Lecturer: Bing W. Brunton Fields: Computational neuroscience, Neuoengineering, Data Science
Content
Discoveries in modern neuroscience are increasingly driven by quantitative understanding of complex data. The work in my lab lies at an emerging, fertile intersection of computation and biology. I develop data-driven analytic methods that are applied to, and are inspired by, neuroscience questions. Projects in my lab explore neural computations in diverse organisms. We work with theoretical collaborators on developing methods, and with experimental collaborators studying insects, rodents, and primates. The common theme in our work is the development of methods that leverage the escalating scale and complexity of neural and behavioural data to find interpretable patterns.
Lecturer
Bing Brunton is the Washington Research Foundation Innovation Associate Professor of Neuroengineering in the Department of Biology. She joined the University of Washington in 2014 as part of the Provost’s Initiative in Data-Intensive Discovery to build an interdisciplinary research program at the intersection of biology and data science. She also holds appointments in the Paul G. Allen School of Computer Science & Engineering and the Department of Applied Mathematics. Her training spans biology, biophysics, molecular biology, neuroscience, and applied mathematics (B.S. in Biology from Caltech in 2006, Ph.D. in Neuroscience from Princeton in 2012). Her group develops data-driven analytic methods that are applied to, and are inspired by, neuroscience questions. The common thread in this work is the development of methods that leverage the escalating scale and complexity of neural and behavioural data to find interpretable patterns. She has received the Alfred P. Sloan Research Fellowship in Neuroscience (2016), the UW Innovation Award (2017), and the AFOSR Young Investigator Program award (2018) for her work on sparse sensing with wing mechanosensory neurons.
Insects are often thought to show only fixed ‘robotic’ behaviours but in fact exhibit substantial flexibility, from maggots exploring their world to find which odours signal risk or reward, to ants and bees discovering and efficiently navigating between food sources scattered over a large environment. Yet insects also have small brains, providing the promise that we may be able to understand and model these aspects of intelligent behaviour down to the single neuron level. This course will describe the current state of research in insect exploration, emphasising an explicitly mechanistic view of explanation: to understand a system, we should (literally) try to build it. The final lecture will reflect on this methodology of modelling and what we can learn by implementing biological explanations as robots.
Session 1: Exploration in maggots, and the role of the body in behaviour.
Session 2: The neural basis of risk and reward in insect learning.
Session 3: Expert insect navigators – how do they discover and remember key locations in their world?
Session 4: Satisfying our own curiosity: using robots as models
Objectives
Understand the importance of linking brain, body and environment to explain behaviour.
Gain knowledge of current models of the neural mechanisms of exploration and learning in insects, and the key open questions.
Explore the role of (robot) models in scientific explanation
Barbara Webb completed a BSc in Psychology at the University of Sydney then a PhD in Artificial Intelligence at the University of Edinburgh. Her PhD research on building a robot model of cricket sound localization was featured in Scientific American. This established her as a pioneer in the field of biorobotics – using embodied models to evaluate biological hypotheses of behavioural control. She has published influential review articles on this methodology in Behavioural and Brain
Sciences, Nature, Trends in Neurosciences and Current Biology. In the last ten years the focus of her research has moved from basic sensorimotor control towards more complex insect behavioural capabilities, in the areas of associative learning and navigation. She has held lectureships at the University of Nottingham and University of Stirling before returning to a faculty position in the School of Informatics at Edinburgh in 2003. She was appointed to a personal chair as Professor of Biorobotics in 2010.
Curiosity has been described as an important driver for learning from infancy onwards. But what is curiosity? How has it been conceptualized, and how has its role in infant learning been identified and characterized? This course will describe the main theories of what curiosity is and how it affects behaviour, and how recent developmental research has studied curiosity in infants and children. Here I will address children’s active role in their learning and in their language development, as well as their preference for specific types of information. I will also touch on the role of play in infant and child development. Computational modelling can help us to develop theories of the mechanisms underlying curiosity-based exploratory behaviour, and I will discuss some of these models.
This course does not require any prior knowledge and all topics will be introduced gently.
Objectives
At the end of this course you will be able to
describe the major theories of curiosity
explain how scientists conduct studies with infants
describe the research done with infants and children on curiosity-based learning
explain principles of computational modelling and some relevant models of curiosity-based learning
Literature
Bazhydai, M., Twomey, K. E., & Westermann, G. (in press). Exploration and curiosity. In J. B. Benson (Ed.), Encyclopedia of Infant and Early Childhood Development, 2nd ed.
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013). Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends in Cognitive Sciences, 17(11), 585–593. http://doi.org/10.1016/j.tics.2013.09.001
Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75–98. http://doi.org/10.1037/0033-2909.116.1.75
Lecturer
Gert Westermann studied Computer Science in Braunschweig and Austin, TX, and received a PhD in Cognitive Science from the University of Edinburgh. After postdocs at the Sony Computer Science Lab in Paris and at Birkbeck College, London, he worked at Oxford Brookes University for several years and in 2011 joined Lancaster University as Professor of Psychology.
Gert is Director of the Leverhulme Trust Doctoral Scholarship Centre on Interdisciplinary Research in Infant Development which trains 22 PhD students on infancy research, and co-director of the ESRC International Centre for Language and Communicative Development which is a large-scale collaboration between the Universities of Manchester, Liverpool and Lancaster.
Gert’s research takes an interdisciplinary approach, combining looking time, pupil dilation, ERP, fNIRS, and behavioural studies with computational modelling to investigate the early cognitive, social and language development in infancy, with a recent focus on curiosity-based learning.
Until quite recently, AI was a scientific discipline defined more by its portrayal in science fiction than by its actual technical achievements. Real AI systems are now catching up to their fictional counterparts, and are as likely to be seen in news headlines as on the big screen. Yet as AI outperforms people on tasks that were once considered yardsticks of human intelligence, one area of human experience still remains unchallenged by technology: our sense of humour.
This is not for want of trying, as this course will show. The true nature of humour has intrigued scholars for millennia, but AI researchers can now go one step further than philosophers, linguists and psychologists once could: by building computer systems with a sense of humour, capable of appreciating the jokes of human users or even of generating their own, AI researchers can turn academic theories into practical realities that amuse, explain, provoke and delight.
The course will comprise four lectures, which will explore the following topics.
Newspaper personal columns are routinely filled with people seeking partners with a good sense of humour (GSOH), with many rating this as highly as physical fitness or physical appearance. Yet what does it mean to have a sense of humour? Conversely, what does it mean to have NO sense of humour, and how might we imbue a humorless machine with a capacity for wit and a flair for the absurd? We begin by unpacking these questions, to suggest some initial answers and models.
So, for example, what would it mean for a computer to have a numeric humour setting, as in the case of the robot TARS in the film Interstellar? Can a machine’s sense of humour be reduced to a single number or parameter setting? Is humour a modular ability? Can it be gifted to computers as a bolt-on unit like Commander Data’s “humour chip” in Star Trek, or is it an emergent phenomenon that arises from complex interactions among all our other faculties? Might humour emerge naturally within complex AI systems without explicitly being programmed to do so, as in the mischievous supercomputer Mike in Robert Heinlein’s The Moon is a Harsh Mistress or in the sarcastic droid K2SO in Rogue One: A Star Wars story?
This course will survey and critique the competing humor theories that scholars have championed through the ages, enlarging on recurring themes (incongruity, relief, superiority) while considering the amenability of each to computational modeling. What is it that these theories are really explaining, and which comes closest to capturing the elusive essence of humour?
The centrality of incongruity in modern theories demands that this concept be given a special focus. So we will unpack its many meanings to show how our understanding of incongruity can be as multifaceted as the idea of humour itself. Popular myths about the brittleness of machines in the face of the incongruous and the unexpected will be unpicked and debunked as we explore how machines might deliberately seek out and invent incongruities of their own.
But computational humour is still in its infancy, and it is no coincidence that the mode of humour for which machines show the greatest aptitude is that which humans embrace at a very early age, puns. Puns vary in wit and sophistication, but the simplest require only an ear for sound similarity and a disregard for the consequences of replacing a word with its phonetic doppelganger. The challenge for AI systems is to progress, as children do, from these simple beginnings to modes of ever greater conceptual sophistication.
To do so, is it possible to capture the essence of jokes in a mathematical formula, much as physicists have done for electromagnetism and gravity? Do jokes have quantifiable features that we and our machines can intuitively appreciate? Can we build statistical models to characterize the signature qualities of a humorous artifact, so that machines can learn to tell funny from unfunny for themselves? And what do these measurable qualities say about humour and about us?
Finally, however we slice it, conflict sits at the heart of humour, whether it is a conflict in meaning, attitude, expectation or perspective. Double acts personalize this conflict by recognizing the different roles a comic can play. Computers can likewise play multiple rules in the creation of humour, from rigid “straight man” to absurdist provocateur, in double-acts with humans and with other machines. So we will explore the ways in which smart machines can contribute to the social emergence of humour, either as embodied robots or disembodied software.
Objectives
This course will use the ideas and achievements of AI to explore what it means to have a sense of humour, and moreover, to understand what it is to not have one. It will challenge the archetype of the humorless machine in popular culture, to celebrate what science fiction gets right and to learn from what it gets wrong. It will make a case for the necessity of a computational understanding of humour, to better understand ourselves and to better construct machines that are more flexible, more understanding, and more willing to laugh at their own limitations.
Literature
The SEEKING mind: Primal neuro-affective substrates for appetitive incentive states and their pathological dyComputational humour studies is an established field that has produced a range of academic books, from Victor Raskin’s Semantic Mechanisms of Humor (1985, one of the first) to the more recent Primer of Humour Research (with chapters from computational humorists). Non-computational humor researchers, such as Elliott Oring, have also written accessible books on humour, such as Engaging Humor, while the computer scientist Graeme Ritchie has written a pair of well-received academic books on humour. Comedians and comedy professionals have also written some noteworthy books on humour, with individual chapters that focus on computational humour or that offer algorithmic insights into the author’s own comedy production strategies. Toplyn’s Comedy Writing for Late-Night TV offers a beginner’s guide to humor production that is frequently schematic in style. Jimmy Carr and Lucy Greeves’ The Naked Jape considers humour more broadly, but also offers a chapter on computational models and the people who build them. I will quote from each of the sources as needed.
Lecturer
Tony Veale is an associate professor in the School of Computer Science at UCD (University College Dublin), Ireland. He has worked in AI research for 25 years, in academia and in industry, with a special emphasis on humour and linguistic creativity. He is the author of the 2012 monograph Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity (from Bloomsbury), co-author of the 2016 textbook, Metaphor: A Computational Perspective (Morgan Claypool), and co-author of 2018’s Twitterbots: Making Machines That Make Meaning (MIT Press). He led the European Commission’s coordination action on Computational Creativity (named PROSECCO), and collaborated on international research projects with an emphasis on computational humour and imagination, such as the EC’s What-If Machine (WHIM) project. He runs a web-site dedicated to explaining AI with humour at RobotComix.com. He is active in the field of Computational Creativity, and is currently the elected chair of the international Association for Computational Creativity (ACC).
In this focused course, we will cover some of the many ways in which stress and arousal can modulate behaviors related to curiosity, risk, and reward through the interactions between physiological, affective, and cognitive processes. We will use the ACT-R/Phi architecture to think about this from a perspective of interacting mind and body processes. The Project Malmo (Minecraft) environment will also be used to show how we might implement some of the theoretical accounts as simulated agents in a virtual environment.
Session 1: Theoretical Background
Session 2: Short Recap, Theoretical Background, and Cognitive Architectures (general)
Session 3: Short Recap, Cognitive Architectures (general), and ACT-R/Phi Background
Session 4: Short Recap, ACT-R/Phi, and Using Project Malmo with Cognitive Architectures to study interactions between arousal, curiosity, risk, and reward
Objectives
Learn some a theoretical background on connections between memory systems, stress, arousal, and curiosity
Learn some background on some cognitive architectures
Learn about ACT-R/Phi
Learn about the Project Malmo Environment (Minecraft)
Learn about how one might create cognitive agents to run in Project Malmo
(Some are meant to be hands on, but we’ll work with what we can if some don’t have a computer!)
Could also read Pankepp & Biven (The Archaeology of Mind: Neuroevolutionary Origins of Human Emotions) as a supplement, but it is a book, so I would say it is a useful read if you find some of the above articles interesting.
Lecturer
Christopher L. Dancy received a B.S. in Computer Science, in 2010, and Ph.D. in Information Sciences and Technology, with a focus on artificial intelligence and cognitive science, in 2014, both from The Pennsylvania State University (University Park). He is an assistant professor of computer science at Bucknell University. His research involves the computational modeling of physiological, affective, and cognitive systems in humans. He studies how these systems interact, what these interactions mean for human-like intelligent behavior and interaction between humans and artificial intelligent systems. His work has been funded by National Science Foundation, US Office of Naval Research, US Army Research Lab, and The Social Science Research Council. Chris Dancy has previously chaired the Behavior Representation in Modeling and Simulation Society and is currently a member of ACM, AAAI, the Cognitive Science Society, National Society of Black Engineers, IEEE SMC, and the IEEE Computer Society.
Curiosity entails being able to delve into the unknown, to challenge habits of thinking, of acting, of reacting, of perceiving, – of sense-making. We can decide to let ourselves be challenged, we can seek uncertainty and the risk of not knowing what will come. This for example happens, when we try out a new physical activity we don’t master yet, e.g. an adult decides to learn to ride a horse. But it also happens, when we expose ourselves to art, challenging our patterns of perceiving.
In such situations we often loose and gain or regain stability. We thereby learn. Accepting moments of instability and uncertainty as part of each learning process can provide insight and even lead to experiencing such situations as pleasurable and rewarding.
Which preconditions and circumstances have to be met in order to develop an attitude of openness, of giving up anticipation and prediction, of letting go and letting come?
Becoming aware of our own patterns of moving, of perceiving, of relating to the world is one necessity. Establishing an atmosphere of trust, where “mistakes” are invited, another.
In this course we aim to put ourselves on “shaky ground” using exercises from dance/movement/contact improvisation, as well as techniques of design/art creation. We will try to become aware of, explore and play around with our habitual ways of interacting with the world and people around us, thereby challenge our habits and raise curiosity for the unknown.
Objectives
The course aims to provide a space, where participants can practice a curious mindset, exploring patterns of thinking and acting, becoming aware of habits and trying to challenge them, a space for trying out, for making mistakes and being awkward.
Participants will move and create, but also discuss how their experiences in class relate to theories and concepts in cognitive science. Therefore, learning goals will be very personal and subjective.
Gapenne, O. (2010). Kinesthesia and the Construction of Perceptual Objects. In Stewart J., Gapenne O., & Di Paolo E. A. (eds.), Enaction: Towards a new paradigm for cognitive science. The MIT Press.
Muth, C., & Carbon, C. C. (2016). SeIns: Semantic Instability in Art. Art & Perception, 4(1-2), 145–184. doi: 10.1163/22134913-00002049
Novack, C. J. (1990) Sharing the Dance: Contact Improvisation and American Culture (New Directions in Anthropological Writing). University of Wisconsin Press.
Claudia Muth is a perception researcher with a background in fine arts. She studied cultural design and cognitive science and was working for a small science centre on perception and illusion in Nuremberg. Since 2011 she has been conducting research at the intersection between art and science and has been teaching psychology students at the University of Bamberg as well as (since 2017) graphic design students at the Akademie Faber-Castell in Stein. Her main interest concerns the experience of uncertain, disordered, ambiguous or indeterminate situations and the various ways in which they can confuse, inspire and enrich us.
Elisabeth Zimmermann studied human biology and cognitive science at the University of Vienna. In her research she investigates how learning with a focus on body and movement can enable changes in habits and foster openness to new ways of interacting, sense-making, and being. Since 2006 she has been coordinating the MEi:CogSci – Middle European interdisciplinary master programme in Cognitive Science and also teaching interdisciplinary cognitive science courses within this curriculum.
She has been dancing since her childhood (ballet, jazz dance, modern dance, expressive dance) and has been practicing contact improvisation for more than 20 years. She has been investigating the relation of body and mind on a theoretical level, but also on a practical level, attending courses in Qigong and Tai Chi, Yoga, Body-Mind Centering, Feldenkrais, Continuum Movement, etc.
She has training in holistic dance- and movement pedagogy as well as in classical massage and teaches workshops in dance/contact improvisation on a regular basis.
Lecturer:Lily FitzGibbon Fields:Cognitive, developmental and educational psychology; neuroscience
Content
This course will provide an overview of research from a number of fields of psychology and neuroscience pertinent to the understanding of the motivational power of curiosity. In particular, we will discuss empirical findings from across the lifespan in the context of a reward learning framework of knowledge acquisition. We will consider where the subjective experiences of curiosity and interest fit into the model and how they might be differentiated. Finally, we will discuss and develop challenges, open questions, and testable predictions from the model, setting out a programme of work for the field. The aim of this final session is to generate and develop research ideas and foster new collaborations between course participants.
Session 1: Introduction to curiosity and interest
Session 2: A reward learning model of knowledge acquisition
Session 3: A lifespan perspective on information as reward
Session 4: Challenges, open questions and testable predictions
Objectives
In this course, participants will gain an understanding of a new model of information acquisition and its power to integrate a previously divided literature and generate new predictions about the process of knowledge acquisition. Participants will also learn about methods from a large array of disciplines that can be applied to the empirical study of information as reward.
Lily FitzGibbon works as a postdoctoral researcher in the Motivation Science Lab at the University of Reading. She has a PhD in Psychology from the University of Sheffield and has worked as a postdoctoral researcher at the University of Birmingham and the University of Southern California. Her research focuses on the cognitive processes involved in decision making, including curiosity, risk processing, and emotional evaluation of actual and hypothetical outcomes.
How do I experience the world around me? What might influence my decisions and actions in everyday life? How do I feel? If you are curious to answer these questions we invite you to come to our course. In our four sessions we will focus on the broad field of self-experience. We are going to introduce you to different exercises and tools out of the range of self-awareness, mindfulness, body perception, biography reflection and interpersonal and intrapersonal communication. A curios mind is the only requirement to join our experiential-group and we are looking forward to welcome you at IK.
Lecturer
Annekatrin Vetter is a clinical Psychologist and an analytic Psychotherapist in training. She works in a hospital for psychiatry, psychotherapy and psychosomatic and is doing analytic and psychotherapeutic inpatient and outpatient treatment. Moreover she is working on a research project about treatment integrity in Mentalization orientated Group Therapy.
Sophia Reul is a clinical Psychologist and an analytic Psychotherapist in training. She works in a hospital for psychiatry, psychotherapy and psychosomatic and is doing analytic and psychotherapeutic inpatient and outpatient treatment. Moreover she defends her PhD in Clinical Neuropsychology at the Neurological clinic of University Hospital Münster with a focus of neurodegeneration and dementia.
Decision-making in human and animal societies often uses a confidence heuristic – trusting the decisions made by confident individuals. This could have the benefit of quick decision-making without having to explore risky options yourself. However, confidence is a good guide to decisions only if it reflects accuracy. When the trusted individuals are overconfident, this results in risky and often catastrophic decisions. Despite the possibility of these negative outcomes, overconfidence persists and is widespread. What are then the advantages of overconfidence? Using an evolutionary perspective demonstrates the individual and social rewards of overconfidence. This also helps us understand how we can make the most of confidence while avoiding the obvious costs of overconfidence.
Vivek Nityananda has a PhD in Animal Behaviour from the Indian Institute of Science, Bangalore. He was worked at the University of Minnesota, St Paul and Queen Mary University of London. He is currently a BBSRC David Phillips Fellow at the University of Newcastle and has previously been a Marie Curie Research Fellow, a Human Frontiers Science Program Fellow and a fellow of the Wissenschaftskolleg Zu Berlin. He has researched communication and visual cognition in insects, overconfidence in humans and hearing in frogs. He is also a published author and illustrator and has worked towards engaging the public with research using comics, animation and theatre. He was awarded a public engagement fellowship from the Great North Museum, Newcastle and a Wellcome Trust Small Arts Award to support these efforts. He currently researches the ecology and evolution of sensory and cognitive behaviour and the evolution of overconfidence.
Lecturer: Alex Kacelnik Fields: Comparative cognition / decision-making, learning, problem solving, intelligence.
Content
Organisms nearly always act with incomplete information about the outcome of possible actions. They can include unpredictability into their decision process (risk sensitivity), or allocate effort to reduce uncertainty (learning, sampling). In all cases, the consequences of uncertainty, and the cost of reducing it, affect the expected payoffs, and hence can be expected to play a role in the decision mechanisms. Similarly, designers of synthetic intelligences are starting to include information-seeking (i.e. curiosity) in the behaviour of autonomous artificial systems, including problem-solving robots. I will present several lines of behavioural research in this area.
Objectives
This evening lecture is a research seminar introducing current and past but relevant research. Attendants should leave with at least a sense of what the problems are, and where some of the solutions are being sought.
Literature
Krebs, J.R., Kacelnik, A., Taylor, P., 1978. Test of optimal sampling by foraging great tits. Nature 275, 27–31. http://dx.doi.org/10.1038/275027a0.
Andrés Ojeda, Robin A. Murphy, & Alex Kacelnik. Paradoxical choice in rats: Subjective valuation and mechanism of choice. Behavioural Processes (2018) 152 73–80; https://doi.org/10.1016/j.beproc.2018.03.024.
Vasconcelos, M., Monteiro, T., Kacelnik, A., 2015. Irrational choice and the value of information. Sci. Rep. 5, 13874. http://dx.doi.org/10.1038/srep13874.
Lecturer
Alex Kacelnik studied biology in Buenos Aires, Argentina, and completed a PhD on decision making in Oxford, UK in 1979. He has been professor of Behavioural Ecology at Oxford since 1990 (emeritus since 2017). Alex has worked (and continues to work) on diverse topics, including decision-making, comparative cognition, brood parasitism, tool use, learning, and communication. His work bridges across behavioural ecology, behavioural economics, experimental psychology and, more recently, Artificial Intelligence. He is presently an external Principal Investigator in the cluster of excellence ‘Science of Intelligence’ (www.scienceofintelligence.de).
