Understanding Natural Intelligence

Natural intelligence (NI) is the opposite of artificial intelligence: it is all the systems of control present in biology.  Normally when we think of NI we think about how animal or human brains function, but there is more to natural intelligence than neuroscience.  Nature also demonstrates non-neural control in plants and protozoa, as well as distributed intelligence in colony species like ants, hyenas and humans.  Our behaviour co-evolves with the rest of our bodies, and in response to our changing environment.  Understanding natural intelligence requires understanding all of these influences on behaviour and their interactions.

One of the best methods for understanding how NI systems work is to try to replicate their behaviour in simulation.  Just as learning to paint forces you to understand the details of what you are seeing, building a working model forces you to understand the intricacies of what the target intelligent system is doing.  For example:

An AI model of an organism is a very-well-specified hypothesis about how that organism thinks and behaves.  Like any hypothesis, we assess an AI model by testing its predictions against the performance of the real system and by evaluating the plausibility of its assumptions. The predictions of a model are its behaviour, which we simply record after running simulations.  Its assumptions are its components; for example, the computations it makes, the information it has access to, the things it perceives and remembers.  We can use standard statistical tests to see how close we come to modelling behaviour in order to argue the validity of our assumptions.

The Artificial models of natural Intelligence (AmonI) group at Bath is dedicated to understanding natural intelligence.  Building AI models of NI systems requires designing intelligent systems.

Below are selected publications of mine contributing to understanding NI.  For full references and a complete list of publications, see my publications page. My main current research is on identity, social structure and public goods investment, which that page just linked goes into detail.  For focused lists concerning task learning, the evolution of primate social structure and the evolution of culture, see my understanding primate intelligence web page.  For a general overview of my views on natural intelligence and how it relates to artificial intelligence, see this book chapter:
  • Structuring Intelligence: The Role of Hierarchy, Modularity and Learning in Generating Intelligent Behaviour, a chapter in McFarland, D., Stenning, K. and McGonigle-Chalmers, M. (eds.) The Complex Mind, Palgrave MacMillan 2012.
  • Papers primarily concerning NI:
  • Paul Rauwolf & I, Expectations of Fairness and Trust Co-Evolve in Environments of Partial Information, in Dynamic Games and Applications 8(4):891-917.
  • Rob Wortham and Joanna J. Bryson, Communication (open access version).  In Living Machines: A Handbook of Research in Biomimetic and Biohybrid Systems, Prescott and Verschure, eds, Oxford University Press.
  • Aylin Caliskan, Joanna J. Bryson, & Arvind Narayanan, Semantics derived automatically from language corpora contain human biases. Science 356 (6334):183-186 [14 Apr 2017].  Be sure to also look at the supplement, which gives the stimuli and shows similar results for a different corpus and word-embedding model.  Open access version: authors' final copy of both the main article and the supplement.
  • Elizabeth M. Gallagher and Joanna J. Bryson, Agent-Based Modelling, in the "continuously updating" Encyclopedia of Animal Cognition and Behavior, Springer, 2017.  Open access version, authors' final copy.
  • Artificial Intelligence and Pro-Social Behaviour from the October 2015 Springer volume, Collective Agency and Cooperation in Natural and Artificial Systems: Explanation, Implementation and Simulation derived from Catrin Misselhorn's 2013 meeting, Collective Agency and Cooperation in Natural and Artificial Systems.
  • Paul Rauwolf, Dominic Mitchell, and Joanna J. Bryson, Value homophily benefits cooperation but motivates employing incorrect social information, Journal of Theoretical Biology 367:246–261.
  • Dominic Mitchell, Joanna J. Bryson, Paul Rauwolf, and Gordon Ingram, On the reliability of unreliable information: Gossip as cultural memory, in Interaction Studies17(1):1–25.
  • Yifei Wang, Yinghong Lan, Daniel Weinreich, Nick Priest and I, Recombination Is Surprisingly Constructive for Artificial Gene Regulatory Networks in the Context of Selection for Developmental Stability,  in the proceedings of The 13th European Conference on Artificial Life, July 20-24 2015, York, UK.
  • Daniel J. Taylor and Joanna J. Bryson, Replicators, lineages, and interactors, Behavioral and Brain Sciences, Volume 37, Issue 03, June 2014, pp 276-277, a commentary on Paul Smaldino's The cultural evolution of emergent group-level traitsOpen access version.
  • Karolina Sylwester, Benedikt Herrmann, and Joanna J. Bryson, Homo homini lupus? Explaining antisocial punishment. In Journal of Neuroscience, Psychology, and Economics, 6(3):167-188. Green open access: revised final version submitted to the publisher (May 2013).
  • Joanna J. Bryson, James Mitchell, Simon T. Powers, and Karolina Sylwester, Understanding and Addressing Cultural Variation in Costly Antisocial Punishment.  To appear in Applied Evolutionary Anthropology, Gibson & Lawson (eds.), Springer.  Revised version from May 2013.
  • The Role of Stability in Cultural Evolution: Innovation and Conformity in Implicit Knowledge Discovery, book chapter in Perspectives on Culture and Agent-Based Simulations, Virginia and Frank Dignum, (eds), Springer, Berlin 2014.
  • Simon T. Powers, Daniel J. Taylor and Joanna J. Bryson, Punishment can promote defection in group-structured populationsThe Journal of Theoretical Biology, 311:107-116.  Archived preprint.  More on Costly Punishment.
  • Yifei Wang, Stephen G. Matthews and Joanna J. Bryson, Evolving Evolvability in the Context of Environmental Change: A Gene Regulatory Network (GRN) Approach, Artificial Life 2014.
  • Eugene Y. Bann and Joanna J. Bryson, Measuring Cultural Relativity of Emotional Valence and Arousal using Semantic Clustering and Twitter, Proceedings of Cognitive Science.
  • Eugene Y. Bann and Joanna J. Bryson, The Conceptualisation of Emotion Qualia: Semantic Clustering of Emotional Tweets, Proceedings of the Thirteenth Neural Computation and Psychology Workshop (NCPW), (2012).   More on Emotions.
  • Harvey Whitehouse, Ken Kahn, Michael E. Hochberg, and Joanna J. Bryson, The role for simulations in theory construction for the social sciences: Case studies concerning Divergent Modes of Religiosity, Religion, Brain & Behaviour, 2(3):182-224 (including commentaries and response,) 2012.
  • A Role for Consciousness in Action Selection in the International Journal of Machine Consciousness 4(2):471-482, 2012.
  • Book:   Modelling Natural Action Selection (Seth, Prescott & Bryson, eds.) on Cambridge University Press (November, 2011).
  • Cultural Ratcheting Results Primarily from Semantic Compression.  From The Proceedings of Evolution of Language 2010, Smith, Schouwstra, de Boer & Smith (eds.) p. 50-57.  Discriminates the size of a culture (how much information can be transmitted from one generation to the next) from its extent (how much useful behaviour can be generated from culture). Argues the majority of cultural ratcheting is due to cultural evolution increasing the extent.
  • Age-Related Inhibition and Learning Effects: Evidence from Transitive Performance, in the proceedings of Cognitive Science 2009.
  • Agent-based models as scientific methodology: A case study analysing primate social behaviour, with Yasushi Ando and Hagen Lehmann.  In Philosophical Transactions of the Royal Society - B, Biology 362(1485):1685-1698, September 2007.  This paper talks about how ABM fits in as a part of scientific methodology, and in particular analyses macaque social structure in the DomWorld model of Charlotte Hemelrijk. Penultimate version in case you don't have access to PTRS-B.  The case analysed in this paper concerns Hemelrijk's DomWorld, that link includes the associated software.
  • Tony J. Prescott,  Joanna J. Bryson and Anil K. Seth, Introduction. Modelling Natural Action Selection in Philosophical Transactions of the Royal Society, B -- Biology.  This is actually quite a substantial article which covers the concept of action selection.
  • Primate errors in transitive `inference': A two-tier learning model, with Jonathan C. S. Leong.  In Animal Cognition 10(1):1-15, January 2007.   A model of transitive inference as the implicit learning of relationships between context-action pairs.  Associated software.
  • Representations Underlying Social Learning and Cultural Evolution.  In Interaction Studies, 10(1):77-100 (2009). See also the 2007 target article of a similar name (and discussion) in the web magazine interdisciplines' on-line conference, Adaptation and Representation.
  • Embodiment vs. Memetics.  Discusses the importance of the discovery that human-like semantics can be learned simply from observing large corpora, with ramifications for the evolution of language.  In Mind & Society 7(1), June 2008 (online now).  Draft from August 2007 in case you don't subscribe to M & S.
  • Ivana Cace and I, Agent Based Modelling of Communication Costs: Why Information Can Be Free, in Emergence of Communication and Language on Springer, edited by Caroline Lyon, Chrystopher L. Nehaniv and Angelo Cangelosi. Shows that the tendency to communicate information can be adaptive even though it has immediate costs to the communicators and there are free riders / information hoarders around the place. This is a draft version from early March 2006.
  • Modular Representations of Cognitive Phenomena in AI, Psychology and Neuroscience (in HTML or PDF) in Visions of Mind, Darryl Davis ed. (2004)
  • ACT-R is almost a Model of Primate Task Learning:  Experiments in Modelling Transitive Inference, coauthored with Mark Wood and Jonathan Leong, from Cognitive Science 2004.
  • Language Isn't Quite That Special (HTML). Commentary on The cognitive functions of language by  Peter Carruthers, both in Behavioral & Brain Sciences (BBS) December 2002.
  • What Monkeys See and Don't Do: Agent Models of Safe Learning in Primates (in pdf), with Marc D. Hauser. A position paper. In the proceedings of the AAAI Spring Symposium on Safe Learning Agents.(2002).
  • Intelligent Control Requires More Structure than the Theory of Event Coding Provides (HTML). Commentary on The Theory of Event Coding: A Framework for Perception and Action Planning by Bernhard Hommel, Jochen Müsseler, Gisa Aschersleben and Wolfgang Prinz, both appeared in Behavioral & Brain Sciences (BBS).  (2001)
  • Modularity and Specialized Learning in the Organization of Behavior in pdf (or postscript). Written with Lynn Andrea Stein. From NCPW6, the final version is © Springer-Verlag.
  • The Study of Sequential and Hierarchical Organisation of Behaviour via Artificial Mechanisms of Action Selection.  MPhil Dissertation: University of Edinburgh, Faculty of Social Sciences (Department of Psychology) 2000
  • Cognition without Representational Rediscription coauthored with Will Lowe. This is a commentary on Dana H. Ballard, Mary M. Hayhoe, Polly K. Pook, and Rajesh P. N. Rao, Deictic Codes for the Embodiment of Cognition; both articles appeared in Behavioral & Brain Sciences (BBS) (1997)

  • Joanna  Bryson
    Last updated 15 June 2019.