Dual process theories of creativity suggest that creative thought is supported by both a generation phase, where unconstrained ideas are generated and combined in novel ways, and an evaluation phase, where those ideas are filtered for usefulness in context. Neurocognitively, both the default mode network (DMN) and the executive control network (ECN) have been implicated in generation and evaluation, respectively. Importantly, generating and evaluating ideas implies that the same information, reflected in patterns of neural activity, must be present in both phases, suggesting that information should be 'reinstated' (i.e. multidimensional patterns must reappear) within and/or between network nodes. In the present study, we used representational similarity analysis (RSA) to investigate the extent to which nodes of the DMN and ECN reinstate information between a generation phase, in which participants generated novel or appropriate word associations to single nouns, and an evaluation phase, where we presented the associations back to participants to evaluate them. We showed strong evidence for reinstatement within the ECN dorsal lateral prefrontal cortex during the novel association task, and within the DMN medial prefrontal cortex during the appropriate association task. We additionally showed between network reinstatement between the ECN dorsal lateral prefrontal cortex and the DMN posterior parietal cortex during the novelty task. These results demonstrate the importance of both within- and between-informational reinstatement for generating and evaluating ideas, and implicate both the DMN and ECN in dual process models of creativity.

We review several new and emerging methods of non-invasive neuromodulation and consider their potential to enhance creative cognition. This review covers the following techniques: transcranial electric stimulation (tES) (which includes transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS)), transcranial magnetic stimulation (TMS), transcranial focused ultrasound stimulation (tFUS), and neurofeedback training (NFT). For each technique, we explain the basic mechanism of action, review relevant research demonstrating its ability to enhance creative cognition, consider limitations and advantages, and suggest future research directions. Lastly, we offer broader conclusions and recommendations for the field of creativity neuromodulation.

Empathy research has long emphasized accuracy when imagining other minds. We explore whether empathy can be a creative process, where people think of multiple diverging possibilities of others' experiences. We developed two tasks to measure creative empathy. First, we adapted forward flow to measure the dynamic unfolding of creativity while imagining other minds, quantified as semantic distance between mental state concepts when freely associating the contents of other minds. Second, we developed a divergent thinking task where participants reflect on others' mental states and responses are scored using subjective and automated methods. In Studies 1-3, participants instructed to be creative showed higher scores than those instructed to be accurate and a no-instruction control, demonstrating that people vary in how creatively they approach empathy. In Study 4, participants instructed to be empathic (vs. objective) toward a target showed greater creativity on the divergent thinking task, demonstrating that empathy can produce creativity. Creativity on these tasks were inconsistently associated with trait and state empathy measures, suggesting complex relationships between creative empathy and empathic outcomes. Overall, these findings support a novel approach to measuring empathy that accounts for creative processes, broadening the scope of empathy and creativity research.

Music is a complex system consisting of many dimensions and hierarchically organized information-the or-ganization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real -world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sen-sitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

Openness to Experience is most strongly related to aspects of high-level cognition, such as creativity. Yet, the role of cognitive capacities in Openness is still far from understood. We examine how individuals search their memory predicts levels of Openness. Participants (N = 163) had one minute to generate synonyms to the word hot, which operationalizes mental navigation through a multidimensional representation of the mental lexicon - a cognitive multiplex network. We find high accuracy in low- and high- Openness group classification, and good prediction of individual differences in Openness. These results support the use of computational cognitive modelling for the study of personality traits. Further, our results suggest that people high in Openness engage in a distinct style of cognitive search.

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.

Creativity research commonly involves recruiting human raters to judge the originality of responses to divergent thinking tasks, such as the alternate uses task (AUT). These manual scoring practices have benefited the field, but they also have limitations, including labor-intensiveness and subjectivity, which can adversely impact the reliability and validity of assessments. To address these challenges, researchers are increasingly employing automatic scoring approaches, such as distributional models of semantic distance. However, semantic distance has primarily been studied in English-speaking samples, with very little research in the many other languages of the world. In a multilab study (N= 6,522 participants), we aimed to validate semantic distance on the AUT in 12 languages: Arabic, Chinese, Dutch, English, Farsi, French, German, Hebrew, Italian, Polish, Russian, and Spanish. We gathered AUT responses and human creativity ratings (N= 107,672 responses), as well as criterion measures for validation (e.g., creative achievement).We compared two deep learning-based semantic models—multilingual bidirectional encoder representations from transformers and cross-lingual language model RoBERTa—to compute semantic distance and validate this automated metric with human ratings and criterion measures. We found that the top-performing model for each language correlated positively with human creativity ratings, with correlations ranging from medium to large across languages. Regarding criterion validity, semantic distance showed small-to-moderate effect sizes (comparable to human ratings) for openness, creative behavior/achievement, and creative self-concept. We provide open access to our multilingual dataset for future algorithmic development, along with Python code to compute semantic distance in 12 languages. © 2023 American Psychological Association

Creativity has long been thought to involve associative processes in memory: connecting concepts to form ideas, inventions, and artworks. However, associa-tive thinking has been difficult to study due to limitations in modeling memory structure and retrieval processes. Recent advances in computational models of semantic memory allow researchers to examine how people navigate a se-mantic space of concepts when forming associations, revealing key search strat-egies associated with creativity. Here, we synthesize cognitive, computational, and neuroscience research on creativity and associative thinking. This Review highlights distinctions between free-and goal-directed association, illustrates the role of associative thinking in the arts, and links associative thinking to brain systems supporting both semantic and episodic memory - offering a new perspective on a longstanding creativity theory.

The associative theory posits that creativity relates to people's ability to connect remote associations to form new ideas, based on the structure of their semantic memory. This theory has spurred several recent studies connecting semantic memory structure and associative thinking to creativity, capitalizing on advances in computational methods. To date, however, this research has almost exclusively focused on assessing creativity in the general population (e.g., assessed via divergent thinking tests), with far less work examining the role of associative thinking in eminently-creative individuals across the arts and sciences. Leveraging data collected as part of the Big-C Project-a sample of world-renowned visual artists (VIS) and scientists, and an intelligence-matched comparison group-we tested whether the ability to generate remote word associations differs as a function of creative expertise. Specifically, we used distributional semantic models to calculate the semantic distance of word associations across three conditions: a free association condition and two goal-directed conditions (common association and uncommon association). We found an interaction between domain expertise and association condition: while artists generated more distant associations overall, this effect was driven by substantially more distant responses in the free association condition. Our findings indicate that VIS spontaneously produce more remote associations-potentially due to a more interconnected semantic memory network structure-but that creative expertise is less relevant for producing associations that require goal-directed cognitive search. The findings are interpreted in the context of the ongoing debate on the domain-generality and domain-specificity of creativity.

Creativity research often relies on human raters to judge the novelty of participants’ responses on open-ended tasks, such as the Alternate Uses Task (AUT). Albeit useful, manual ratings are subjective and labor intensive. To address these limitations, researchers increasingly use automatic scoring methods based on a natural language processing technique for quantifying the semantic distance between words. However, many methodological choices remain open on how to obtain semantic distance scores for ideas, which can significantly impact reliability and validity. In this project, we propose a new semantic distance-based method, maximum associative distance (MAD), for assessing response novelty in AUT. Within a response, MAD uses the semantic distance of the word that is maximally remote from the prompt word to reflect response novelty. We compare the results from MAD with other competing semantic distance-based methods, including element-wise-multiplication—a commonly used compositional model—across three published datasets including a total of 447 participants. We found MAD to be more strongly correlated with human creativity ratings than the competing methods. In addition, MAD scores reliably predict external measures such as openness to experience. We further explored how idea elaboration affects the performance of various scoring methods and found that MAD is closely aligned with human raters in processing multi-word responses. The MAD method thus improves the psychometrics of semantic distance for automatic creativity assessment, and it provides clues about what human raters find creative about ideas.