Last Modified December 2011.
Modelling Primate Intelligence and Social Behaviour

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Background & Funding
Learning and intelligence in primates (including humans) is
interesting from both a scientific and an engineering perspective,
because we primates learn more than other classes of
animals. The 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.
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.
The work on this page therefore covers both work on basic primate
cognition, general social behaviour (human, non-human primates,
and sometimes other species), specific human culture, and theories
of how human culture and intelligence came to be. My group
takes a computational perspective on both cognition and culture
(culture can be thought of as distributed cognition /
computation). We hope our results will not only inform the
science of natural intelligence, but also help us improve
artificial intelligence as well.
This research program began during my 2001 PhD
dissertation work on Behaviour
Oriented Design. Since 2002 I have been working with
colleagues in AmonI
studying the interaction between modularity, individual agent
learning, and intelligence provided by either evolution (in
nature) or a developer (in AI.) Note that in nature this
"provided" intelligence can be either genetic or memetic – it can
come either via biology or from culture.
Funding for this research:
General and Specialized Learning of Tasks
Q & A on this research.
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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. |
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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.' Papers about the monkey data are here,
look for "gravity" in the title. Explaining this data
has led to extending and generalising the two-tier model.
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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.
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.
Related publications:
- Joanna J. Bryson and Marc D. Hauser, ``What
Monkeys
See and Don't Do: Agent Models of Safe Learning in Primates'',
Proceedings of the AAAI Symposium on Safe Learning Agents,
M. Barley and H. W. Guesgen, eds., AAAI Press March 2002.
- Mark Wood, Jonathan C. S. Leong and Joanna J. Bryson, ``ACT-R
is almost a Model of
Primate Task Learning: Experiments in Modelling
Transitive Inference'', in Proceedings of the 26th
Annual Meeting of the Cognitive Science Society (CogSci 2004),
pp. 1470-1475.
- Joanna J. Bryson and Jonathan C. S. Leong ``Primate errors in
transitive `inference': A two-tier learning model'' Animal
Cognition, 10(1), 2007. Associated
software.
- Joanna J. Bryson, Age-Related
Inhibition and Learning Effects: Evidence from Transitive
Performance, to appear at Cognitive
Science 2009 in July. Same software as above.
- Modelling
Natural Action
Selection (Seth, Prescott & Bryson eds.) on Cambridge University
Press, 2012.
Evolving Social Behaviours
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These are pictures (which I got from the
Internet) of two different species of macaque
monkeys in social interactions. Different species of
macaques, despite being closely related, have different
sorts of social structures. Some, such as the rhesus
on the left, have very strict social structures and violent
but infrequent fights. Others, such as the stumptails
on the right, 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.
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Related publications:
- Joanna J. Bryson, ``Where
Should Complexity Go? Cooperation in Complex Agents with
Minimal Communication'', Innovative Concepts for
Agent-Based Systems, W. Truszkowski, C. Rouff and M.
Hinchey, eds., pp. 298-313, Springer, 2003.
- Joanna J. Bryson and Jessica C. Flack, ``Action Selection for an
Artificial-Life Model of Social Behavior in Non-Human Primates'',
Proceedings of the International Workshop on
Self-Organization and Evolution of Social Behaviour, C.
Hemelrijk ed., pp. 42-45 , 2002.
- Emmanuel Tanguy, Philip Willis and Joanna J. Bryson, ``Emotions
as
Durative Dynamic State for Action Selection'', in The
Twentieth International Joint Conference on Artificial
Intelligence (IJCAI), Hyderabad, India, pp.1537-1542,
January 2007. Associated
software.
- Mark A. Wood and Joanna J. Bryson, ``Skill Acquisition through
Program-Level Imitation in a Real-Time Domain'', IEEE
Transactions on Systems, Man and Cybernetics Part
B--Cybernetics, 37(2):272-285,
April 2007.
- Joanna J. Bryson, Yasushi Ando and Hagen Lehmann ``Agent-based models as
scientific methodology: A case study analysing primate social
behaviour'', Philosophical Transactions of the Royal
Society, B - Biology,
362(1485):1685-1698, September 2007. The case
analysed in this paper concerns Hemelrijk's
DomWorld, that link includes the associated software.
- Philipp Rohlfshagen
and
Joanna J. Bryson, Flexible
Latching:
A
Biologically-Inspired
Mechanism
for Improving the
Management of Homeostatic Goals in Cognitive
Computation 2(3):230-241 2010. Associated
software comes with the standard
python/jython
distribution of BOD.
- Gideon M. Gluckmann & Joanna J. Bryson, An
Agent-Based Model of the Effects of a Primate Social Structure
on the Speed of Natural Selection, in Evolutionary
Computation and Multi-Agent Systems and Simulation (ECoMASS)
at GECCO 2011 in Dublin.
Evolving Human-Like
Culture
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Individuals of most social
species (even guppies) keep track of how their group-mates
have treated them in the past.
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Primates appear to also keep
track of how their troop-mates treat each other. This
takes much more memory, and possibly compositional
reasoning.
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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. For references and
evidence, see the papers below:
- 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.
Related publications:
- Ivana Cace and Joanna J. Bryson, ``Agent
Based Modelling of Communication Costs: Why Information Can Be
Free.'', in Emergence and Evolution of Linguistic
Communication C. Lyon, C. L Nehaniv and A. Cangelosi,
eds., pp. 305-322, Springer 2007.
- Steven Butler and Joanna J. Bryson ``Effects
of Mass Media and Opinion Exchange on Extremist Group
Formation'', in The Proceedings of the Fourth
Conference of the European Social Simulation Society (ESSA
'07), Toulouse, France, pp. 455-465 2007. Associated
software.
- Joanna J. Bryson ``Embodiment
vs. Memetics'', Mind
& Society, 7(1):77-94, June 2008.
- Avri Bilovich and Joanna J. Bryson, Detecting the Evolution
of Semantics and Individual Beliefs Through Statistical
Analysis of Language Use, Proceedings of the Fall AAAI
Symposium on Naturally-Inspired
Artificial
Intelligence, Washington DC, November 2008. Associated
software.
- Joanna J. Bryson, The Role of
Modularity in Stabilizing Cultural Evolution: Conformity and
Innovation in an Agent-Based Model, Proceedings of the
Fall AAAI Symposium on Adaptive
Agents
in Cultural Contexts (AACC ’08), Washington DC, November
2008. Associated
software.
- Joanna J. Bryson ``Representations
Underlying Social Learning and Cultural Evolution'', Interaction
Studies, 10(1):77-100,
March 2009.
- Joanna J. Bryson, ``Cultural
Ratcheting Results Primarily from Semantic Compression''.
The Proceedings of Evolution
of Language 2010, Smith,
Schouwstra, de Boer & Smith (eds.) pp. 50-57.
Software for simulations in the above articles is available from the AmonI
software page.
page author: Joanna Bryson
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