Creative cognition in the city: underlying principles for creativity and innovation in cities
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This presentation was delivered at the 2nd Delft International Conference on Complexity, Cognition, Urban Planning and Design (http://www.bk.tudelft.nl/ccupd). The talk presents a theory for creative cities, by linking together the predictive brain hypothesis and design thinking.
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Creative cognition in the city: underlying principles for creativity and innovation in cities
- 1. CREATIVE COGNITION IN THE CITY Underlying principles for creativity and innovation in cities ANDY DONG Faculty of Engineering & Information Technologies University of Sydney
- 3. “ Many diverse properties of cities from patent production and personal income to electrical cable length are shown to be power law functions of population size with scaling exponents, β ” Bettencourt, L. M. A., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104(17), 7301-7306. doi: 10.1073/pnas.0610172104
- 4. “ absolute brain size measures were the best predictors of primate cognitive ability ” Deaner, R. O., Isler, K., Burkart, J., & van Schaik, C. (2007). Overall Brain Size, and Not Encephalization Quotient, Best Predicts Cognitive Ability across NonHuman Primates. Brain, Behavior and Evolution, 70(2), 115-124.
- 9. ( capability to design ) biological / evolutionary design competence cognitive socio-political
- 10. Premise 1 – The fundamental organizing principle underlying the brain is prediction. Premise 2 – Design thinking consists of a set of cognitive strategies closely aligned with prediction. Premise 3 – Design thinking is an adaptive function of cognitive abilities relevant to prediction. Conclusion – The organizing principles for creative cities should follow principles underlying the predictive brain and design thinking.
- 11. Premise 1 – The fundamental organizing principle underlying the brain is prediction. Premise 2 – Design thinking consists of a set of cognitive strategies closely aligned with prediction. Premise 3 – Design thinking is an adaptive function of cognitive abilities relevant to prediction. Conclusion – The organizing principles underlying creative cities should follow principles underlying the predictive brain and design thinking.
- 12. “ Rather than passively „waiting‟ to be activated by sensations, it is proposed that the human brain is continuously busy generating predictions that approximate the relevant future. ” Bar, M. (2007). The proactive brain: using analogies and associations to generate predictions. Trends in Cognitive Science, 11, 280-289. doi:10.1016/j.tics.2007.05.005 Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(03), 181-204. doi: 10.1017/S0140525X12000477
- 13. “ Ibid., Fig. 1
- 14. “ Leslie, A. M. (1987). Pretense and Representation: The Origins of "Theory of Mind". Psychological Review, 94(4), 412426
- 15. “ …recursion holds the key to that [difference in mind between man and higher animals], underlying such uniquely human characteristics as language, theory of mind, and mental time travel ” Corballis, M. C. (2011). The recursive mind : the origins of human language, thought, and civilization. Princeton: Princeton University Press.
- 16. “ an overwhelming urge to seek novelty and alternative vantage points, is a major characteristic of the human CNS ” Mesulam, M. M. (1998). From sensation to cognition. Brain, 121(6), 1013-1052. doi:10.1093/brain/121.6.1013
- 17. Premise 1 – The fundamental organizing principle underlying the brain is prediction. Premise 2 – Design thinking consists of a set of cognitive strategies closely aligned with prediction. Premise 3 – Design thinking is an adaptive function of cognitive abilities relevant to prediction. Conclusion – The organizing principles underlying creative cities should follow principles underlying the predictive brain and design thinking.
- 18. “ novel design is the capacity {faculty of the brain} to envision a non-existent material world to a level of complexity that is not obvious based on the local material environment and then to reify that nonexistent world in material or symbolic semiotic form ” Dong, A. (2010). Biological first principles for design competence. Artificial Intelligence Engineering Design Analysis and Manufacturing, 24(4), 455-466. doi:10.1017/S0890060410000338
- 19. framing analogising abduction Producing a novel standpoint mental simulation analogising Uncertainty resolution
- 20. “ … is design-by-analogy worth the extra time and effort? Our findings suggest that if the goal of conceptual ideation is to ultimately generate and develop a concept that is high quality and novel, then the answer is yes. ” Chan, J., Fu, K., Schunn, C., Cagan, J., Wood, K., & Kotovsky, K. (2011). On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples. Journal of Mechanical Design, 133(8), 081004-081004. doi: 10.1115/1.4004396
- 21. “ analogical reasoning is a core design strategy that is instantiated coincident with situations of design uncertainty, serving to facilitate the resolution of such uncertainty Analogies within mental simulations … have a „generative‟ role … [and] to explain the nature of the simulation run or resulting representation ” Ball, L. J., & Christensen, B. T. (2009). Analogical reasoning and mental simulation in design: two strategies linked to uncertainty resolution. Design Studies, 30(2), 169-186. doi:10.1016/j.destud.2008.12.005
- 23. Premise 1 – The fundamental organizing principle underlying the brain is prediction. Premise 2 – Design thinking consists of a set of cognitive strategies closely aligned with prediction. Premise 3 – Design thinking is an adaptive function of cognitive abilities relevant to prediction. Conclusion – The organizing principles underlying creative cities should follow principles underlying the predictive brain and design thinking.
- 24. design thinking framing mental simulation abduction analogizing meta representation recursion secondary representation primary representation curiosity
- 25. “ What are the primary functions of the ability to imagine what is not available to the senses and the creative faculty of the mind? … we would like here to emphasize two potential adaptive functions that appear to be fundamental to the modern human mind and crucial to our current dominance on this planet: mental time travel and design. ” Suddendorf, T., & Dong, A. (2013). On the evolution of imagination and design. In M. Taylor (Ed.), The Oxford Handbook of the Development of Imagination (pp. 453467). Oxford: Oxford University Press.
- 26. Gentner, T. Q., Fenn, K. M., Margoliash, D., & Nusbaum, H. C. (2006). Recursive syntactic pattern learning by songbirds. Nature, 440(7088), 1204-1207. doi: 10.1038/nature04675
- 27. Santino, a 31 year old male chimpanzee His cache of rocks to throw Osvath, M. (2009). Spontaneous planning for future stone throwing by a male chimpanzee. Current Biology, 19(5), R190R191. doi: 10.1016/j.cub.2009.01.010
- 28. Figure 4 from Torigoe, T. (1985). Comparison of object manipulation among 74 species of non-human primates. Primates, 26(2), 182-194. doi: 10.1007/BF02382017
- 29. Premise 1 – The fundamental organizing principle underlying the brain is prediction. Premise 2 – Design thinking is an adaptive function of cognitive abilities relevant to prediction. Premise 3 – Design thinking consists of a set of cognitive strategies closely aligned with prediction. Conclusion – The organizing principles underlying creative cities should follow principles underlying the predictive brain and design thinking.
- 30. Predictive & Creative Brain (A)natomy or (F)unction Creative City Principle transmodal pathways A multi-scale, multi-modal transport options larger working memory A historicity of place analogy F create unexpected (physical, socio-economic) encounters; mixed-used schemes recursion F hierarchically embedded sociospatial forms (e.g. fractals – Batty & Longley 1986) novelty-seeking behaviour F social value for novelty
- 31. [ To build creative cities, apply design principles of the predictive and thereby creative brain. ]
- 33. “ Dorst, K. (2011). The core of 'design thinking' and its application. Design Studies, 32(6), 521-532. doi:10.1016/j.destud.2011.07.006
- 34. “ novel design is the capacity to envision a nonexistent material world to a level of complexity that is not obvious based on the local material environment and then to reify that non-existent world in material or symbolic semiotic form ” Dong, A. (2010). Biological first principles for design competence. Artificial Intelligence Engineering Design Analysis and Manufacturing, 24(4), 455-466. doi:10.1017/S0890060410000338
- 35. (top) a good nest rated good (bottom) crude nest (top) animal sitting at rest in a nest (figure) suggestive of a large birds nest (bottom) another lying asleep in a nest Bernstein, I. S. (1962). Response to nesting materials of wild born and captive born chimpanzees. Animal Behaviour, 10(1-2), 1-6. doi: 10.1016/00033472(62)90123-9
- 36. Bird uses hook to grab bucket containing food Bird makes hook to grab bucket containing food Bird, C. D., & Emery, N. J. (2009). Insightful problem solving and creative tool modification by captive nontool-using rooks. Proceedings of the National Academy of Sciences, 106(25), 1037010375. doi: 10.1073/pnas.0901008106
- 37. competitor subject Figure 1 from Kaminski, J., Call, J., & Tomasello, M. (2008). Chimpanzees know what others know, but not what they believe. Cognition, 109(2), 224-234. doi: 10.1016/j.cognition.2008.08.010
- 38. ( Design Capability Set ) Abstraction (the stock of valuable cultural resources that provide the raw material for creative activities such as design) Authority (the capability to exert positive or negative obligation on government to design a world that they value) Evaluation (the capability to validate design solutions put forward both during the design process and when the design work has been completed) Information (a right to information that is authoritative, complete and truthful) Knowledge (the capability to have general knowledge of the practice of design) Participation (capability for setting conditions for meaningful citizen participation) Dong, A., Sarkar, S., Nichols, C., & Kvan, T. (2013). The capability approach as a framework for the assessment of policies toward civic engagement in design. Design Studies, 34(3), 326–344. doi: 10.1016/j.destud.2012.10.002
Hinweis der Redaktion
- The context for my talk is the concept of the ‘creative city’. In Sydney as it is I am sure in many other cities around the world, alongside the concept of the ‘sustainable city’ or the ‘world city’ or “I am Amsterdam” or any number of ways in which cities are branding and marketing themselves, cities are trying to find ways to position themselves as competitive cities of the future. Edward Malecki described this phenomenon as cities “jockeying for position”.Much of this jockeying is based around the core idea that industrialized economies are knowledge-based economies. To boost their knowledge economics, cities are trying to attract the so-called creative class as Richard Florida describes them – and us.Richard Florida has proposed some principles underlying knowledge economies that excel – he calls it the three T’s: talent, technology, and tolerance. And there are any number of knowledge competitiveness indices, which measure the extent to which cities achieve their aim of being knowledge-based societies.Aside from the marketing and branding, what theory should we use to predict and explain how it is that cities actually behave creatively?There is at least one other complex system that we know of that also behaves creatively. And, that is our brain. And, it is to our brain that I’d like to look for some principles that may say something about how we should organize cities for creativity. But also, as I believe Juvali will discuss in his talk, when we talk about cities as complex systems, we cannot do so without attention to the behavior of the agents in the system – in other words, what kinds of cognitive strategies are linked with being creative. In this talk, then, I wish to try to bring these two aspects together, linking the predictive brain, design thinking, and principles for creative cities together.The brain and cities have one interesting aspect in common that makes it plausible to look to the brain for ideas about how to produce creative cities.
- Physicist Luis Bettencourt has investigated how certain properties of cities scale according to a power law based upon its population size. For example, all socio-economic metrics, such as wages, wealth, the number of patents, violent crime and educational institutions, scale with population size with a common exponent b ~ 1.15 (> 1, “superlinear” scaling), whereas all infrastructural metrics, such as length of roads, electrical cables, and number of gas stations, scale with b ~ 0.85 (< 1, “sublinear” scaling). So, based on just two simple parameters – population size and a scaling factor, we can predict many properties of cities.
- Similarly, the most parsimonious reason why it is that the brain is so creative and so gives humans an exceptional capacity to modify our world is that it is big. In primates the encephalization quotient, or the ratio of brain to body mass, is not a statistically significant predictor of cognitive ability. Total brain size though is a significant predictor of General Mental Ability.Why did humans grow such large brains? This has been a question that evolutionary scientists have been considering for quite some time. There are a number of theories such as the expensive-tissue hypothesis, which postulates an evolutionary trade-off between the size of the brain and that of the digestive tract, the social brain hypothesis promoted by Robin Dunbar, which hypothesizes that our increasingly social lives necessitated larger brains to coordinate social relations, and many other.However, there may be an even more parsimonious evolutionary driver for both our brain size but more crucially how the brain organized itself – that is, its function.
- It is probably self-evident that humans expend a great deal of our time trying to predict the future. Wanting to know what’s going to happen tomorrow and into the future is a fixture of human life. Before I travelled, since it’s now basically summer in Australia, I of course want to know what the weather’s going to be like in northern Europe.
- Longer term, we want to know where our climate is heading and how fast.This figure shows projected greenhouse gas concentrations for four different emissions scenarios. The top three scenarios assume no explicit climate policies. The bottom green line is an illustrative “stabilization scenario,” designed to stabilize atmospheric carbon dioxide concentration at 450 parts per million by volume (ppmv). Source: USGCRP (2009)
- Europeans would probably really like to know where their economies are heading.
- And, if all else fails and I fail to see where my life is heading given the uncertainties of the modern world, I can go get my star charts done to help me predict where my life is headed.
- So my researchprogramme is about understanding what I call design competence. It is as many of you will know a term that is a blatant rip off of Noam Chomsky’s concept of linguistic competence, which is knowledge of language. Chomsky distinguished between linguistic competence, as your ability to be able to form a grammatically correct expression, and linguistic performance, which refers to your fluency in a particular language. The questions that interest me are why do humans even have design competence, which are biological and evolutionary issues and what provides communities or societies the capability to design. For today’s talk, I will mostly touch upon my and others’ research on the evolutionary biology and cognitive aspects of design thinking.For today’s lecture, the subject of my talk will sit at the crossroads of a few fields: creative cities, cognitive neuroscience, and design thinking. I would like to suggest that the cognitive skills that provide for the human faculty of design might be useful in understanding the structural regularities underlying knowledge creating and innovation in cities in two ways – both at the level of what makes people productively creative and what cities could do to help people do more creativity. To do so, I will build up a cognitive model of design thinking. I will then use this cognitive model to propose principles that we should apply to the design of creative cities.I will not however be taking the normal route, which is to rely solely on cognitive design research.Underlying theme – search for a coherent framework from neuroscience to (cognitive) design thinking to socio-political theory to explain why it is that we (humans) have design thinking.
- I will instead start by considering the organizing principles behind the architecture of the brain. My first proposition is that the brain is architected for prediction. I will first thus start with the proposition that the fundamental organizing principle underlying the brain is prediction. I will review the research performed by Moshe Bar and his framework for the predictive brain. Design thinking is “structured forecasting”.I will then explain the connection between Bar’s framework and what I believe is a cognitively-grounded model of design thinking. I will describe and explain the cognitive strategies for which I believe there is strong evidence to confirm that these are the cognitive strategies that are emphasized in design thinking. I will then explain the connection between these strategies and the predictive brain. I make the proposition that the human faculty of design is thus an exaptation of the predictive brain. I believe that the predictive brain model is the most consonant with what we currently know design thinking to be about.I will then conclude with some organizing principles that creative cities could apply.
- My first proposition is that the fundamental organizing principle underlying the brain is prediction.
- To my understanding, one of the key questions in neuroscience is to identify a small set of unifying principles that could explain the multitude of ‘functions’ in the brain.Brains, it has been argued by many, are essentially prediction machines. In other words, our brain is a collection of neural processes that generate successively complex predictions about the future. In the theory put forward by Moshe Bar, Karl Friston, and many other neuroscientists, the brain proactively predicts what information it should receive based on memory, experience, and bias. It does not simply passively process sensory stimuli as they are received, analyses them, and then determines what they are.In evolutionary terms, predicting the future would have been a significant advantage. We could predict where animals might be so that we go hunt in one location as opposed to another. We could predict how others would respond to our behavior and modify our behavior so as to achieve certain social outcomes. Or we could be devious and trick them knowing that the other person would respond in a particular way. We could predict what set of operations on a stone would produce a desired outcome. So, if we possessed a brain that has the skills to reduce uncertainty between visual input and perception we could apply that skill to a host of other uses, one of which would be design.
- Moshe Bar proposes an analogically-driven model of prediction in the brain. In his model, an input (A′) is connected with an analogous representation in memory (A). This link results in the co-activation of related representations (B, C, D), associations that remain ‘online’ to provide testable predictions. The input can be either an external, sensory input, or internally generated during thought processes. Furthermore, the input can be of varying degrees of complexity, which would result in predictions that are correspondingly of different levels of elaboration, encompassing the range from perceptual to executive predictions.As I will show later, this model proposed by Bar is remarkably similar to what it is we know how designers think creatively. Before I draw the parallels, I would like to elaborate a bit more on this model and the cognitive behaviours that the model predicts. In other words, what is the purpose of some cognitive functions such as representation, recursion, etc.? Why were they “built”?(From Bar’s paper) A minimalistic depiction of the proposed principle. An input (A′) is connected with an analogous representation in memory (A). This link results in the co-activation of related representations (B, C, D), associations that remain ‘online’ to provide testable predictions. The input can be either an external, sensory input, or internally generated during thought processes. Furthermore, the input can be of varying degrees of complexity, which would result in predictions that are correspondingly of different levels of elaboration, encompassing the range from perceptual to executive predictions.
- The first issue is that of representation. What is representation for? In the predictive model of the brain, representation, or perhaps more precisely mental imagery, is part of a process of generating predictions based on past experience by making explicit, or let’s say ‘visible’, the antecedents and consequences of actions. In other words, mental imagery allows us to pose and to answer ‘what if’ questions – what if this rock were sharp, what if I offered food to my peer, what if I did not have to look at my cell phone in order to know who just sent me a text message? In fact, the main point is that I can create a mental image (representation) that is wholly dissociated from sensory ‘reality’.We know that the development of representational skills in children indeed follows a pattern wherein they progress from having the ability to represent the world as it is, that is, to represent the world in such a way that the representation has semantic fidelity with the actual world – what is generally called primary representation, to the ability to ‘play’ – that is pretense. They progress to the stage wherein they recognize that the representation can have a representation – or meta-representation. In meta-representation, children understand that a representation has an interpretation. At this stage, they can represent that a banana is not a banana but that it is a telephone and that the relation between the representation of the banana as a telephone and the banana is ‘play’. If I update that relation to something else, let’s say ‘tool’, then the banana might become something that I use to scratch my back. If I thus represent in my mind that the banana could be a tool, I could imagine myself scratching my back and thus relieving my itch. And, I predict that the banana as a scratching tool would be useful for that purpose – and so I scratch my back with it.Whatever the circumstance, we can thus think of representation as having the purpose of considering the ‘what if’ possibilities.Indeed, meta-representation is an essential component to design thinking. Meta-representation is at the root of developing alternative useful representations of the same imaginary object that is to be designed. This is basically the argument that is put forth by Schön and Wiggins in their influential paper, “Kinds of seeing and their functions in designing”.One of the three ways that Schön and Wiggins described seeing is exactly meta-representation. When what designers see in a representation are appreciations of forms, configurations, and moves, this is meta-representation. They no longer ‘see’ the lines and shapes; rather, they see harmony, cohesion, or balance. That is, they ‘see’ the relation between the referent, the lines and shapes, and their representation of the lines and shapes. This is, I believe, what Barbra Tversky was presenting in her lecture two nights ago.
- What mental structures and processes would meta-representation engage? I propose two essential ones: recursion and curiosity. Meta-representation relies on recursion.While the definition of recursion varies somewhat between fields, the essential idea is that recursion is a procedure that calls or refers to itself.Thus, to have a meta-representation, I recursively represent that my representation of my laptop is a representation of a brand which is a representation of design-led innovation which is a representation of a management theory and so on. To represent harmony, I represent that lines form representations of shapes which represent houses which represent an urban development and so on. Every subsequent representation is recursively built upon the prior representation.Recursion plays several direct roles in design thinking. It provides the capacity to generate an infinite range of artifacts from a finite set of physical elements and construction operations. This skill would have been central to the production of hafted (having a handle) tools or decorative items, which require the combination of physical elements (structural recursion) and production steps (processual recursion). Second, recursion supports mental time travel, which entails projecting oneself into past and future sequences of events. We design with future use in mind. We can foresee how something will be useful or aesthetic in the future.
- Finally, though, we have to consider why (and how) it is that the brain would willingly expend more energy to produce alternative representations.I propose curiosity, a fundamental propensity to seek novelty and difference.One of the most important architectural (anatomical) differences between the human brain (and also the primate brain) and the brain of many other species is that conception is not directly connected to sensation. Our cerebral cortext lacks interconnections linking unimodal areas that serve our different senses. Sensory information undergoes extensive associative elaboration. The large number of alternative trajectories through the brain as sensation becomes transformed into cognition means that the same sensory stimulus can potentially elicit numerous alternative representations. The consequence of this behavior permeates all aspects of cognition and underlies the uniquely human aptitude for discovering multiple solutions to similar problems.The curiosity galvanises us to propose alternative possibilities of futures. It is actually worth our while to be curious (seek/simulate (synthetic) knowledge) about what the future might be like, so that we could plan accordingly, e.g.
- The foundation of the predictive brain and in particular Bar’s model thus leads me to my second proposition, that design thinking consists of a set of cognitive strategies closely allied with prediction.I have already touched upon some cognitive skills enabled by a predictive brain – meta-representation, recursion, and curiosity, and I will now circle back to explain how these skills provide the foundation for the cognitive strategies we see being emphasized during design.
- I propose a working definition of design, as I have used elsewhere (Dong 2010), as the capacity to envision a nonexistent material world to a level of complexity that is not obvious based on the local material environment and then to reify that nonexistent world in material or symbolic semiotic form. The latter part of this definition accords with the idea that artifacts “reflect projections of intent from a mind into the external world” (Gowlett 2009). In other words, this definition of design requires that we can do more than rely on the perceived affordance of an object as it exists to test the depth of water before crossing it, which has been observed in orangutans (Russon et al. 2010). Rather, we would gather up sticks and vines to build a raft to cross the water.
- While there is a considerable amount of discussion about what design thinking is – and you get various definitions depending upon the discipline – for me, from a cognitive perspective, design thinking has two essential functions: novel framing and uncertainty resolution. There are many pathways to novelty and uncertainty resolution, and I do not claim exclusivity. It just so happens that design thinking entails a particular set of cognitive strategies emphasized for these purposes and these methods have been empirically shown to be very productive (i.e., effective, efficient, etc.). (And, even under many definitions of design thinking, including Tim Brown’s definition, this is what we have: e.g., “a discipline that uses the designer’s sensibility and methods” = novel framing (assuming you believe designers produce novelty/creativity=novel+useful and this is the premise of the definition in any case), “what is technologically feasible and what a viable business strategy can convert into customer value and market opportunity” = uncertainty resolution.Novel framing because of the emphasis on creativity – which in design is construed to entail novelty AND usefulness. It is wonderful to be wild and whacky – but commercial sensibilities and other practicalities generally force designerws to consider the pragmatic, too.In cognitive design research, we routinely find that what designers do is use various tools (such as ethnography, appeal to first principles) as mechanisms to assist with novel framing. Then, they apply other tools, such as prototyping and sketching, to reduce uncertainty, to ‘try out’ potential problem and solution frames to determine if something ‘can work’. We know this because designers do not exhaustively delineate alternatives – and this is generally a very unproductive design approach. In fact, brainstorming has been shown to be very inefficient and generally ineffective in terms of productivity in the design process. Rather, they start from “primary generators” as Jan Darke called them, which are generative hypotheses, to produce synthetic knowledge, which is combined with experience and empirical observation to learn ‘what could work’.Consonant with Bar’s framework, analogical reasoning figures prominently in both of these functions. So, I’m going to focus in the next few slides how analogies are recruited in novel framing and uncertainty resolution.(Alternative text) Many models and definitions of design thinking, but, I prefer the perspective of design thinking as a set of cognitive strategies emphasized in a systematic way of projecting future circumstances.Framing is the process of drawing associations and dissociations between the facts of the situation, assumptions, and precedence to produce a schema for their interpretation, which makes it possible to clarify the detailed requirements of the decision and determine the extent to which the proposed solution can satisfy them.Abduction is the process of proposing a hypothesis to explain the data, which in design is “what might be” rather than the current or previous state of affairs.Analogizing involves identifying and carrying over knowledge from prior situations to support current decision making activities.Mental simulation is the act of mentally trying out the consequences of a decision, thereby producing a sequence of future interdependent events, or stepping back in time to identify the sequence of interdependent precedents to support the decision.
- First, let’s take novel framing. Drawing inspiration from examples by analogy can be a powerful tool for innovative design during conceptual ideation even though it carries the risk of negative design outcomes (e.g., design fixation), depending on key properties of examples. In the study by Chan and colleagues, they show that less-common example designs, which designers are less likely to have been exposed to, present a unique advantage over more-common example designs in terms of the potential for stimulating creative ideation. Specifically, it could be that less-common examples are more likely to support multiple interpretations, and thus facilitate broader search through the space of possible solutions.We are relatively confident that with the appropriate analogical stimulus, designers are more creative and productive.
- Creative ideation is only one function of analogical reasoning. The other, when combined with mental simulation, is uncertainty resolution.Linden Ball and Bo Christensen have been studying analogical reasoning and mental simulation over a number of years. In I think one of their key studies, they coded analogies for ‘purpose’ and in terms of whether they were within-domain or between-domain. Mental simulations were coded for ‘focus’: technical/functional or end-user. All expressions of uncertainty were also identified. Analogies were found to be typically between-domain (indicative of innovative reasoning) and were evenly distributed across solution generation, function finding and explanation. Mental simulations were predominantly technical/functional. Their most striking observation was that analogies and mental simulations were associated with conditions of uncertainty. They propose that analogising and mental simulation are strategies deployed to resolve uncertainty – a claim that is supported by the fact that uncertainty levels returned to baseline values at the end of analogising and simulation episodes.Thus, while I haven’t had sufficient time to go over all of the studies that discuss the role of analogical reasoning in design, many other design researchers such as KeesDorst affirm that new frame creation is an important element in professional design practice. Alongside those new frames, a practical concern on the plausibility of frames is uncertainty resolution – so we have cycles of identifying problems, solving problems, and explaining concepts – all of which exploit cognitive strategies including framing, analogical reasoning, abduction, and mental simulation.
- (For reference from Bar’s paper) A minimalistic depiction of the proposed principle. An input (A′) is connected with an analogous representation in memory (A). This link results in the co-activation of related representations (B, C, D), associations that remain ‘online’ to provide testable predictions. Associations are formed by a lifetime of extracting repeating patterns and statistical regularities from our environment, and storing them in memory. The input can be either an external, sensory input, or internally generated during thought processes. Furthermore, the input can be of varying degrees of complexity, which would result in predictions that are correspondingly of different levels of elaboration, encompassing the range from perceptual to executive predictions.To design, we reverse the predictive brain, so to speak. We generate new frames and new design inputs until the distance between them is simultaneously sufficiently far (“novel”) such that the stimulus can be explained by the frame using an out-of-domain analogy. Similarly, and at the same time, to test plausibility of the stimulus, we generate a within-domain analogy. If these match up, then, the frame can stimulate other frames or “moves” based upon the current way of seeing the design problem and these stimulated frames are based upon experience as a designer. This recurrent seeing-moving cycle has been described by Donald Schon as reflection-in-action, so it’s very nice that this framework fits in exactly with a widely accepted view of the design process.
- Fundamentally, design thinking is an open-ended capacity that enables us to create novel objects or situations by combining and recombining, and sometimes inventing, base elements into novel constellations. It entails a set of thinking strategies and tools and methods to construct a repertoire of knowledge that helps to frame problems in such a way so as to predict whether design interventions will work. This is what I mean by novel framing and uncertainty resolution being the core functions of design thinking.Given the multiple models of design thinking, it seems timely to consider if there may be a more parsimonious explanation tying cognitive strategies emphasized in design together. The predictive brain is I believe a unifying model.Design always entails imagining a future object and a future world that do not exist. The problem is figuring out possible future worlds. I didn’t touch on this much, but the whole problem of abductive reasoning in design is exactly this – what type of future best explains the data I have – which means, what kind of product would best resolve the set of issues I have identified? A brain architected for prediction would be a powerful ally to achieve this outcome.
- Till now, cognitive design research has identified four key cognitive strategies associated with design practice: framing (Dorst 2011); abductive reasoning (Roozenburg 1993); analogical reasoning (Visser 1996, Ball, Ormerod, and Morley 2004); and mental simulation (Ball and Christensen 2009, Christensen and Schunn 2009). It is these thinking strategies that form the research basis of and a cognitive perspective on design thinking.What I mean is what we currently consider to be the key cognitive strategies associated with design thinking rest on what may be the most significant skills produced by a brain built with a capacity to predict – meta-representation, itself predicated on recursion, and curiosity.I would like now to touch upon some evidence on the origins of design thinking from other species that exhibit creative and innovative behaviors to explain why it is I think that these are the necessary cognitive skills for design thinking.
- The first is recursion. To date, there is no evidence that any other species other than humans has the capacity for recursion. A study by Gentner and colleagues who attempted to teach European starlings to sing a context-free grammar purports to show that birds have recursive computing capabilities. However, the findings are in doubt with alternative explanations having been proposed to explain the findings. If nonhuman animals had the capability for recursion, then they should be able to pass tests for full-fledged theory of mind, that is, the capability to represent that I have a representation.
- The first is recursion. To date, there is no evidence that any other species other than humans has the capacity for recursion. A study by Gentner and colleagues who attempted to teach European starlings to sing a context-free grammar purports to show that birds have recursive computing capabilities. However, the findings are in doubt with alternative explanations having been proposed to explain the findings. If nonhuman animals had the capability for recursion, then they should be able to pass tests for full-fledged theory of mind, that is, the capability to represent that I have a representation.
- Recursion itself is necessary for mental time travel and planning.www.guardian.co.uk/science/2009/mar/09/chimp-zoo-stones-science;http://dx.doi.org.ezproxy2.library.usyd.edu.au/10.1016/j.cub.2009.01.010This was reported in the journal Current Biology and then reported in the popular press in the UK by the Guardian.This is Santino,a 31-year-old male chimpanzee at Furuvik zoo in Sweden. The zookeepers believe that he may be the first animal to exhibit an unambiguous ability to plan for the future.Santino didn’t like large crowds and would throw rocks at them. But he wouldn’t just go collect rocks at the time he threw them. At night, when the zoo is closed, Santino would collect rocks as a supply. He would then use his stash of rocks during the day. He wouldn’t use the stones for any other purpose than for throwing them at people and he always located the stones are the shoreline (moat) facing the visitors. This is not a scientifically controlled study, but it is a very long term observation over a decade, so it can’t be dismissed as merely coincidental. It does show something remarkable. In order for Santino to have forward planning, he would have had to envisage future events and plan a way to deal with it. In this case, his plan was to make a cache of rocks. He wouldn’t do this during winter when the zoo is closed.Unfortunately, Santino had to undergo an operation to reduce his aggression. He probably doesn’t throw rocks anymore, but I wonder if he is ‘stewing’ and planning other kinds of acts … I think we can really say that Santino has ‘evolved’ if he somehow decides to symbolically express his disaffection with the zoo visitors. I’ll let you decide what kind of symbolic expression he might be thinking of.
- This thus takes me back to the main thesis of my paper. If we want to build creative cities, perhaps we should look to the organizing principles behind our predictive and thereby creative brain.And, if we want to understand what makes cities creative, we must also know what cognitive strategies are emphasized in creativity – and then perhaps design our cities to augment these strategies.In my last slide, I want to touch upon some of these.
- We can look to anatomical features to derive some principles for structural layout.We can look to functional features to derive some desirable properties of cities.Connect analogy and multi-scale, multi-modal transport to Bill Hillier’s talkConnect recursion to Batty’s talk
- The organization of cities has followed numerous theories, including economy, geometry, technology, and finance (Hall 1988). Cognitive science and cognitive design research may offer new ways of envisioning the organizing principles for the spatial organization of cities. Under the proposed framework, technology precincts concentrating a specific industry would be eschewed in favor of more diverse industries, as in biotechnology next to digital media to permit cross-fertilization of analogical ideas. Further, under this framework, preservation and reuse of buildings would be preferred over demolition. The historic buildings not only preserve the history of place; they also embody the cumulative cultural knowledge and thereby promote and exercise recursion and mental simulation to construct documentary narratives. Just as urban planning has started to respond to issues surrounding public health as a set of organizing principles for spatial layout of cities (Saelens, Sallis, and Frank 2003), perhaps cities can promote growing smarter and creative brains by responding to concepts from cognitive science and cognitive design research.But, perhaps, more importantly, in designing creative cities, we need to place our attention first and foremost on the cognitive skills and strategies that may underlie our creative thinking. I have suggested in this talk that the predictive brain is a unifying model for the brain’s behavior and that there are several cognitive strategies emphasized in design thinking, which are consonant with the predictive brain model.I propose that cities exert a contribution to the behavior and evolution of cognitive skills and strategies associated with design, a critical faculty underlying our evolutionary success. They are perhaps more so than the sling - In the book The Artificial Ape: How Technology Changed the Course of Human Evolution, Timothy Taylor (2010) claims that technology produced humans, rather than the other way around. Hence, we are ‘artificial apes’. The invention of the sling, a device he believes is a short conceptual distance from observing chimpanzees carrying their young on their backs, would have prolonged the gestation period of humans by allowing the skull to enlarge outside the womb. In other words, it was the sling, a technology, which enabled larger brains – responsible for making us artificial apes.
- Backup slides to follow
- The basic reasoning pattern in productive thinking is Abduction. Abduction comes in two forms – what they have in common is that the outcome of the process is conceived in terms of value. The first form, Abduction-1, is often associated with conventional problem solving. Here we know both the value we wish to create, and the ‘how’, a ‘working principle’ that will help achieve the value we aim for. What is missing is a ‘what’ (an object, a service, a system), that will give definition to both the problem and the potential solution space within which an answer can be sought.This is often what designers and engineers do – create a design that operates with a known working principle, and within a set scenario of value creation. This is a form of ‘closed’ problem solving that organisations in many fields do on a daily basis (see Dorst, 2006). The other form of productive reasoning, Abduction-2, is more complex because at the start of the problem solving process we ONLY know the end value we want to achieve. This ‘open’ form of reasoning is more closely associated with (conceptual) design.So the challenge in Abduction-2 is to figure out ‘what’ to create, while there is no known or chosen ‘working principle’ that we can trust to lead to the aspired value. That means we have to create a ‘working principle’ and a ‘thing’ (object, service, system) in parallel. The need to establish the identity of two ‘unknowns’ in the equation, leads to design practices that are quite different from conventional problem solving (Abduction-1). As the challenge that is before the actor in Abduction-2 is most closely associated with design (Roozenburg & Eekels, 1995) and best represents the open, complex problems for which organisations are seeking new approaches, Adbuction-2 will be the focus of this paper.
- In summary, these indicators result in a general framework by which we can measure: can the community design. The capability set for design is proposed as the set of the real opportunities and freedoms people have to achieve the functioning of designing, that is, to ‘do’ design.