Symposium: Creative Cognition - eScholarship
Bibliographic record
Abstract
Symposium: Creative Cognition Will Bridewell (will.bridewell@nrl.navy.mil) Navy Center for Applied Research in Artificial Intelligence Washington, DC 20375 USA Liane Gabora (liane.gabora@ubc.ca) Department of Psychology, University of British Columbia Kelowna BC V1V 1V7 Canada David Kirsh (kirsh@ucsd.edu) Department of Cognitive Science, University of California La Jolla, CA 92093 USA Paul Thagard (pthagard@uwaterloo.ca) Department of Philosophy, University of Waterloo Waterloo, ON N2L 3G1 Canada Keywords: Creativity, cognitive processes, domains, learning, adaptation, methods. provided hard constraints not only on the form of the constructed models but also on the space of potential solutions, calling any attribution of creativity into question. However, recent versions possess the capacity to learn knowledge from their modeling experiences. This knowledge improves their ability to account for data in later tasks. In this context, he will identify how such systems can violate their own constraints to create models by exploring outside-the-box solutions. Introduction Creativity is the generation of products and ideas that are new, valuable, and surprising. Interdisciplinary research in cognitive science makes it clear that creativity does not have to be the mysterious result of divine inspiration. Rather, we can investigate the mental processes that have creative results. This symposium will discuss creativity from a combination of disciplinary vantage points, including philosophy, psychology, neuroscience, and computer modeling. We will try to answer questions such as the following: What are the most important cognitive processes involved in producing creative results? Do these cognitive processes operate in the same way across the many domains of creativity, including science, technology, the arts, and social innovation? How can understanding of cognitive processes be used to enhance creativity? Is creativity amenable to computer modeling? Is there an optimal level of creativity at the individual and social level? Liane Gabora Liane Gabora, an Associate Professor of Psychology at the University of British Columbia, has over 130 publications on the mechanisms underlying creativity and the cultural evolution of creative ideas. She has lectured on creativity worldwide and secured funding for her research totaling over one million dollars from sources in Canada, Europe, and the USA. She will present a theory of creativity, honing theory, according to which the creative mind is a self-organizing, autopoietic structure, and the creative impulse stems from its self-mending tendencies. She will present converging evidence for honing theory from neuroscience, studies of painting and analogy formation, a mathematical theory of concepts that incorporates their contextual, non- compositional nature, and an agent-based computer model of the birth and evolution of ideas. In this computer model the cultural evolution of ideas is not open-ended unless agents can chain simple ideas into more complex ones. The adaptive value and diversity of new ideas increases when agents can (1) shift between convergent and divergent processing modes, or (2) adjust their ratio of inventing to imitating over time in accordance with the success of their creative ideas. She will show that individual creative styles are recognizable not just within a domain, but across domains (e.g., if we know someone’s writing style we are more likely than chance to know which artworks were Will Bridewell Will Bridewell earned his PhD in Computer Science in 2004 from the University of Pittsburgh, where he developed a simple method for detecting negation in medical records and a unique approach to explaining anomalies in scientific data. He then moved to Stanford University where he conducted research in computational scientific discovery and socially aware inference. In 2013, he joined the Naval Research Laboratory to investigate the interaction between attention and perception in cognition. For the symposium, he will discuss his research on computational systems that construct mathematical models from scientific data. The need for human-encoded knowledge limited the capabilities of early versions of these systems. More specifically, the rigidity of this knowledge
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.140 | 0.039 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".