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People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Functional neuroimaging research has recently revealed brain network interactions during performance on creative thinking tasks—particularly among regions of the default and executive control networks—but the cognitive mechanisms related to these interactions remain poorly understood. Here we test the hypothesis that the executive control network can interact with the default network to inhibit salient conceptual knowledge (i.e., pre-potent responses) elicited from memory during creative idea production. Participants studied common noun-verb pairs and were given a cued-recall test with corrective feedback to strengthen the paired association in memory. They then completed a verb generation task that presented either a previously studied noun (high-constraint) or an unstudied noun (low-constraint), and were asked to “think creatively” while searching for a novel verb to relate to the presented noun. Latent Semantic Analysis of verbal responses showed decreased semantic distance values in the high-constraint (i.e., interference) condition, which corresponded to increased neural activity within regions of the default (posterior cingulate cortex and bilateral angular gyri), salience (right anterior insula), and executive control (left dorsolateral prefrontal cortex) networks. Independent component analysis of intrinsic functional connectivity networks extended this finding by revealing differential interactions among these large-scale networks across the task conditions. The results suggest that interactions between the default and executive control networks underlie response inhibition during constrained idea production, providing insight into specific neurocognitive mechanisms supporting creative cognition.

Cognitive and neuroimaging evidence suggests that episodic and semantic memory—memory for autobiographical events and conceptual knowledge, respectively—support different aspects of creative thinking, with a growing number of studies reporting activation of brain regions within the default network during performance on creative thinking tasks. The present research sought to dissociate neural contributions of these memory processes by inducing episodic or semantic retrieval orientations prior to performance on a divergent thinking task during fMRI. We conducted a representational similarity analysis (RSA) to identify multivoxel patterns of neural activity that were similar across induction (episodic and semantic) and idea generation. At the behavioral level, we found that semantic induction was associated with increased idea originality, assessed via computational estimates of semantic distance between concepts. RSA revealed that multivoxel patterns during semantic induction and subsequent idea generation were more similar (compared to episodic induction) within the left angular gyrus (AG), posterior cingulate cortex (PCC), and left anterior inferior parietal lobe (IPL). Conversely, activity patterns during episodic induction and subsequent generation were more similar within left parahippocampal gyrus and right anterior IPL. Together, the findings point to dissociable contributions of episodic and semantic memory processes to creative cognition and suggest that distinct regions within the default network support specific memory-related processes during divergent thinking. •Creative thinking involves semantic and episodic memory retrieval.•Memory inductions were given prior to creative thinking during fMRI.•Representational similarity analysis assessed induction and idea generation activity.•Semantic induction boosted semantic distance of generated ideas.•Default network regions support specific memory processes for creative thinking.

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

Despite increased focus on well-being in the psychological sciences in recent years, cross-sectional and group-level methods fall short of capturing the dynamics of individual people’s well-being in daily life. Such an investigation is especially important for young adults who are in a developmental phase in which they are more likely to experience fluctuations in their daily lives. We used ecological momentary assessment over one week in two samples of first-year college students (Sample 1, N  = 103, assessments = 2,535; Sample 2, N  = 76, assessments = 1,796) and a dynamic network approach (Dynamic Exploratory Graph Analysis). This approach estimates, within each person, how well-being elements fluctuate across days and co-vary with one another, recovering both their differentiation (exclusivity) and their interdependence (connectivity). In this approach, elements of well-being—measured via the PERMA framework—are modeled as a dynamic network to address the following research questions: (1) How do elements of well-being in young adults’ daily life form a dynamic network? (2) Does the structure of well-being hold across all young adults, or is there heterogeneity? (3) Is there synchrony among well-being elements, and do they drive a person’s well-being network? Findings suggest that whereas group-level dynamic well-being networks align with theoretical models, each person’s individual experiences vary significantly, with each person demonstrating unique well-being network structures and synchronous elements. These findings underscore the importance of considering individual variability in well-being, highlighting the necessity for personalized approaches to understand and promote well-being among young adults.

Aesthetic emotions are defined as emotions arising when a person evaluates a stimulus for its aesthetic appeal. Whether these emotions are unique to aesthetic activities is debated. We address this debate by examining if recollections of different types of engaging activities entail different emotional profiles. A large sample of participants were asked to recall engaging aesthetic (N = 167), non-aesthetic (N = 160), or consumer (N = 172) activities. They rated the extent to which 75 candidate aesthetic emotions were evoked by these activities. We applied a computational psychometric network approach to represent and compare the space of these emotions across the three conditions. At the behavioral level, recalled aesthetic activities were rated as the least vivid but most intense compared to the two other conditions. At the network level, we found several quantitative differences across the three conditions, related to the typology, community (clusters) and core nodes (emotions) of these networks. Our results suggest that aesthetic and non-aesthetic activities evoke emotional spaces differently. Thus, we propose that aesthetic emotions are distributed differently in a multidimensional aesthetic space than for other engaging activities. Our results highlight the context-specificity of aesthetic emotions.

It has been widely assessed that very preterm children (<32 weeks gestational age) present language and memory impairments compared with full-term children. However, differences in their underlying semantic memory structure have not been studied yet. Nevertheless, the way concepts are learned and organized across development relates to children’s capacities in retrieving and using information later. Therefore, the semantic memory organization could underlie several cognitive deficits existing in very preterm children. Computational mathematical models offer the possibility to characterize semantic networks through three coefficients calculated on spoken language: average shortest path length (i.e., distance between concepts), clustering (i.e., local interconnectivity), and modularity (i.e., compartmentalization into small sub-networks). Here we assessed these coefficients in 38 very preterm schoolchildren (aged 8–10 years) compared with 38 full-term schoolchildren (aged 7–10 years) based on a verbal fluency task. Using semantic network analysis, very preterm children showed a longer distance between concepts and a lower interconnectivity at a local level than full-term children. In addition, we found a trend for a higher modularity at a global in very preterm children compared with full-term children. These findings provide preliminary evidence that very preterm children demonstrate subtle impairments in the organization of their semantic network, encouraging the adaptation of the support and education they receive.

People’s knowledge about the arts shapes how they experience and engage with art. Since its introduction, the 10-item Aesthetic Fluency Scale has been widely used to measure self-reported art knowledge. Drawing from findings and researchers’ experience since then, the present work develops and evaluates a Revised Aesthetic Fluency Scale using item response theory to broaden its scope (36 items) and refine its response scale. In a large sample ( n = 2,089 English-speaking adults), Study 1 found strong evidence for unidimensionality, good item fit, and a difficulty level suitable for its targeted population; Study 2 ( n = 392) provided initial evidence for score validity via relationships with art engagement, Openness to Experience, and aesthetic responsiveness; and Study 3 derived a brief, 10-item form for time-constrained projects. Taken together, the revised scales build upon lessons learned from the original and appear promising for the next generation of research.

As cultural consumption increasingly moves to a digital space, it is crucial to understand the evolving landscape of art consumption both in and outside of a physical museum context. The current study delves into this contrast, seeking to understand how art is perceived and appreciated in museums and on a digital medium (like a computer screen). Across two experiments at the Barnes Foundation and Penn Museum in Philadelphia, Pennsylvania, we explored how the aesthetic engagement of paintings and artifacts is influenced by the physical context in which an artwork is encountered and by the characteristics of the viewer. Our findings suggest that the cognitive and emotional impacts of artworks on viewers, as well as the viewers’ overall aesthetic experiences are comparable across physical museum spaces and digital platforms. However, participants reported gaining more understanding from art viewed in museums, compared to participants who viewed art in the lab. Art experience and openness to experience influenced aesthetic impacts and ratings differently in the museum and in the lab. Overall, routes to broader valuations of liking were more similar than different between the museum and lab contexts, whereas patterns of impacts that might lead to new knowledge or understanding gained differed between museum and lab contexts. As digital technologies are increasingly integrated into diverse processes in museums such as collections management, curation, exhibitions, and education and learning, our research highlights how museums can leverage digital expansion to achieve their missions as centers of learning and education.

Education is central to the acquisition of knowledge, such as when children learn new concepts. It is unknown, however, whether educational differences impact not only what concepts children learn, but how those concepts come to be represented in semantic memory—a system that supports higher cognitive functions, such as creative thinking. Here we leverage computational network science tools to study hidden knowledge structures of 67 Swiss schoolchildren from two distinct educational backgrounds—Montessori and traditional, matched on socioeconomic factors and nonverbal intelligence—to examine how educational experience shape semantic memory and creative thinking. We find that children experiencing Montessori education show a more flexible semantic network structure (high connectivity/short paths between concepts, less modularity) alongside higher scores on creative thinking tests. The findings indicate that education impacts how children represent concepts in semantic memory and suggest that different educational experiences can affect higher cognitive functions, including creative thinking.