No 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 3500 short narratives, we compared six models of DSI that varied in their computational architecture. The best-performing model employed Bidirectional Encoder Representations from Transformers (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 nonexpert 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 (osf.io/ath2s) to freely retrieve DSI scores.

Narrative text permeates our lives from job applications to journalistic stories to works of fiction. Developing automated metrics that capture creativity in narrative text has potentially far reaching implications. Human ratings of creativity in narrative text are labor-intensive, subjective, and difficult to replicate. Across 27 different story prompts and over 3,500 short stories, we used distributional semantic modeling to automate the assessment of creativity in narrative texts. We tested a new metric to capture one key component of creativity in writing – a writer’s ability to connect divergent ideas. We termed this metric, word-to-word semantic diversity (w2w SemDiv). We compared six models of w2w SemDiv 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). The BERT w2w SemDiv scores demonstrated impressive predictive power, explaining up to 72% of the variance in human creativity ratings, even exceeding human inter-rater reliability for some tasks. In addition, w2w SemDiv scores generalized across Ethnicity and English language proficiency, including individuals identifying as Hispanic and L2 English speakers. We provide a tutorial with R code (osf.io/ath2s) on how to compute w2w SemDiv. This code is incorporated into an online web app (semdis.wlu.psu.edu) where researchers and educators can upload a data file with stories and freely retrieve w2w SemDiv scores.

We report an analysis of lexical noun phrases (NPs) in narrative and expository texts written by Dutch deaf individuals from a bimodal bilingual perspective. Texts written by Dutch deaf children and adults who are either proficient in Sign Language of the Netherlands (SLN) or low-proficient in SLN were compared on structures that either overlap in Dutch and SLN (presence of overt subject and object NPs, NP modifiers, and NP-internal agreement), or are absent in SLN (articles). We found that deaf participants experienced significant difficulty with lexical NPs. Further, deaf proficiently signing children (but not adults) more often omitted obligate articles than deaf low-proficiently signing children. Deaf proficiently signing children and adults did not differ from low-proficiently signing children and adults, however, in the use of NP modifiers, NP-agreement errors and omissions of obligatory NPs. We conclude that proficiency in sign language seems to affect particularly those aspects that differ substantially across sign language and oral language, in this case, articles. We argue that adopting a bimodal bilingual approach is important to understand the writing of deaf children.