subjects make. This research led me to build a two-tier model of TI learning.
A monkey working on a puzzle at the Primate Cognitive Neuroscience Laboratory at Harvard. This participant is a Cotton-Top Tamarin, Saguinus oedipus. The tamarin is trying to figure out Bruce Hood’s tube task, another puzzle originally given to children. Despite the fact the food reliably goes down the tube, monkeys and small children keep expecting it will fall straight down. On the other hand, monkeys can learn this task if the apparatus is placed horizontally. This has led to the theory that their mistakes are a ‘gravity fallacy.’ Explaining this data has led to extending and generalising the two-tier model.
See a Q&A on this research →
Note: None of the animals in the above pictures live in their test apparatus! Monkeys only participate in behavioural tests like these if they enjoy it — otherwise they refuse to work and there is nothing that can make them pay attention. This does occasionally happen, for example if there has been a big political disruption the previous day in the monkey colony (two monkeys fought or befriended each other) in which case they temporarily lose interest in anything else. If you are worried about the ethics of primate research, you might want to read Why Primate Models Matter.
At least part of the reason the monkeys enjoy going to testing rooms is because they know it is a good place to get treats (peanuts for the squirrel monkeys, bits of Fruit Loops for the tamarins). But many monkeys seem to think puzzles are intrinsically interesting and will play with them for a while at least even for no reward.
Evolving Social Behaviours
Different species of macaques, despite being closely related, have different sorts of social structures. Some, such as the rhesus, have very strict social structures and violent but infrequent fights. Others, such as the stumptails, have more egalitarian social structures with frequent scuffles but few very violent incidents.
The original goal of our research was to examine two conflicting theories of why this might be. Charlotte Hemelrijk believes it is because the more structured species evolved in more difficult climates with scarcer resources, leading to more violent conflicts. More violent conflicts in turn led to more structured societies. Frans de Waal believes that more egalitarian species have learned or evolved more social behaviours that help reduce the seriousness of conflict. Thus, violence is a consequence of species-wide behavioural ignorance. Carel van Schaik, among others, thinks that different social structures are responses to different environmental opportunities and threats — this is called the socio-ecological theory. Others like Bernard Thierry think the differences are the result of chance events over their phylogenetic history.
Charlotte Hemelrijk already has a well-published AI model she used to try to demonstrate her model could be plausible. However, we’ve replicated Hemelrijk’s DomWorld model (click there for more details including our code), and found it was less applicable than she has said. Hagen Lehmann did most of this work for his PhD, and has also built a model of the socio-ecological model, which we are testing.
Key Related Publications:
Evolving Human-Like Culture
Scientists, philosophers, and of course many ordinary people have long wondered about what makes us special — well, really what makes me special (where me is each of us), but from that, my planet, my country, my species.
Although we probably share more of our intelligence and motivation with related species than we realise, there is no question that contemporary human lives are really quite different from the lives of other animals. We are the only ones with such elaborate and varied artifacts like buildings and laptop projectors, and we are the only ones who transmit behaviour via language. We are also different from other species in a large number of other ways. But the question is, which difference(s) came first? Science favours parsimonious answers, so we are looking for just one or a few simple differences between us and other species that might explain all the other differences.
I became professionally involved in these questions through attending the Evolution of Language conferences. Originally I did this just because it was such an interesting and interdisciplinary group of scientists, not because I was interested in language origins. But I came to realise that understanding the social transmission of behaviour was fundamental to understanding intelligence. Consequently my hobby changed into the main topic of my two-year research sabbatical in 2007-2009.
Here are my current understandings of the issue:
The altruistic communication of behaviour is easy to evolve.
Species are cultural (that is, they communicate behaviour by means other than reproduction) broadly to the extent that they are cognitive. That is, if they learn and think at all, they are very likely to exploit the learning and thinking of their conspecifics.
The reason many species have neither culture nor cognition is because learning is slow, unreliable and costly. The reason some species do have it is because individual plasticity can accelerate biological evolution, thus producing adaptive tradeoffs. Adaptive tradeoffs in turn produce species-level variation.
In humans, cultural evolution happens faster so we use it more. This is because language allows both faster transmission of ideas and cognitive compression of concepts into simpler and more manipulable representations.
We evolved language in the first place because we happened to be the only species to combine two or a few useful traits:
- The ability of perfect, temporally precise imitation. This probably evolved due to sexual selection for vocal imitation, as it has in other species. This gives us a representational substrate rich enough in information to provide robust, redundant cues to meaning, thus allowing an unsupervised learning process like evolution to operate. I’m sure this was essential.
- The ability for compositional reasoning. This ability co-evolved with our complex social structure, and we share it with other higher primates. However, no other higher primates happen to be able to do vocal imitation. The compositional capacity in humans allows the compositional (recursive) structure of language, which gives it much of its power to overcome combinatorial complexity. I’ve written a few papers about this, but I am also entertaining a simpler hypothesis right now …
- The ability to remember a lot of stuff. Apes have long lives and big heads, presumably in order to keep track of their social affiliations and their vast and creative set of feeding strategies. We and our ancestors may be the only vocal imitators with enough individual “work space” for cultural evolution to have generated such an efficient representation as language.
Key Related Publications:
Note that the above work has recently started influencing my AI ethics work, notably:
Artificial Intelligence and Pro-Social Behaviour
Patiency Is Not a Virtue: The Design of Intelligent Systems and Systems of Ethics
Software for simulations in the above articles is available from the AmonI software page
Funding
The NSF Biological Information Technology & Systems (BITS) initiative provided a postdoc for Joanna Bryson with Marc Hauser at Harvard’s Primate Cognitive Neuroscience Laboratory in their Department of Psychology.
The EPSRC Adaptive and Interactive Behaviour of Animal and Computational Systems (AIBACS) programme has provided a PhD studentship for Hagen Lehmann with Joanna Bryson in modelling macaque social behaviour.
The British Council’s Alliance: Franco-British Partnership Programme supported travel for Hagen & Joanna between our lab and the Centre d’Ecologie et Physiologie Energétiques in partnership with Bernard Thierry.
The Konrad Lorenz Institute for Evolution & Cognition Research partially funded a sabbatical for Joanna through their Hans Przibram fellowship, to study the biological evolution of cultural evolution. Further support was provided by the University of Bath sabbatical programme.
The US Air Force Office of Scientific Research, Air Force Material Command, funded research in using social simulation to understand cultural variation in contemporary human anti-social economic behaviour.