Metaphors are frequently used during communication, offering insight into creativity and cognition through figurative language. In bilingual individuals, metaphor production is influenced by language proficiency and cultural context. However, there is no psychometrically validated test measuring metaphor production ability beyond English. This study develops a tool to assess bilingual metaphor production using simple prompts (e.g., “The small dog is...”) that require original metaphors (e.g., “a tiny tornado”). Using automatic item generation with GPT-4, we generated 100 metaphor prompts, evenly split between English and Spanish. A sample of 160 Spanish-English bilinguals on Prolific provided responses. To evaluate metaphor originality, 100 Spanish-English bilingual raters scored responses using a planned missing data design. We applied a graded response model (GRM) to compute factor scores for each item and person, yielding sufficient rater agreement and measurement precision across both languages (Factor Determinacy Index = .77, ranging from .58 to .87). To further develop the assessment tool, additional factor analysis identified the most effective items, forming a short test of 10 items per language. This research provides a foundation for developing reliable and valid assessments of bilingual metaphor production, advancing the understanding of creative language use in multilingual populations and supporting linguistic assessment applications.

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain's Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N = 2433)-constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N = 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.

Across development, experience has a strong impact on the way we think and adapt. School experience affects academic and social-emotional outcomes, yet whether differences in pedagogical experience modulate underlying brain network development is still unknown. In this study, we compared the brain network dynamics of students with different pedagogical backgrounds. Specifically, we characterized the diversity and stability of brain activity at rest by combining both resting-state fMRI and diffusion-weighted structural imaging data of 87 4-18 years old students experiencing either the Montessori pedagogy (i.e., student-led, trial-and-error pedagogy) or the traditional pedagogy (i.e., teacher-led, test-based pedagogy). Our results revealed spatiotemporal brain dynamics differences between students as a function of schooling experience at the whole-brain level. Students from Montessori schools showed overall higher functional integration (higher system diversity) and neural stability (lower spatiotemporal diversity) compared to traditionally schooled students. Higher integration was explained mainly through the cerebellar (CBL) functional network. In contrast, higher temporal stability was observed in the ventral attention, dorsal attention, somatomotor, frontoparietal, and CBL functional networks. This study suggests a form of experience-dependent dynamic functional connectivity plasticity, in learning-related networks.

Standard learning assessments like multiple-choice questions measure what students know but not how their knowledge is organized. Recent advances in cognitive network science provide quantitative tools for modeling the structure of semantic memory, revealing key learning mechanisms. In two studies, we examined the semantic memory networks of undergraduate students enrolled in an introductory psychology course. In Study 1, we administered a cumulative multiple-choice test of psychology knowledge, the Intro Psych Test, at the end of the course. To estimate semantic memory networks, we administered two verbal fluency tasks: domain-specific fluency (naming psychology concepts) and domain-general fluency (naming animals). Based on their performance on the Intro Psych Test, we categorized students into a high-knowledge or low-knowledge group, and compared their semantic memory networks. Study 1 (N = 213) found that the high-knowledge group had semantic memory networks that were more clustered, with shorter distances between concepts-across both the domain-specific (psychology) and domain-general (animal) categories-compared to the low-knowledge group. In Study 2 (N = 145), we replicated and extended these findings in a longitudinal study, collecting data near the start and end of the semester. In addition to replicating Study 1, we found the semantic memory networks of high-knowledge students became more interconnected over time, across both domain-general and domain-specific categories. These findings suggest that successful learners show a distinct semantic memory organization-characterized by high connectivity and short path distances between concepts-highlighting the utility of cognitive network science for studying variation in student learning.

Abstract Undergraduate and graduate students need professional development skills to form expertise applicable to any job or future career. Mentoring is a way that students can learn how to engage in professional development. Likewise, students can learn professional development skills from mentors who they look to for expanding their knowledge base. To help address the needs of undergraduate and graduate students in engineering, the principal investigator developed and facilitated the Mentoring and Professional Development in Engineering Education (MPD-E2) Program. For this study, we examined the program's general functions and elements using session notes and discussion of our observations. The guiding research question for this study is: what are some elements of a mentoring and professional development program that students value? In this work, we present details about the elements of the program that support student development and insights about potential future opportunities for these types of programs.

The visual modality is central to both reception and expression of human creativity. Creativity assessment paradigms, such as structured drawing tasks Barbot (2018), seek to characterize this key modality of creative ideation. However, visual creativity assessment paradigms often rely on cohorts of expert or naive raters to gauge the level of creativity of the outputs. This comes at the cost of substantial human investment in both time and labor. To address these issues, recent work has leveraged the power of machine learning techniques to automatically extract creativity scores in the verbal domain (e.g., SemDis; Beaty & Johnson 53, 757-780, 2021). Yet, a comparably well-vetted solution for the assessment of visual creativity is missing. Here, we introduce AuDrA - an Automated Drawing Assessment platform to extract visual creativity scores from simple drawing productions. Using a collection of line drawings and human creativity ratings, we trained AuDrA and tested its generalizability to untrained drawing sets, raters, and tasks. Across four datasets, nearly 60 raters, and over 13,000 drawings, we found AuDrA scores to be highly correlated with human creativity ratings for new drawings on the same drawing task (r = .65 to .81; mean = .76). Importantly, correlations between AuDrA scores and human raters surpassed those between drawings' elaboration (i.e., ink on the page) and human creativity raters, suggesting that AuDrA is sensitive to features of drawings beyond simple degree of complexity. We discuss future directions, limitations, and link the trained AuDrA model and a tutorial (https://osf.io/kqn9v/) to enable researchers to efficiently assess new drawings.

Fostering creativity is vital for tackling 21st-century challenges, and education plays a key role in nurturing this skill. According to the associative theory, creativity involves connecting distant concepts in semantic memory. Here, we explore how semantic memory changes following an educational intervention intended to promote creativity. Specifically, we examine how a scientific education curriculum-Scientific Creativity in Practice (SCIP) program-impacts the semantic memory networks of 10-18-year-old students in a chemistry class (n = 176). Students in an Intervention group who received the SCIP intervention, and a Control group who did not, completed creative thinking tests, as well as verbal fluency tasks to estimate semantic networks in science-specific (chemistry) and domain-general (animal) categories. Results showed that the SCIP intervention enhanced performance on one test of scientific creative thinking but showed no significant difference on another. Using network science methods, we observed increased interconnectedness in both science-specific and domain-general categories, with lower path distances between concepts and reduced modularity. These traits define a 'small-world' network, balancing connections between closely related and remote concepts. Notably, the chemistry semantic network showed substantially more reorganization, consistent with the chemistry contents of the SCIP intervention. The findings suggest that semantic memory reorganization may be a cognitive mechanism underlying successful creativity interventions in science education.

Creative divergent thinking involves the generation of unique ideas by pulling from semantic memory stores and exercising cognitive flexibility to shape these memories into something new. Although cognitive abilities decline with age, semantic memory tends to remain intact. This study aims to utilize that memory to investigate the effectiveness of a brief cognitive training to improve creative divergent thinking. Older adults were trained using a semantic retrieval strategy to improve creativity in the Alternate Uses Task (AUT) and the Divergent Association Task (DAT). Participants were tested on the AUT and DAT across three time points: before the strategy was introduced (T0 and T1) and afterward (T2). Results showed that the strategy enhances idea novelty in the AUT; additionally, participants that initially scored lowest on the AUT showed the greatest increase in AUT performance. This finding suggests that older adults can use a semantic retrieval strategy to enhance creative divergent thinking.

Overinclusive thinking, characterized by flexible category boundaries and broad associative networks, is closely linked to creativity. However, there has been a lack of rigorous psychometric evaluation of tasks measuring overinclusive thinking. In this paper, we introduce the Categorical Overinclusive Thinking Task (COverTT) and evaluate its reliability and validity. Across three studies involving 749 participants, we explore the relationship between overinclusive thinking, creativity, openness, creative behaviors, intelligence, and schizotypy. Our findings demonstrate that the COverTT is a reliable tool for assessing overinclusive thinking. Furthermore, we discuss the relationship between the COverTT and the criterion validity measures. To enable other researchers to readily use the COverTT for measuring overinclusive thinking, we provide open access to our data, code, and materials at OSF (https://osf.io/c3phq/?view_only=2973ca020429414db1ea069ede02bed5).

• Introduces two automated transformer models for assessing cognitive question complexity: high-performing LLaMA 2 and efficient, robust RoBERTa. • These fine-tuned transformer language models greatly outperform surface-based and semantic-distance baselines. • Both models predict open-ended question complexity and align strongly with human ratings. • Introduces PrediQT , a free web app for automated question-complexity scoring. • Bridges creativity, cognition, and AI for educational assessment innovation. Question-asking, an essential yet understudied activity, holds significant implications for learning, creativity, and cognitive development. Research shows that asking complex, open-ended questions is better for learning than closed ones. Previous research has explored open-ended question complexity through Bloom's taxonomy, but the measurement of complexity remains challenging. Recent advancements in natural language processing have enabled automated scoring of psychological tasks aligned to human-ratings. However, automatic assessment of open-ended questions remains understudied. We address this gap by fine-tuning transformer language models to predict human ratings of open-ended question complexity and comparing them to existing baseline measures (i.e., word count and semantic distance). Using previously collected human-rated responses and Bloom ratings from a creative question-asking task, we trained an encoder model (RoBERTa) and a Large Language Model (Llama-2-7B). Our results reveal that RoBERTa correlated strongly with human ratings of complexity (r = .73), exceeding baseline measures and offering an efficient, lightweight solution suitable for broad adoption. Our fine-tuned LLaMA 2 model achieved stronger performance (r = .84), establishing a new benchmark for predictive accuracy. Thus, we demonstrate how language models can be utilized to automatically score the complexity of open-ended questions. Importantly, LLaMA 2 demonstrates higher accuracy, while RoBERTa provides a replicable, accessible, and cost-effective option for everyday educational and psychological applications. Our work paves the way for automatic assessment of open-ended questions, which are critical across a wide range of cognitive domains.