Creativity and Cognitive Science
Jonathan Firth's Memory & Metacognition Updates #107
Hello – I hope all is going well for you. It certainly feels like spring where I am. What about you?
After my book announcement last week, I thought I’d share a brief comment on the (not always obvious) links between creativity and cognitive science, and also a relevant research paper.
When we think of cognitive science, many people tend to think of memory, knowledge, worked examples, and so forth. Creativity is not the first thing that springs to mind.
Likewise, if you mention creativity in education, a lot of people appear to see this as a synonym of the arts. If they associate it with pedagogy and learning at all, then it’s probably with unscientific approaches.
However, a combination of the two is very beneficial! From a psychological point of view, there is no creativity without cognition. It is, after all, a mental process, and one that builds on and develops our knowledge and skills.
Any creativity-focused education would do well to consider what cognition research says about what creativity is, and how it develops.
Likewise, cognitive science can benefit from paying more attention to creativity. Being creative means coming up with something new (and useful); when we apply this definition to learning contexts, then a great many educational activities are creative:
It’s creative to plan a lesson
It’s creative for a student to write an essay
Coming up with a novel example of a factual concept is creative.
An emphasis on creativity helps us to think not just about knowledge (which is, of course, crucial) but also about the flexibility that allows students to apply and transfer that knowledge.
Claire Badger and I say much more about these ideas in the book; if you check it out, I’d love to hear what you think (find out more here).
A research study
An author whose research I’ve been exploring a lot (including while working on the book), is Giyoo Hatano. He emphasises that while some experts (‘routine experts’) become fast and efficient, others (‘adaptive experts’) are flexible and creative. To quote his article with Yoko Oura:
“…some experts can go beyond the routine competencies, and can be characterized by their flexible, innovative, and creative competencies within the domain, rather than in terms of speed, accuracy, and automaticity of solving familiar problems.”
I feel that this research connects expertise and creativity in a way that illustrates the points I tried to make above. Enjoy the article—it’s a good one, and pretty short!
Hatano, G., & Oura, Y. (2003). Commentary: Reconceptualizing school learning using insight from expertise research. Educational Researcher, 32(8), 26–29.
Thanks for reading, and have a good week.
Jonathan
Last time: No, Schools Don’t Kill Creativity!
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In-group struggle to create due to vmPFC regulation of conceptual flow through brain. In-group, particularly scientists exhibit slight adaptive learning within logical positivism (ie fail). Association cortices (novel concepts * interactions) are gated by vmPFC directly. Out-group are taught adaptive learning by societal forces (entropy as brain functions like authoritarianism or socially distributed cognitive offloading).
Logical positivism, empiricism, science has failed totally (just imagine a multiverse for no reason), there is no creative work being done, they are incapable due to cognitive structuring/education. Roger Penrose slowly applied one concept (quantum) to another (microtubles) very very slowly because...
HIPPs binds all concepts rendered—+ any you want, excluding extraneous. It filters relevance like a drift-sculptor, not a librarian.
How does P ≠ NP apply here? It’s not about computation—it’s about conceptual collapse time. Some ideas verify fast but resolve slow. We cut through by recursive medium-term memory learning—not single concept to single concept (like Penrose, one-to-one, quantum to microtubules).
We don’t so much use working memory like a typewriter tapestry of behavioural outcomes. We go multivariate drift. Stack concepts. Spin networks.
This works because all learning is conceptual—symbols drift naturally in relative structures. Mind is relative. HIP → LIFG → Broca → Phonology → Wernicke → Lex → Herschel’s → VWFA. Language is a recursive interface, not output.
Extreme data resolution possible—when drift aligned. Flexible learning by causal neuroscience, predictive salience, symbolic compression. We don’t solve—we collapse NP into felt structure.
This is how you and I think like Von Neumann.-Think like Ramanujan.
Fairly creative, huh? It doesn't help the in-group can't read it (RAS-VTA-Hipss-ACC-PFC-OFC-feedback to VTA (log pos-JVN). Association cortices-vmPFC-acc-hipps-PFC-OFC-VTA (adaptive learning via novel concepts-Ramunujan). science, in-group out-group bias, vmPFC, consensus only).
Thoughts?