Divergent Semantic Integration (DSI): Extracting Creativity from Narratives with Distributional Semantic Modeling

Abstract

We developed a novel conceptualization of one component of creativity in narratives by integrating creativity theory and distributional semantics theory. We termed the new construct, divergent semantic integration (DSI), defined as the extent to which a narrative connects divergent ideas. Across nine studies, 27 different narrative prompts and over 3,500 short narratives, we compared six models of DSI that varied in their computational architecture. The best performing model employed Bidirectional Encoder Representations Transformer (BERT), which generates context-dependent numerical representations of words (i.e., embeddings). BERT DSI scores demonstrated impressive predictive power, explaining up to 72% of the variance in human creativity ratings, even approaching human inter-rater reliability for some tasks. BERT DSI scores showed equivalently high predictive power for expert and non-expert human ratings of creativity in narratives. Critically, DSI scores generalized across Ethnicity and English language proficiency, including individuals identifying as Hispanic and L2 English speakers. The integration of creativity and distributional semantics theory has substantial potential to generate novel hypotheses about creativity and novel operationalizations of its underlying processes and components. To facilitate new discoveries across diverse disciplines, we provide a tutorial with code (osf.io/ath2s) on how to compute DSI and a web app (semdis.wlu.psu.edu) to freely retrieve DSI scores.