Keywords: creativity, inspiration, ideation, analogy
People build new ideas on what they know and have seen. Sometimes this a good thing; sometimes it kills creativity. We want to know: are there scientific principles that can guide creators’ interactions with sources of inspiration so that they are inspired and not hindered in their creativity?
This is an old, fundamental question in the cognitive science of creativity (and has been the main thread of my research from the beginning of my research career), but it’s taken on a fresh significance in today’s information age, where creators can be exposed to many, many potential sources of inspiration online (e.g., Google Scholar, US Patent Database). The sheer number of potential inspirations is both exciting and daunting. Computational inspiration systems (such as search engines and recommender systems) can help by directing users’ attention to what is most inspirational. But what is most inspirational? (How) can we predict this beforehand?
What we’ve learned so far
Here are some things we’ve learned so far. For more details, please read the relevant papers!
- Building directly on diverse sources of inspiration doesn’t yield immediately creative ideas; these inspirations only turn into creative (i.e., both novel and useful) ideas when people spend time iterating on the resulting ideas (Chan & Schunn, 2015)
- Analogies from conceptually far domains can help people come up with new ideas (Chan et al., 2011; Chan & Schunn, 2015), but somewhat-far analogies strike the best balance between novelty and quality (Chan, Dow, & Schunn, 2015; Fu et al., 2013).
- When determining what is most inspirational, computational inspiration systems are more effective if they account for users’ cognitive states (e.g., whether they are “stuck” or “on a roll”) (Siangliulue, Chan, Gajos, & Dow, 2015), and ideally also account for how they have been thinking about the problem (Chan et al., 2017).
Here are some questions we’re currently pondering:
- Are there (really) long-term, long-tail differences in the outcomes of far vs. near inspirations? In other words, are far inspirations less likely to be helpful on average, but far more likely to yield outsized breakthroughs when they are helpful? If so, what conditions/strategies, if any, increase the odds of these breakthroughs?
- How can we account for users’ cognitive states in a predictive (vs. reactive) manner? Could we create “thinking caps” that leverage real-time signals (e.g., physiological/brain signals, real-time NLP) to predict and (pre-emptively) respond to users’ cognitive states?
- Why do we fail to remember knowledge that we already possess that can help inspire creative new ideas? Can we leverage models of human memory to build information technologies that help us remember helpful things at the right moments?
- Empirical findings on the benefits of initial idea diversity are mixed. Can we create models that predict the problems and conditions under which diversity of initial ideas leads to better final solutions?
Semantically Far Inspirations Considered Harmful?: Accounting for Cognitive States in Collaborative Ideation In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition 2017
Providing timely examples improves the quantity and quality of generated ideas In Proceedings of the ACM Conference on Creativity and Cognition 2015 Best Contribution to Creative Communication (nominated)
Do The Best Design Ideas (Really) Come From Conceptually Distant Sources Of Inspiration? Design Studies 2015 Design Studies Award
The impact of analogies on creative concept generation: Lessons from an in vivo study in engineering design Cognitive Science 2015
The importance of iteration in creative conceptual combination Cognition 2015
The Meaning of Near and Far: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output Journal of Mechanical Design 2013
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 2011