Brain Scans and Creativity

As well as its obvious clinical applications, magnetic resonance imaging (MRI) helps researchers create maps locating brain activity during tasks such as solving puzzles, daydreaming, playing musical instruments, composing, writing and generally being creative. Knowing where something takes places provides confidence that we are closer to understanding it. This tendency to equate understanding with getting a spatial fix is itself an interesting cognitive propensity.

Matrix of squares of different sizes
Patterns of activation in an associative neural network

What do patterns of activity as coloured patches on a map of the brain reveal? According to one report the patterns in the brains of people doing something creative are similar to the patterns evident in people exhibiting schizophrenic and bipolar behaviour ( This is great news for Surrealists and Critical Theorists, as well as followers of Deleuze and Guattari, all of whom look to those marginalized “underclasses” whose condition makes it difficult for them to conform. To exalt schizophrenia is to challenge the status quo, and provides cues for political action, resistance, and the production of art. But there are other examples of correspondences between one kind of thinking and another, namely between ordinary thinking and creative thinking.

Brain scanning fits within neuroscience, a collective discipline whose authority is growing. Neuroscience pertains to understanding the biology of the nervous system of the whole animal, but it also embraces mathematical models of cognitive activity known as neural networks, ie the application of computational techniques to mimic, and therefore hopefully to understand, human thinking.

Back in the early 1990s, in seeking to understand more about the design process, colleagues and I were impressed by the two-volume collection of papers by Rumelhart and McClelland on “parallel distributed processing,” computational techniques for replicating the way the human nervous system develops viable responses to sensory “inputs.” One paper in particular caught our attention. It claimed convincingly to replicate by means of computational techniques, the way children develop language skills, in particular the ability to acquire vocabulary and grammar. Infants learning language tend to “overgeneralize” as when adding “ed” to the end of any verb in the past tense, eg saying “runned” instead of “ran.” In time, with sufficient examples, and learning cycles, language learners, and the mathematical neural networks that simulate this behaviour, eventually behave as if accounting for exceptions and nuances as well as general rules.

There are several interesting aspects to these theories. There’s no representation of rules and exceptions in a neural network. Language learners behave as if there are rules, and exceptions, without explicitly representing or understanding those rules. In other words the neural networks were able to replicate what everyone knows about language, that you don’t need to know anything about grammar to be grammatically correct.

From a design point of view, if anyone needs any convincing, it therefore seems not to be necessary to understand design rules in order to design. Rumelhart and McClelland’s studies validated the idea in a computational way that much knowledge, if not all, is “tacit” before it is formal or rule-based.

A further spinoff was to defuse any idea that creativity is different to other mental processes. In explaining their theories about language, Rumelhart and McClelland developed an interesting experiment involving the contents of rooms. They described unnamed rooms to their automated neural network in terms of contents. For example, a particular room contains a cooking stove, a cupboard, a refrigerator, and a toaster. Other rooms contain items such as easy-chairs, beds, and coffee tables. Many examples of such real-world combinations (as lists of words) are fed into the neural network. After processing the inputs the system is presented with a single word, such as “toaster.” The system then produces other descriptors strongly associated with toaster, eg cooking stove, refrigerator, dishwasher. In other words the system presents a description of the contents of a typical kitchen. This is a case of generalizing what makes up a kitchen from lots of examples.

What interested us was the idea that if you then force such a system to return what typically contains a toaster and a bed, a combination that never existed in any of the learning examples, then you still get a sensible combination of components. The system has invented something like a bed sitting room. This is creativity of a sort.

But of most interest to us was that the computational process by which new combinations were devised was exactly the same as that for producing the standard room types. The process also takes the same length of time.

This is obviously an overly simple model of cognitive activity, but it provides further evidence of the normalcy of creativity. Creativity is after all a socially-decided category for particular kinds of activity. We have to look very hard to find evidence of its origins in particular mental processes, occurring in particular parts of the brain, or even in particular individuals.

We used simple neural networks (associative neural networks) to explore the way in which “thinking” occurs without rules or categories, and the simple way that innovation occurs as a normal neural process. We tried this with examples from architectural design, looking at entranceways, windows, and foundations (footings). The goal that neural networks might have led to better computer-aided design (CAD) systems was more elusive. But that computation can be used to challenge the claim that there are special kinds of thinking, such as creative thinking, is compelling.


  1. Coyne, R.D. (1990). Design reasoning without explanations, AI Magazine, Vol.11, No.4, pp.72-80. Link via
  2. Coyne, R.D. (1991). Modelling the emergence of design descriptions across schemata, Environment and Planning B: Planning and Design, Vol.18, pp.427-458. Link via
  3. Coyne, R.D. and Newton, S. (1990). Design reasoning by association, Environment and Planning B: Planning and Design, Vol. 17, pp.39-56. Link via
  4. Coyne, R.D. and Postmus, A. (1990). Spatial applications of neural networks, Artificial Intelligence in Engineering, Vol.5, No.1, pp.9-22. Link via
  5. Coyne, R.D. and Yokozawa, M. (1992). Computer assistance in designing from precedent, Environment and Planning B: Planning and Design, Vol.19, pp.143-171. Link via
  6. Coyne, Richard. 1997. Creativity as commonplace. Design Studies, (18) 2, 135-141. Link via
  7. Dietrich, Arne. 2007. Who’s afraid of a cognitive neuroscience of creativity? Methods, (42)22–27.
  8. Rumelhart D E, McClelland J L, 1987b, “On learning the past tense of English verbs”, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 2, Psychology and Biological Models Eds J L McClelland, D E Rumelhart (MIT Press, Cambridge, MA) pp 216-271.


  1. Dan Streat says:

    Dear Richard,
    Can I ask of the origin of the “Patterns of activation in an associative neural network” image used to illustrate this article?
    Is it yours? Do you have any other examples?
    I’m coming at this from a design point of view – it’s a really beautiful way of visualising brain activity.
    Any extra info/images would be greatly appreciated.
    Keep up the good work.
    Many thanks in advance,
    Dan Streat

    1. Hi Dan, yes, the image was a visualisation from an associative neural network of the strength of connections between attributes in a training set. Threshold values are indicated along the bottom of the diagram. Here’s the full image, and a PDF of the paper. Are you doing similar work?

      1. Dan Streat says:

        Hi Richard,

        Thanks for the swift response.

        I’m currently designing a book about Maps. Not restricted to conventional cartographic drawings, these maps have been carefully curated and drawn by scientists, artists, designers, philosophers, mathematicians and historians. I stumbled across your work as part of my research. I have to confess I had little understanding of the science behind your practise before stumbling across you website – it’s really fascinating.

        Thanks for sharing the image and paper.
        I’ll let you know more about our project once it’s a little further down the road.

        Best wishes,

  2. On the subject of thought and brain patterns see Andy Clark’s guest blog Out of our Brains on the New York Times’ Opinionator site. Clark refers to the “blobs” that appear on magnetic resonance imaging of brain activity, reiterating his powerful argument that thinking is best understood as occurring beyond as well as within the brain. Clark, Andy. 1997. Being There: Putting Brain, Body and World Together Again. Cambridge, Mass.: MIT Press.

  3. Owen Davian says:

    Brain Scans and Creativity and neural networks … well there is some light reading. One thing that is so fun about science is that it goes to enormous lengths to explain and quantify things only to realize that most things in the end are just “tacit.” But you have to come to that detailed understanding before you can come to the intuitive understanding.

    This brings Steve Jobs and Apple to mind. “It just works.” He said over and over again that he and Apple wanted to exist at the crossroads of the humanities and technology. What is more interesting about this claim is that more people don’t think this way. The more we understand about the brain the more I believe we are going to head in this direction.

    The idea that we could understand better the brain and how certain areas affect certain activities in our lives is fascinating. What becomes really interesting is the idea of art and its attractiveness. I can’t remember where, but there exists some type of “magic music machine” that the industry uses to predict, with fearful accuracy, what songs will be hits and which won’t … before they have been released to the public. This formulaic approach really brings into question the individual creativity of the artist. It also feels a little confining to think we are that easily entertained!

    However, in the areas of teaching and learning this could be very exciting in shaping the way our schools teach more efficiently. Also, in terms of understanding personalities and mental illness this could be very useful. Interestingly they say that a large percentage of Fortune 500 companies’ CEOs would be classified as “mentally ill.” Obsessive compulsive I think. So they are marginalised in our school systems but they can run the world economy! Global Financial Crisis?

    1. Thanks Owen, interesting observations. This has helped me in my post on “Countercultural values,” in the wake of eulogies to Steve Jobs:

  4. Holly Warner says:

    Links between schizophrenic behavioural patterns and creativity are substantial. This, to me, suggests not the connection between artistic temperaments and madness (although that features heavily) but that a classification system such as the DSM is not flexible enough to account for the complex nexus of traits arising from the human brain during processual information passages during creative practices. This has always appeared to me as a conceptualisation of the brain in stasis, whereas concepts of neuroplasticity tend to arise in relation to the cases involving damage to the neural networks after which the subject displays unusual musical behaviour, cases that present more fruitful findings to relations between music and the brain. Musical sound prepares the brain for more malleable auditory processing in comparison with environmental soundscapes, Simon Reynolds (1996) has described the utilisation of alternate perceptual schema as manifesting as disorientation in listeners, leading to a ‘schizophrenic consciousness’, i.e. one that engages forms of perception ‘previously attributed to schizophrenics’.
    I find this recent study ( exciting for both sound and neuroplasticity. There is also a fantastic BBC documentary on Oliver Sacks work that highlights these issues, MRI mapping is used on a test subject while listening to a song he chose due to its emotional value to him and his brain appeared to be literally bathed in blood. I feel the relatively emerging field of neurophilosophy will provide useful theoretical frameworks in which to posit certain actions we cannot yet account for through quantitative data alone. This will enable parallels with critical cultural theory and possibly neuroanthropology, leading to a more interdisciplinary approach to matters of music and the mind.

    1. Great comments. Thanks Holly, and for the really useful references. Brain studies are a big thing in the Reid School of Music, and I assume you’re connected in to that.

  5. Zhe Wang says:

    The description of the way mathematical neural networks successfully simulate the behaviour of human being without knowing the general rules behind it is quite impressive. It implies a possibility that computers or AI can be as creative as human being even if they do not understand it.

    The study of AI had been criticised or even be thought as impossible at the very beginning. One of the main reasons that critic hold is that the computer scan never really understand the work like human being, let alone creativity. What they can do is just take order and do mathematical computing.From the perspective of nowadays research, the claim that computers cannot understand the work still seems to be right, but the judge that computers or AI cannot be creative needs to be reconsidered..

    Creativity has been considered as a very special and complex mental activity. Take the writer for example, a writer is supposed to learn all kinds of writing skills, read, think and write extensively before he can write something new(not necessarily). All his works are based on the premise of understanding the writing rules and hard work., perhaps he still need to wait for the occasional inspiration. However, the way neural networks work gives another different picture, which is if we give enough learning materials to the neural networks, the neural networks will find the pattern in them and are able to produce creative works according to the patterns without understanding them.

    It brings us to a world that the understanding is not important anymore, what matters is just pattern(for computers of course). Image this,when we need something creative, we can just give some existing works to computers and computers produce creative works after find its pattern. Someone may argue that there is no pattern in creativity. It is true for general creativity. But when we limit the creativity to a special field,such as room decoration. There is no any reason why computers cannot produce something new based on the patterns, just as the bed sitting room mentioned in the article.

    Although there are still many problems to be solved to turn the assumption into reality, what is more worth attention is the potential of this idea and the impact to our definition of creativity.

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