Abstract
Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of generated ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC Sánchez–Batet) and semantic similarity (Lin/Sánchez–Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence.
Highlights
Creativity is the intellectual ability to create, invent, and discover, which brings novel relations, entities, and/or unexpected solutions into existence [1]
With regard to creative thinking, our primary interest was focused on semantic similarity because as a two-argument function, it is able to evaluate the relationship between pairs of vertices in the constructed semantic networks
A comparison between the student and instructor speech in the problem-solving conversations did not show significant differences in semantic similarity (three-factor repeatedmeasures analysis of variance (rANOVA): F1,12 < 0.3, P > 0.58; Fig. 2(A)), information content (three-factor rANOVA: F1,12 < 0.2, P > 0.65; Fig. 2(B)), polysemy (F1,12 < 0.6, P > 0.46; Fig. 2(C)), or level of abstraction (F1,12 < 0.9, P > 0.38; Fig. 2(D)); this could be because all of the ideas originating from the student or the instructor were commented upon by both participants
Summary
Creativity is the intellectual ability to create, invent, and discover, which brings novel relations, entities, and/or unexpected solutions into existence [1]. Creative thinking involves cognition (the mental act of acquiring knowledge and understanding through thought, experience, and senses), production, and evaluation [2]. We first become aware of the problems with which we are confronted, produce solutions to those problems, and evaluate how good our solutions are. Each act of creation involves all three processes—cognition, production, and evaluation [2]. P. Guilford, who first introduced the terms convergence and divergence in the context of creative thinking, productive thinking can be divided into convergent and divergent thinking; the former which can generate one correct answer, and the latter which goes off in different directions without producing a unique answer [2]. Currently there is no general consensus on the definition of convergent and divergent thinking, modern
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
