Enhancing user creativity: Semantic measures for idea generation
This work addresses the need for tools to monitor and enhance human creativity in cognitive science and AI applications, though it is incremental in applying existing semantic measures to a new dataset.
The authors tackled the problem of predicting the success of creative ideas in real-world design problem-solving by analyzing a dataset using 49 semantic measures, finding that divergence of semantic similarity, increased information content, and decreased polysemy predict idea success, with client feedback enhancing these effects.
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.