Title : Understanding and Augmenting Creative Inspiration: The Tricky Case of Prior Knowledge
Speaker: Joel Chan (Carnegie Mellon)
Location: UTA 5.522 (1616 Guadalupe St., 5th Floor)
Decades of scientific research have shown that prior knowledge (e.g., analogous solutions to related problems) is necessary for innovation. Yet, information technologies that increase access to prior knowledge (e.g., idea recommender systems, online innovation platforms) have yielded inconsistent gains in people’s ability to generate innovative ideas. I argue that, in order to build information technologies that reliably and effectively augment creative inspiration, we need a deeper scientific understanding of how creative inspiration works. In this talk, I examine one prominent hypothesis from creativity theory: the most creative ideas are inspired by analogous solutions from domains that are conceptually very far from one’s problem domain. Drawing on lab experiments with engineering designers and large-scale computational linguistic analyses of creative ideation on a large-scale Web-based innovation platform, I show how this hypothesis needs significant refinement to accurately capture the complex nature of real-world human creativity. I will conclude by discussing how these insights inform the design of novel information technologies that more effectively augment human creativity, including examples from ongoing work in my research program.
Joel Chan is a Postdoctoral Research Fellow in the Human-Computer Interaction Institute at Carnegie Mellon University. He received his PhD in Cognitive Psychology from the University of Pittsburgh in 2014. Joel’s research
integrates cognitive science and human-computer interaction to explore how we can better understand and support human creativity. His long-term research goal is to develop robust theories of creativity embodied in computing systems that augment human creativity.
His work has been recognized with a Best Paper Award at the ASME Design Theory and Methodology conference, the Design Studies Award 2015, and supported by an NSF Doctoral Dissertation Improvement Grant.
1:15pm to 2:30pm