Neural mechanisms underpinning the association between visual arts education and creativity

Abstract

Visual arts education has been linked to cognitive and neural benefits, yet the neural mechanisms associated with creativity remain unclear. This study examined how long-term engagement in design-related visual arts education relates to creative performance and brain function. Using a quasi-experimental design with propensity score matching, we compared design majors to matched non-design majors. Participants completed visual art creative tasks (product and book cover design) and divergent thinking tasks (AUT, TTCT-figural) during fNIRS recording. The design group outperformed peers across tasks and showed greater left dorsolateral prefrontal activation during early idea generation, while non-design peers relied more on sensory and motor regions. Functional connectivity revealed reduced coupling within task-relevant circuits, indicating greater neural efficiency. Dynamic network analysis showed design majors spent more time in efficient states and switched between states more flexibly. These findings suggest that design-related visual arts education may support creativity through efficient and flexible brain network engagement.

Introduction

Creativity is widely regarded as a fundamental aspect of human cognition and a driving force behind innovation across diverse domains, ranging from the arts to science and technology¹. It has been defined as the ability to produce novel ideas or behaviors that are meaningful and effective². In response to the increasing demand for adaptive, problem-solving skills in an ever-more complex world, arts education has emerged as a powerful tool for cultivating creative potential³. Among the various forms of arts education, visual arts education has garnered noteworthy attention for its potential to enhance not only artistic abilities but also broader cognitive and emotional development. In the present study, our focus is on design education, which, in a broad sense, can be regarded as a branch of visual arts education that encompasses systematic training in drawing, sketching, visual imagination, and creative problem-solving through design practice⁴.

Visual arts education, which provides students with opportunities for creative expression and personal growth, has long been recognized as a valuable component of a well-rounded education⁵. According to the theory of aesthetic cognitivism, art holds not only aesthetic value derived from sensory experience but also cognitive value in promoting learning, critical thinking, imagination, and creativity^6,7. Recent studies have shown that visual arts education not only fosters domain-specific creativity, such as sketching and visual imagination, but also enhances domain-general creativity, including divergent thinking^3,8–12. For example, recent studies on design education (Teng et al. 2022, 2025) found that students majoring in design arts demonstrated higher levels of visual artistic creativity and divergent thinking compared to their peers in non-design disciplines^9,10. Similarly, Catterall (2002) reported that students engaged in arts education showed enhanced creativity in non-artistic domains, suggesting that the benefits of artistic training extend beyond the confines of the arts³. Furthermore, visual arts education has been linked to improved cognitive processes related to creativity, including the enhancement of mental imagery¹³, heuristic thinking¹⁴, conceptual flexibility¹⁵, and so on. These cognitive processes are often thought to be nurtured through the immersive, process-oriented nature of art-making, which encourages exploration, risk-taking, and non-linear thinking¹⁶.

Visual arts education has been shown to influence neural plasticity, which refers to the brain’s capacity to reorganize itself by forming new neural connections in response to learning and experience¹⁷. Neuroimaging studies have provided preliminary evidence that engagement in artistic activities can lead to structural and functional changes in the brain^18–25. For instance, visual artists have been found to display enhanced connectivity in brain networks related to inhibitory control, spatial imagination, and mental rotation¹⁸. A longitudinal study by Schlegel et al. (2015) further explored the impact of art training (painting) on brain structure and function, revealing that three months of painting training enhanced sketching abilities and visual divergent thinking without affecting perceptual skills. Notably, improved sketching proficiency was associated with changes in the right anterior lobe of the cerebellum, while reduced integrity of the frontal white matter was observed¹⁹. Chamberlain et al. (2014) also identified structural changes in the right medial frontal gyrus and left anterior cerebellum as representational drawing skills improved in art students compared to non-art students²⁰. Remarkably, Likova (2012) demonstrated that even in individuals who are congenitally blind, learning to draw through tactile memory can rapidly reorganize the primary visual cortex (V1), highlighting the role of training-induced plasticity in non-visual memory tasks. These findings suggest that the immersive and repetitive nature of artistic practice may contribute to long-lasting neural adaptations that support creative performance, even in populations with unique neural demands²¹.

Despite growing evidence of the cognitive and neural benefits of visual arts education, a notable lack of detailed investigation remains into the neural foundations of how visual arts education influences different types of creativity, particularly the dynamic neural activity across the various stages of visual artistic creativity. To address this gap, the present study employs dynamic brain network analysis to examine the temporal and phase-specific reorganization of brain networks during creative processes. Given that our tasks involved considerable movement (e.g., drawing), functional near-infrared spectroscopy (fNIRS) was selected for its tolerance to motion and suitability for naturalistic creative tasks. Beta increments are widely used in fNIRS studies to capture task-related brain activation during creative processes^26–28. They provide a sensitive index of neural engagement in idea generation, while baseline correction helps ensure that these increments reflect task-specific changes rather than pre-existing differences²⁹. Accordingly, the present study examined beta increments between experimental phases and baseline as indicators of creativity-related neural activity. By monitoring brain activity during three major stages of the creative design task—idea generation, idea sketching, and idea refinement—we aimed to uncover the phase-specific neural mechanisms that underlie artistic creativity. This approach provides a dynamic, process-oriented perspective on how arts education relates to neural adaptations underlying creative cognition. In addition, we extended the scope of investigation to divergent thinking tasks, offering a broader view of how sustained engagement in visual arts education may support both domain-specific and domain-general creativity.

To this end, a quasi-experimental design was employed, comparing students with extensive design arts training (design majors) to those without such training (non-design majors). However, such direct comparisons may introduce confounding effects due to differences in key covariates between the groups. For instance, variations in age, gender, grade, intelligence, and visual working memory might influence creative performance, making it challenging to disentangle the contribution of design arts training from these other factors. These variables were selected based on prior research linking age and gender to creativity^30,31 and highlighting the role of intelligence and working memory in creative performance^32,33. In particular, we included visual working memory given its relevance to visual artistic creativity. To address this and improve comparability between groups, propensity score matching (PSM) was employed to control for these factors³⁴, providing a more balanced basis for assessing the relationship between visual arts education and creativity. Participants were then fitted with an fNIRS device to record brain activity at rest and while completing different creativity tasks, with the order of tasks randomized (see Fig. 1a). The tasks included: Product design task (see Fig. 1b), in which participants design an innovative suitcase with multiple phases of idea generation and revision; Book cover design task (see Fig. 1c), where participants design a book cover for a wuxia novel, progressing through phases of idea thinking, idea generation, and idea revision; Verbal and visual divergent thinking tasks (see Fig. 1d), which include the Alternative Uses Task (AUT) to measure verbal divergent thinking, and the Torrance Test of Creative Thinking – Figural version (TTCT-figural) to assess visual divergent thinking in generating ideas. Additionally, resting-state brain activity is recorded to serve as a baseline for comparison with task-related brain activation. The fNIRS data analysis mainly includes three components: estimation of brain activation levels using a general linear model (GLM), comparing the beta increment between experimental phases and resting baseline activity³⁵; assessment of functional connectivity between brain regions through neural coupling (NC) strength, measured by Pearson correlation coefficients³⁶; and dynamic brain network analysis using a sliding window approach, followed by k-means clustering to identify distinct dynamic states of neural coupling, which are then analyzed using graph-based network metrics to explore the efficiency of information processing within the brain^37,38. Figure 2 illustrates the experimental setup, probe locations, and the fNIRS data analysis procedure. Weighted independent samples t-tests are used to compare the design and non-design groups, identifying differences in creativity task performance, brain activity, and dynamic brain network connectivity patterns. All p-values were corrected using the FDR method.

Fig. 1. Experimental flowchart in the study.

a General experiment procedure. The order of tasks was random. b Product Design Task (PD) procedure. c Book Cover Design Task (CD) procedure. d The Alternative Uses Task (AUT) or a subset of the Torrance Test of Creative Thinking figural version (TTCT-figural) procedure. I Instruction, R Rest.

Fig. 2. Experimental setup, probe location and fNIRS data analysis.

a Experimental setup. b Optode probe set. c The procedure of the fNIRS data analysis.

Based on the above literature, the hypotheses of our study were as follows: Students in the design arts group would demonstrate better performance on both visual artistic creativity tasks and divergent thinking tasks compared to the non-design group. Additionally, the design arts group was expected to show distinct activation patterns in brain regions associated with cognitive control and visual imagination, such as sensorimotor regions. Finally, we hypothesized that the design group would exhibit a more streamlined, task-specific neural network characterized by reduced connectivity between certain brain regions, reflecting a structure optimized for creative performance.

Results

Propensity score matching results

After propensity score matching using the optimal full matching method, none of the covariates exceeded the predefined imbalance thresholds (SMD < 0.2, VR < 2), yielding acceptable balance across both groups, as indicated in Table 1. Furthermore, normalized weights (summing to 1 within each group) derived from this matching procedure were applied in subsequent weighted independent-sample t-tests to compare the two groups. After weighting, the matched dataset yielded an effective sample size (ESS) of 22.09 for the control group (e.g., non-designer group) and 39 for the designer group.

Table 1.

Covariate balance before and after matching

Covariates	SMD (Before-Mat)	SMD (After-Mat)	VR (Before-Mat)	VR (After-Mat)
Gender_female	0.00	–	0.04	–
IQ	0.21	0.09	0.62	0.81
Grade	0.22	0.07	1.43	1.44
Age	0.23	0.02	1.14	1.34
VisWMC	0.37	0.11	1.18	1.08

SMD standardized mean difference, VR variance ratio, Before-Mat before matching, After-Mat after matching, An en dash (–) in the table indicates that the measure is not applicable. Gender_female indicates the proportion of female participants. IQ was assessed using the short-form Raven’s Advanced Progressive Matrices, and VisWMC was measured using a visuospatial working memory span task.

Group difference in creative performance

Weighted independent-sample t-tests were conducted to examine group differences across the four creativity tasks, with p-values adjusted for multiple comparisons using the False Discovery Rate (FDR) correction. The results revealed significant differences across most metrics of the product design task (Fig. 3a), with the design group scoring higher in functional usefulness (t (74) = 3.58, p_corr < 0.001, Cohen’s d = 0.81), functional novelty (t (74) = 4.06, p_corr < 0.001, Cohen’s d = 0.92), appearance novelty (t (76) = 5.06, p_corr < 0.001, Cohen’s d = 1.15), aesthetics (t (74) = 9.11, p_corr < 0.001, Cohen’s d = 2.06), elaboration (t (76) = 10.13, p_corr < 0.001, Cohen’s d = 2.29), concept novelty (t (76) = 4.10, p_corr < 0.001, Cohen’s d = 0.93), imagination (t (74) = 3.07, p_corr = 0.004, Cohen’s d = 0.70), likability (t (72) = 5.71, p_corr < 0.001, Cohen’s d = 1.30), and overall evaluation (t (71) = 5.63, p_corr < 0.001, Cohen’s d = 1.28), though no significant difference was found for functional fluency (t (76) = 1.11, p_corr = 0.28, Cohen’s d = 0.25). Similarly, the design group outperformed the non-design group in the book cover design task (Fig. 3b) for content appropriateness (t (76) = 5.74, p_corr < 0.001, Cohen’s d = 1.30), content novelty (t (74) = 7.12, p_corr < 0.001, Cohen’s d = 1.63), appearance novelty (t (76) = 7.50, p_corr < 0.001, Cohen’s d = 1.70), content fluency (t (73) = 2.13, p_corr = 0.041, Cohen’s d = 0.48), aesthetics (t (72) = 11.01, p_corr < 0.001, Cohen’s d = 2.49), elaboration (t (72) = 10.67, p_corr < 0.001, Cohen’s d = 2.42), imagination (t (71) = 6.50, p_corr < 0.001, Cohen’s d = 1.47), likability (t (68) = 9.37, p_corr < 0.001, Cohen’s d = 2.12), and overall evaluation (t (72) = 8.73, p_corr < 0.001, Cohen’s d = 1.98). For the AUT task (see Fig. 3c), significant differences were observed for combined creativity scores (t (73) = 2.19, p_corr = 0.037, Cohen’s d = 0.50), while fluency showed marginal significance (t (76) = 1.98, p_corr = 0.051, Cohen’s d = 0.45), and originality did not differ between groups (t (74) = 0.82, p_corr = 0.417, Cohen’s d = 0.19). In the TTCT-figural task (see Fig. 3d), the design group scored significantly higher for fluency (t (72) = 3.47, p_corr = 0.001, Cohen’s d = 0.79), originality (t (74) = 3.47, p_corr = 0.001, Cohen’s d = 0.79), and combined creativity scores (t (68) = 3.80, p_corr < 0.001, Cohen’s d = 0.86). These results showed that the design group consistently outperformed the non-design group, particularly on measures of domain-specific creativity, with medium to large effect sizes, indicating that design arts training enhanced both domain-specific and domain-general creative abilities.

Fig. 3. Group differences in creative performance.

a Product design performance. b Book cover design performance. c AUT performance. d TTCT-figural performance. The raincloud plot shows data distribution, with dots representing unweighted raw data. *p < 0.05, **p < 0.01, ***p < 0.001.

Group difference in brain activation and their correlations with creativity performance

Weighted independent sample t-tests were conducted to examine differences in brain activation between the design and non-design groups during various phases of the four creativity tasks. p-values were adjusted for multiple comparisons using FDR correction, and only results with corrected p-values (p_corr) below 0.01 are reported. Specifically, in Channel 15 (Left dorsolateral prefrontal) during product thinking phase I-first half stage (see Fig. 4a), the design group exhibited significantly greater brain activation compared to the non-design group, t (71.59) = 3.47, p_corr = 0.042, Cohen’s d = −0.811. During product thinking phase II - second half stage (see Fig. 4b), significant differences were observed in Channel 78 (Left somatosensory association cortex), t (70.38) = − 4.29, p_corr = 0.009, Cohen’s d = 1.162. During product drawing phase-second half stage (see Fig. 4c), significant differences were also observed in Channel 78 (Left somatosensory association cortex), t (59.81) = − 3.85, p_corr = 0.046, Cohen’s d = 1.046. In Channel 31 (Left primary visual cortex) during book cover thinking phase II-second half stage (see Fig. 4d), the non-design group exhibited significantly greater brain activation compared to the design group, t (73.96) = − 3.53, p_corr = 0.044, Cohen’s d = 0.845. During book cover revision phase - first half stage (see Fig. 4e), significant differences were observed in Channel 2 (Left frontal pole), t (68.54) = − 3.18, p_corr = 0.043, Cohen’s d = 0.746, in Channel 30 (Right secondary visual cortex), t (68.54) = − 4.32, p_corr = 0.007, Cohen’s d = 1.075, and in Channel 47 (Left premotor cortex), t (68.54) = − 3.65, p_corr = 0.038, Cohen’s d = 1.044.These results demonstrated significant brain activation differences between the design and non-design groups, suggesting task-specific neural adaptations associated with creative performance.

Fig. 4. Group differences in brain activation.

a Product thinking phase I - First half stage. b Product thinking phase II - Second half stage. c Product drawing phase - Second half stage. d Book cover thinking phase II-Second half stage. e Book cover revision phase - First half stage. The raincloud plot shows data distribution, with dots representing unweighted raw data. *p < 0.05, **p < 0.01.

Next, weighted partial correlations were conducted to examine the relationships between differentially activated brain regions and creativity task performance in the two groups. p-values were adjusted for multiple comparisons using FDR correction. Correlation matrices and details of significance are provided in Supplementary Fig. 2. Representative significant results are highlighted in Fig. 5. First, during the cover revision stage (Fig. 5a), CH30 (Right secondary visual cortex) activation was significantly correlated with the cover usefulness score in the non-designer group (r = 0.379, p_corr = 0.037). However, no significant correlation was observed in the designer group (r = 0.030, p_corr = 0.918). Second, during the product thinking II stage (Fig. 5b), CH78 (Left somatosensory association cortex) activation was significantly associated with the product originality score in the non-designer group (r = 0.353, p_corr = 0.045), but not in the designer group (r = 0.276, p_corr = 0.148). Third, during the product drawing stage, CH78 activation was significantly correlated with the product imagination score in the non-designer group (r = 0.361, p_corr = 0.040); No significant correlation was observed in the designer group (r = 0.097, p_corr = 0.630) (see Fig. 5c). Additionally, CH78 activation was also significantly correlated with the product aesthetic score in the non-designer group (r = 0.418, p_corr = 0.016), while the designer group showed no significant correlation (r = −0.019, p_corr = 0.922) (see Fig. 5d).

Fig. 5. Scatter plots illustrating correlations between brain activation and creativity task performance.

a CH78 (Left somatosensory association cortex) activation during the product thinking II stage and product originality score. b CH78 activation during the product drawing stage and product imagination score. c CH78 activation during the product drawing stage and product aesthetic score. d CH30 (Right secondary visual cortex) activation during the cover revision stage and cover usefulness score. Blue dots represent the designer group, and yellow dots represent the non-designer group. The scatter dots reflect raw data without considering weighted information.

Group difference in brain connectivity and their correlations with creativity performance

Weighted independent sample t-tests were conducted to examine differences in brain connectivity between the design and non-design groups during various phases of the four creativity tasks. p-values were adjusted for multiple comparisons using FDR correction, and only results with corrected p-values below 0.01 are reported. During the product thinking phase II - first half stage (Fig. 6a), a significant group difference was observed in NC values for CH3-CH52 (Right frontal pole-Right premotor cortex), t (68.54) = 1.48, p_corr = 0.005, Cohen’s d = 0.336. In the product revision phase - first half stage (Fig. 6b), significant group differences were identified in NC values for CH3-CH80 (Right frontal pole - Right somatosensory association cortex), t (67.77) = 1.75, p_corr = 0.009, Cohen’s d = 0.397, and CH35-CH52 (Left primary visual cortex - Right premotor cortex), t (67.77) = 1.75, p_corr = 0.005, Cohen’s d = 0.397. During the product revision phase - second half stage (Fig. 6c), a significant difference was found in NC values for CH71-CH80 (Right V3 - Right somatosensory association cortex), t (73.10) = 1.08, p_corr = 0.008, Cohen’s d = 0.244. For the book cover thinking phase I - second half stage (Fig. 6d), significant group differences were detected in NC values for CH10-CH61 (Left dorsolateral prefrontal lobe - Right primary motor cortex), t (75.62) = 0.44, p_corr = 0.008, Cohen’s d = 0.099, which represents a very small effect and should be interpreted with caution, and Channel CH11-CH88 (Left inferior frontal gyrus - Left somatosensory association cortex), t (75.62) = 0.44, p_corr = 0.005, Cohen’s d = 0.099, again reflecting a weak effect size with limited practical significance. Finally, during the book cover thinking phase II - first half stage (Fig. 6e), significant connectivity differences were observed in CH32-CH46 (Left secondary visual cortex - Left somatosensory association cortex), t (74.25) = 0.69, p_corr = 0.005, Cohen’s d = 0.157. In addition to the significant findings presented in Fig. 6, three additional results reached statistical significance but are not emphasized due to their negligible effect sizes. During the book cover drawing phase - second half stage, significant differences were observed for CH46-CH70 (Left Somatosensory association cortex - Right V3), t (66.62) = − 0.12, p_corr = 0.007, Cohen’s d = −0.027, and CH6-CH62 (Left frontal pole - Right primary somatosensory Cortex), t (66.62) = − 0.12, p_corr = 0.005, Cohen’s d = −0.027. During the AUT - second half stage, significant differences were identified for CH23-CH80 (Right visual association cortex - Right somatosensory association cortex), t (75.40) = − 0.09, p_corr = 0.010, Cohen’s d = −0.021. Although statistically significant, these three findings will not be a focus in the subsequent discussion due to their minimal effect sizes.

Fig. 6. Group differences in brain connectivity.

a Product thinking phase II - First half stage. b Product revision phase - First half stage. c Product revision phase - Second half stage. d Book cover thinking phase I - Second half stage. e Book cover thinking phase II - First half stage. NC value Neural coupling value. The raincloud plot shows data distribution, with dots representing unweighted raw data. **p < 0.01. ***p < 0.001.

Next, weighted partial correlations were conducted to examine the relationships between differentially significant functional connectivity and creativity task performance in the two groups. p-values were adjusted for multiple comparisons using FDR correction. The correlation matrices and significance details are available in Supplementary Fig. 3. The analysis focused on functional connectivity that showed significant differences between the design and non-design groups. However, the results revealed no significant correlations between these differential functional connections and performance on the product design and book cover design tasks in either group.

Group difference in brain dynamic networks

Using a sliding window and k-means clustering approach, three representative dynamic neural coupling (dNC) states were identified for each creativity task. Analysis of network parameters (i.e., CC, ASPL, globE, and locE) revealed significant differences across these states (please see details in Supplementary Tables 2 to 5 and Supplementary Figs. 4 to 7). Finally, weighted independent samples t-tests were conducted to examine group differences in network state dynamics (e.g., dwell time, state ratios, and transition frequency) between the design and non-design groups.

For the product design task, State 2 exhibited a unique network profile characterized by distinct connectivity patterns and higher network efficiency compared to States 1 and 3. As shown in Fig. 7a, connectivity between channels Ch21-Ch22 and Ch45-Ch54 was lower in State 2 than in States 1 and 3 [e.g., Ch21-Ch22: F (2154) = 12.69, p = 0.012, η_P ² = 0.141; post-hoc with Bonferroni corrected ps < 0.005]. Conversely, connectivity between Ch81-Ch83 was significantly higher in State 2 than in the other two states [F (2154) = 16.75, p = 0.001, η_P ² = 0.179; post-hoc with Bonferroni corrected ps < 0.001]. Regarding GROUP differences (Fig.7b), the design group showed a higher proportion of time spent in State2 (M_weighted = 0.22) than non-design group (M_weighted = 0.16), t (75.87) = 2.79, p_corr = 0.040. Although the non-design group had a longer dwell time in State 1, this result was marginal and potentially influenced by outliers, thus not emphasized. No other significant group differences were found, ps > 0.05 (please see details in Supplementary Table 6).

Fig. 7. Brain dynamic network results during the product and book cover design task.

a The significant brain connectivity during State 1 vs. State 2 and State 3 vs. State 2 in the product design task. Red lines indicate stronger connectivity in State 1 or 3, while blue lines represent stronger connectivity in State 2. b The significant group difference in metrics of dynamic neural coupling(dNC) states (dwell time of State1 and ratio of State2 during product thinking Ⅱ-second half stage). c The significant brain connectivity during State 1 vs. State 2 and State 3 vs. State 2 in the book cover design task. Red lines indicate stronger connectivity in State 1 or 3, while blue lines represent stronger connectivity in State 2. d The significant group difference in metrics of dNC states (transition during cover thinking Ⅰ-first half stage and ratio of State 2 during cover thinking Ⅱ- second half stage). *p < 0.05.

For the cover design task, State 2 exhibited significantly lower connectivity than States 1 and 3 across multiple channel pairs, including Ch13-Ch31 [F (2154) = 10.89, p = 0.026, η_P ² = 0.124; post-hoc with Bonferroni corrected ps = 0.002], Ch20-Ch41, Ch21-Ch44, Ch56-Ch66, Ch57-Ch85, Ch66-Ch75, and Ch68-Ch73 (Fig. 7c); Conversely, connectivity in State 2 was significantly higher than States 1 and 3 for Ch56-Ch80 [F (2154) = 11.91, p = 0.017, η_P ² = 0.134]and Ch35-Ch38 [F (2,154) = 12.48, p = 0.013, η_P ² = 0.139], with all post-hoc comparisons (Bonferroni corrected) ps < 0.01(see Supplementary Table 10 for details). Regarding GROUP differences, the design group (M_weighted = 13.59) compared to the non-design group (M_weighted = 9.28) showed significantly higher transition frequency between brain states during the early cover thinking phase (t (75.63) = 2.81, p_corr = 0.044), indicating greater neural flexibility. A marginally significant Group differences was also found during the cover thinking Ⅱ first half phase, where the design group exhibited a higher ratio of State2 (M_weighted = 0.21) compared to the non-design group (M_weighted = 0.15), t (75.67) = 2.75, p_corr = 0.052 (Fig. 7d). No other significant group differences were found, ps > 0.05(please see details in Supplementary Table 7).

In the AUT and TTCT-figural tasks, although significant differences in brain network efficiency were observed across network states, no significant group differences were found in dynamic network metrics (e.g., dwell time, state ratios, or transition frequency). Detailed results are provided in the supplementary Table 8 and 9.

Discussion

This study examined behavioral and neural differences associated with sustained design-related visual arts training, focusing on creative performance as well as underlying brain activation, functional connectivity, and dynamic brain network organization. Propensity score matching (PSM) was employed to control for potential confounding variables, such as age, grade level, IQ, and visual working memory capacity, aiming to improve comparability between the design and non-design groups. Behaviorally, the design group performed better than the non-design group across creative tasks, including visual artistic creativity (product design and book-cover design) and divergent thinking (AUT and TTCT-figural). Neurally, the design group exhibited higher activation in the left dorsolateral prefrontal cortex during early product thinking, while the non-design group showed greater activation in the left somatosensory association cortex during later product thinking and drawing, the left primary visual cortex during book cover thinking, and the left frontal pole, right secondary visual cortex, and left premotor cortex during book cover revision. Functional connectivity analysis indicated that the design group demonstrated lower functional connectivity compared to the non-design group, particularly during visual artistic creative tasks. Significant differences were observed in task-relevant circuits, including connections involving the frontal pole, premotor cortex, somatosensory association cortex, and visual processing regions. Lastly, dynamic brain network analysis during creative idea generation phases revealed that the design group exhibited higher flexibility in transitioning between network states, with increased dominance of efficient network configurations (State 2).

The design group performed better than the non-design group on both divergent thinking tasks (verbal and figural) and visual artistic creativity tasks, suggesting that sustained design-related visual arts training may be associated with benefits for both domain-general and domain-specific creativity. At the same time, these group differences may also reflect pre-existing characteristics—such as higher baseline creativity, motivation, or visuospatial ability in students who choose design majors—so the results should not be taken as strong causal effects of training. Beyond baseline differences, task demands may also have varied across sub-majors within the design group. For example, visual communication students may have been advantaged in the book cover task, while product design students may have performed better in the product design task. Although our sample covered a broad range of sub-majors, the unequal representation across them could have influenced task-specific outcomes. Future studies may employ more balanced or cross-disciplinary tasks to minimize such asymmetries.

Nonetheless, these findings align with prior studies and reinforce the role of visual arts education in fostering creative abilities across domains^3,9,10,39. The domain-general theory of creativity suggests that fostering general cognitive processes, such as flexibility, associative thinking, and problem-solving, can enhance creativity across various domains⁴⁰. The significant expertise observed in the design group reflect a bi-directional relationship between domain-general and domain-specific creativity, where skills such as divergent thinking can transfer and enhance performance in specialized creative domains such as visual design. Importantly, visual arts education emphasizes iterative problem-solving, exploration, and non-linear thinking, which likely cultivate core cognitive skills such as flexibility, associative thinking, and enhanced visual-spatial processing¹⁶. Beyond domain-specific gains, it also strengthens domain-general creativity by enhancing originality and fluency in idea generation⁵ and by cultivating perspective taking and empathy that support the integration of diverse viewpoints into creative processes⁴¹. Unlike traditional education, which prioritizes knowledge transmission and standardized testing, art education provides an open environment that stimulates self-reflection and expression⁴². By incorporating arts-based approaches into educational curricula, students can develop both domain-specific and domain-general creative capacities, equipping them with essential skills for innovation and adaptability in diverse fields⁵. This is also consistent with the theory of aesthetic cognitivism, which posits that art promotes not only aesthetic appreciation but also cognitive processes that support creativity, critical thinking, and problem-solving^6,7.

Neural activation analysis revealed distinct patterns of brain activity between the design and non-design groups during creative tasks, providing insights into the neural adaptations associated with prolonged visual arts education. The design group exhibited significantly higher activation in the left dorsolateral prefrontal cortex (DLPFC) during early product thinking. The DLPFC is a critical region for executive functions, such as goal-directed planning, cognitive flexibility, and working memory, which is essential for top-down control in creative problem-solving tasks⁴³. This increased activation is consistent with the complex problem-solving demands of creative design tasks, suggesting that top-down cognitive control processes are more effectively engaged by design-trained individuals, consistent with previous findings on the role of the DLPFC in creative cognition⁴⁴. In contrast, the non-design group showed greater activation in the left somatosensory association cortex during later product thinking and drawing, suggesting the role of sensory and perceptual processing when conceptualizing and sketching their designs⁴⁵. Notably, activation in this region correlated significantly with the product originality, imagination, and aesthetic scores in the non-design group, reflecting the role of sensory-motor integration in generating creative outputs. Similarly, the non-design group exhibited higher activation in the left primary visual cortex during book cover thinking. This finding suggests that participants without visual art training might engage more in early-stage, visual-perceptual processes, focusing on the direct visual representation of ideas rather than abstract or conceptual synthesis. During the book cover revision stage, the non-design group showed higher activation in the right secondary visual cortex and left premotor cortex. The right secondary visual cortex is involved in higher-order visual processing, including the integration of complex visual features and spatial attention⁴⁶, as well as the processing of complex visual-emotional stimuli⁴⁷, suggesting the non-design group relies more on visual adjustments during revision. Additionally, the left premotor cortex, linked to motor planning and execution, reflects the physical drawing and motor preparation required during revision^48,49, which may be more challenging for non-design participants due to less experience with artistic tools. These activation patterns suggest that the design group relies more on higher-order cognitive control and conceptual integration via the DLPFC, whereas the non-design group relies more on sensory, perceptual, and motor processing.

The functional connectivity analysis revealed that the design group exhibited significantly lower neural coupling (NC) values compared to the non-design group across various stages of the creativity tasks. In the design group, desynchronization was particularly evident in task-relevant circuits, such as between the frontal pole, premotor cortex, somatosensory association cortex, and visual processing regions. For example, during the product thinking and revision phases, reduced connectivity between the right frontal pole and right premotor cortex, as well as between the left primary visual cortex and right premotor cortex, suggested a streamlined and localized neural strategy. This aligns with the neural efficiency hypothesis^50–52, which posits that higher cognitive abilities are linked to reduced brain activation during complex tasks. Specifically, Grabner et al. (2006) demonstrated that chess experts show lower activation in task-relevant networks compared to novices, supporting the idea that expertise fosters neural efficiency⁵². In the context of artistic creation, Ellamil et al. (2012) reported reduced functional connectivity between the right inferior frontal gyrus and prefrontal cortex regions during generative phases of drawing, consistent with the desynchronization observed in the design group during product thinking and revision⁵³.

In contrast, the non-design group showed stronger connectivity, particularly between default mode network (DMN) regions and task-specific areas, such as the somatosensory association cortex and primary visual cortex. For instance, during the book cover thinking and revision phases, the non-design group exhibited greater connectivity between the left inferior frontal gyrus and somatosensory association cortex. However, some of these connectivity differences, particularly in the book cover task, were associated with very small effect sizes, and thus should be regarded as weak effects and interpreted with caution. Although these effects were small and should be interpreted cautiously, they may still point to a compensatory mechanism in novices, who rely on a broad range of neural coordination to compensate for less sophisticated cognitive strategies. Beaty et al. (2018) reported that individuals with lower creative ability rely on broader network interactions, while those with higher creative ability exhibit reduced connectivity in task-relevant circuits, consistent with the current findings⁵⁴. These findings suggest that visual arts training fosters more specialized and efficient neural communication, enabling experts to perform creative tasks with greater focus and precision compared to novices. It is important to note that these differences were most apparent in art-domain tasks, such as product and book cover design, but were not observed during divergent thinking tasks. This discrepancy may indicate that divergent thinking tasks may lack the sensitivity to detect the specific neural adaptations promoted by visual arts training, or may involve more generalized cognitive processes that are equally accessible to both groups.

Importantly, the analysis of dynamic brain networks further revealed deeper differences between the design and non-design groups. For both the product and book cover design tasks, we focused on the idea-thinking phases, which are directly involved in creative idea generation. Three representative neural coupling states (State 1, State 2, and State 3) were identified for each creative task. In particular, State 2 emerged as a typical network pattern associated with increased network efficiency, characterized by lower average shortest path length, higher clustering coefficient, global and local efficiency. In addition, State 2 also showed reduced functional connectivity, echoing the desynchronization seen in static analyses. These patterns suggest streamlined neural communication that supports task-specific processing. We found that the design group exhibited a higher ratio of time spent in State 2 and greater flexibility in transitioning between states during art creative tasks (i.e., product and book cover design). These findings closely align with recent work by Chen et al. (2025), who demonstrated that creative ability is strongly predicted by the brain’s ability to dynamically switch between networks, particularly the default mode, salience, and executive control networks⁵⁵. Their study highlights that creative individuals show not only efficient static connectivity but also enhanced temporal flexibility, a hallmark of adaptive cognitive control in open-ended tasks. Our results echo this framework, suggesting that visual arts training may cultivate this neural flexibility, enabling expert individuals to engage efficient brain states more frequently and transition fluidly across network modes as task demands shift. Taken together, these findings suggest that visual arts training enhances both static neural efficiency and dynamic flexibility, enabling individuals to adaptively coordinate brain networks for optimal creative performance. This provides refined neural support for the theory of aesthetic cognitivism⁶, highlighting its cognitive benefits in fostering creativity through the development of more efficient and flexible brain network engagement.

While the present study offers valuable insights into how design-related visual arts education may be associated with creative performance and neural underpinnings, several limitations should be noted. First, although we used propensity score matching to control for age, grade, gender, IQ, and working memory, design students likely differed from controls in other ways. They had years of art training and chose art as a career path, which may reflect pre-existing differences in creativity, motivation, or visual-spatial ability. Moreover, variations in training years and cumulative artistic experience within the design group could also have influenced both creative performance and neural activity. These factors represent potential confounding influences that should be considered when interpreting the findings. Second, both groups were predominantly female, reflecting the demographics of normal universities in China. In addition, the non-design group had a higher proportion of non-STEM majors, which may also influence creativity-related findings. Moreover, the design group included students from different sub-majors (e.g., product design, visual communication, environmental design, public art, digital media art) with unequal distribution. Given that the two core design tasks involved different cognitive demands (spatial-functional vs. visual-aesthetic), such variation in academic background may have influenced task-specific performance. Together, these sample characteristics may limit the generalizability of the findings to broader populations. Third, this study used a quasi-experimental design in which propensity score matching was applied after participants had already received design-related visual arts education. While this approach improves ecological validity, it also means that some pre-existing differences may not have been fully accounted for, and thus causal interpretation remains limited. Future research should consider longitudinal or intervention-based studies that track changes in creativity and brain function over time in response to structured art training. Finally, fNIRS, while suitable for creative tasks involving movement, only captures activity in cortical surface areas. It cannot assess deeper brain regions such as the basal ganglia or hippocampus, which may also play key roles in creativity. Combining fNIRS with other imaging techniques like fMRI in future studies could provide a more complete view of the neural mechanisms involved.

The findings of this study have both theoretical and practical implications. First, the results lend further support to the theory of aesthetic cognitivism⁶, which suggests that art contributes to cognitive development beyond hedonic values associated with art and aesthetic experience, fostering creativity and cognitive flexibility. Specifically, our study indicates that design-major students, compared with non-design students, showed higher levels of both divergent thinking and artistic thinking. This suggests broader cognitive benefits of art training, which may extend to both domain-general and domain-specific creativity. The observed neural efficiency and increased brain network flexibility in design-major students not only support aesthetic cognitivism but also expand the scope of neural efficiency theory. According to this theory, expertise is linked to more efficient brain activation during complex tasks⁵². Our study offers preliminary evidence within the context of design-related visual arts expertise, suggesting that visual art training may be linked to more efficient neural processing during creative tasks. Second, the findings highlight the potential value of incorporating selected elements of design-related visual arts education, particularly design-related practices such as sketching, visual imagination, and visual-spatial problem solving, into general curricula in the form of elective or supplementary courses. These practices, beyond enhancing artistic skills, directly engage cognitive processes such as flexibility, imagination, and problem-solving, which are essential for success in various fields^5,56. Nevertheless, art education often faces resistance because it is perceived as less essential compared to subjects like mathematics and science. Our findings provide initial neuroscientific evidence indicating that design-related visual arts experience is associated with enhanced creative performance and more efficient neural processes related to creativity. While further research is needed, the present findings may inform educational discussions and encourage policymakers to consider making key elements of design-related training accessible to students through elective or supplementary courses so that its benefits can complement existing curricula.

Overall, our study mainly revealed differences between design-major students and their non-design peers in both creative performance and its underlying neural mechanisms. Behaviorally, design major students performed better than non-design peers on tasks measuring both visual artistic and divergent thinking abilities. Neurally, they showed greater activation in the left dorsolateral prefrontal cortex—an area critical for cognitive control—during early creative ideation, while the non-design group relied more on sensory and motor regions such as the left somatosensory association cortex and premotor cortex. The design group also exhibited lower functional connectivity in task-relevant circuits and greater use of efficient, flexible brain network configurations during idea generation. Together, these findings suggest that design-related visual arts education may support creativity by promoting top-down cognitive engagement and streamlined neural processing. However, given the quasi-experimental design and focus on design majors, the findings should be interpreted cautiously and not generalized beyond this context.

Methods

Transparency and Openness

This study adheres to the Transparency and Openness Promotion (TOP) Guidelines⁵⁷. All key aspects of the research, including sample size determination, task manipulations, data exclusions, and measures, are reported. The data, analysis code, and research materials are available from the corresponding author upon request. The data were analyzed using R version 4.4.0 (R Core Team, 2023) and MATLAB 2019a (The MathWorks Inc., 2019). The study design and analysis were not pre-registered. The language in the introduction and discussion sections was refined with the assistance of generative artificial intelligence (e.g., ChatGPT) to ensure clarity and coherence.

Participants

A sample of 86 university students was recruited as subjects for this study. All participants were drawn from a normal university in China to ensure consistency in academic environment. Recruiting participants was particularly challenging due to the limited availability of design major students and COVID-19 pandemic-related restrictions. Therefore, we adhered to the widely accepted rule of thumb for experimental studies, which recommends a minimum of 30 participants per group. Consequently, the design group consisted of 44 participants, and the non-design group consisted of 42 participants. However, eight participants were excluded from the analysis: one non-design participant withdrew midway, two non-design participants failed to follow task instructions, one design participant had excessive head movement, another design participant’s work was deemed inconsistent with design expertise by five professional raters, and three participants were left-handed. In the end, 78 valid participants were obtained, comprising 39 design major students (32 females, aged 22.23 ± 1.74 years, grade 4.85 ± 1.46) and 39 non-design major students (30 females, aged 22.74 ± 1.62 years, grade 5.23 ± 1.25). All design-major participants were sophomores or above and had received an average of 4.65 ± 4.30 years of drawing training prior to university, as well as 4.85 ± 1.46 years of professional design training after entering university. Within the design major group, participants were distributed across product design (25.6%), visual communication design (23.1%), environmental design (23.1%), public art (15.4%), and digital media art (12.8%), reflecting a representative range of disciplines within design education. The non-design major group consisted of students in their second year or above, none of whom had received professional or amateur training in painting, photography, or other related arts, nor reported hobbies in these areas (e.g., regularly visiting painting exhibitions). Among the 39 non-design participants, 13 (33.3%) were from STEM-related disciplines (e.g., software engineering, computer science, neurobiology, mathematics education), and 26 (66.7%) were from non-STEM fields (e.g., psychology, education, accounting, sociology, business), ensuring a broad representation of academic backgrounds. Participants should have normal or corrected-to-normal vision and should not have any serious physical or mental illnesses. Participants were paid RMB 100 for their participation. All procedures were approved, and all participants provided informed consent prior to participating in the study, as approved by the East China Normal University Committee on Human Research Protection (UCHRP) (Code: HR 132-2020).

Experimental tasks and procedure

On arrival at the laboratory, participants signed an informed consent form and completed a demographic questionnaire. The experimenter then used a PowerPoint presentation to explain the tasks and their specific requirements for each stage, ensuring that participants fully understood before proceeding. Participants were then fitted with an fNIRS device to record brain activity at rest and while completing different creativity tasks, with the order of tasks randomized (see Figs. 1a, 2a). A camera recorded the creative process, and task progress was controlled using E-Prime 2.0. After the fNIRS session, participants provided recorded explanations of their creations. Following the completion of the four fNIRS tasks and their recorded explanations, participants performed a series of cognitive tests, including a 12-item short form of the Standard Raven’s Advanced Progressive Matrices (1993 version) to assess intelligence, and the Visuospatial Working Memory Span Task⁵⁸ requiring sequential recall of locations on a 5 × 5 grid. The four creativity tasks were as follows:

Product Design Task (see Fig. 1b). This task was adapted from the paradigm developed by Kowatari et al. (2009) ¹⁸ and further refined for the present study. Participants were asked to design an innovative product—a “suitcase”—by following a set sequence of steps. The product could be improved in both function and appearance, potentially going beyond existing technological constraints. The more creative the product, the better. The task consisted of four phases. The first two phases comprised the idea thinking phase I and idea thinking phase II. In idea thinking phase I, participants analyzed the pain points and shortcomings of current suitcase designs, with two minutes allocated for silent reflection and two minutes for written articulation. In idea thinking phase II, they generated creative solutions to address the previously identified issues, again with two minutes for thinking and two minutes for writing. Next was the idea generation phase, in which participants conceptualized and sketched their design solutions over seven minutes, thinking and writing simultaneously. The final idea revision phase involved refining and optimizing the initial sketches, with three minutes allotted for revision. The duration of each phase was determined based on a pilot study with 10 participants, in which all were able to complete the tasks within this time. Our aim was to capture the initial stage of creative ideation rather than fully elaborated designs; therefore, a brief time window was considered sufficient and helped ensure focused, task-related responses.

Book Cover Design Task (see Fig. 1c). This task was adapted from Ellamil et al. (2012)⁵³ and further refined for the present study. Participants were tasked with designing a book cover for Gu Long’s wuxia novel Happy Heroes by following a structured sequence of steps. After reading the book’s synopsis, participants were instructed to create a cover. The more creative the cover design, the better. The first two phases were idea thinking phase I and idea thinking phase II. In idea thinking phase I, participants identified the thematic concept of the cover, with two minutes for silent reflection and two minutes for writing. In idea thinking phase II, they explored visual strategies to communicate this theme, again with two minutes for thinking and two minutes for writing. This was followed by the idea generation phase, during which participants had seven minutes to conceptualize and sketch their design ideas, combining both visual and written elements. Finally, in the idea revision phase, participants refined and optimized their initial sketches, with three minutes allocated for adjustments. The time for each stage was also set based on 10 pilot participants.

Both the product design and book cover design tasks have been previously used in creativity research^9,18,53 and are well-aligned with the training objectives of design-related visual arts education. Specifically, the product design task emphasizes both functional innovation and aesthetic refinement, while the book cover task engages visual communication and aesthetic expression. Together, these tasks provide appropriate and context-relevant measures of creative performance for design-trained students.

The verbal and visual divergent thinking task (see Fig. 1d). The Alternative Uses Task (AUT)⁵⁹ was used to assess verbal divergent thinking. Participants were required to generate as many unusual and original uses as possible for common objects. In this experiment, the AUT task used the item “tire”. The visual divergent thinking test was adapted from the Picture Completion task, a subset of the Torrance Test of Creative Thinking figural version (TTCT-figural)^60,61. Participants used four geometric shapes (i.e., one triangle, two circles, and one rectangle) to create meaningful pictures and then name their pictures. Each geometry figure could only be used once. The size of the figure could be altered, but not the shape. Each task had a total duration of 3 min and followed the “idea-button” paradigm⁶². Specifically, participants pressed the “Enter” key each time they thought of an idea and then recorded their answer by writing or drawing it on the answer sheet.

Assessment of performance on creative tasks

The performance of participants on the AUT and TTCT-figural tasks was assessed in terms of fluency and originality⁵⁹. Fluency scores were calculated based on the total number of ideas generated. Originality scores were determined by evaluating the uniqueness of the ideas. Each idea was rated by five trained raters on a 5-point Likert scale, ranging from 1 (not original at all) to 5 (highly original). The inter-rater agreement was found to be satisfactory, with internal consistency coefficient (ICC, calculated as Cronbach’s α) values of 0.77 for the AUT task and 0.88 for the TTCT-figural task. The final originality score for each participant was calculated by averaging the ratings from the five raters. For the product design task, five independent raters, each with at least five years of experience in design arts, rated the products on a 5-point Likert scale across 10 dimensions⁹: functional novelty, functional usefulness, functional fluency, appearance novelty, aesthetics, concept novelty, elaboration, imagination, likeability, and overall rating. The ICC values for these dimensions were 0.77, 0.68, 0.88, 0.76, 0.72, 0.79, 0.79, 0.78, 0.78, and 0.78, respectively, indicating satisfactory inter-rater agreement across all dimensions. Similarly, for the book cover design task, five independent raters evaluated participants’ designs on a 5-point Likert scale across nine dimensions⁹: content novelty, content appropriateness, content fluency, drawing novelty, aesthetics, elaboration, imagination, likeability, and overall evaluation. The ICC values for these dimensions were also satisfactory, with scores of 0.66, 0.72, 0.78, 0.89, 0.87, 0.89, 0.84, 0.86, and 0.86, respectively. The inter-rater reliability for all dimensions in both tasks met the required standards, confirming the robustness of the evaluation process.

Propensity Score Matching

To address potential confounding covariates and ensure comparability between the art and control groups, propensity score matching (PSM) was conducted using the MatchIt package in R³⁴. Missing data in the continuous covariates (e.g., IQ and visual working memory capacity) were imputed using their means. Propensity scores were estimated using logistic regression to predict group membership based on the covariates including age, grade, gender, IQ, and visual working memory capacity (VisWMC), following the foundational framework introduced by Rosenbaum and Rubin (1983)⁶³. Initially, optimal pair matching was applied, in which the sum of the absolute pair distances between participants in the designer group and the control group (e.g., the non-designer group) was minimized⁶⁴. The quality of the matching was evaluated using standardized mean differences (SMD) and variance ratios (VR) of each covariate. In psychology, SMD values⁶⁵ smaller than 0.2 and VR values⁶⁶ smaller than 2 indicated an acceptable balance. However, despite improvements, optimal pair matching did not achieve sufficient balance across all covariates. Therefore, optimal full matching, a matching algorithm that has been shown to outperform other matching algorithms in terms of balance^65,67, was applied instead. The quality of matching was assessed again using SMD, VR, and eCDF statistics for all covariates. The matched dataset was then used for subsequent analyses. However, full matching resulted in a smaller effective sample size for the control group, which may reduce statistical power and affect the stability of the results. This methodological limitation should be taken into account when interpreting the findings.

fNIRS data acquisition

In this study, functional near-infrared spectroscopy (fNIRS) data were collected using the ETG-7100 system (Hitachi Medical Corporation, Japan). The device specifically assessed the absorption of near-infrared light at wavelengths of 695 and 830 nm, with a sampling rate of 10 Hz, enabling continuous monitoring and recording of concentration changes in deoxyhemoglobin (HbR) and oxyhemoglobin (HbO) in the brain. Three optode probe sets were used during the experiment: a 3 × 5 set, a 3 × 10 set, and a 4 × 4 set. The 3 × 5 probe set consists of 7 detectors and 8 emitters, with a minimum optode separation of 3 cm, supporting 22 measurement channels. The 3 × 10 probe set consists of 15 detectors and 15 emitters, with a minimum optode separation of 3 cm, supporting 47 measurement channels. The 4 × 4 probe set consists of 8 detectors and 8 emitters, with a minimum optode separation of 3 cm, supporting 24 measurement channels. The three probe sets were strategically placed to monitor bilateral prefrontal, parietal, temporal, and occipital regions, effectively achieving near-whole-brain coverage (see Fig. 2b). The registration of the probe sets was based on the 10–20 system. For the 3 × 5 probe set, the bottom row of optodes was aligned with the horizontal reference line, with the central optode corresponding to the nasion. For the 3 × 10 probe set, the central row was aligned with the sagittal reference line, with the channel at this row corresponding to FCz. For the 4 × 4 probe set, the bottom dorsal row of optodes was aligned with the horizontal reference line, with the central channel corresponding to Pz, and the middle column aligned with the sagittal reference line. Channel locations were further determined using a 3D digitizer and converted into Montreal Neurological Institute (MNI) space coordinates. The MNI coordinates of each fNIRS channel were then assigned to the corresponding regions using the Automated Anatomical Labelling (AAL) atlas. Specifically, each channel was automatically assigned to the nearest AAL region based on its MNI coordinates, and channels within the same AAL region were grouped to form regions of interest (ROIs). The MNI co-ordinates of the optode channels of a normal participant are shown in Supplementary Table 1.

fNIRS data analysis

Several pre-processing steps have been taken to reduce noise^68,69. First, the principal component spatial filtering algorithm was applied to remove global components from the raw data. Next, we used correlation-based signal enhancement to remove motion artefacts. After correction, oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) were found to be negatively correlated (i.e., corrected HbR values were equal to the products of corrected HbO and a negative coefficient⁷⁰). Therefore, data analysis focused primarily on HbO rather than HbR. Additionally, poor channels were identified by visual inspection using NIRS time course plots, identifying channels with significantly higher variance compared to others in the same participant. The noise channels were removed and replaced with the average data of its four neighboring channels (e.g., if channel 34 is identified as a bad channel, it will be replaced with the average date of channels 30, 31, 37, and 38).

The fNIRS data analysis mainly includes three components: estimation of neural activation, estimation of neural coupling, and dynamic brain network analysis. Generally, the NIRS Statistical Parametric Mapping (NIRS_SPM) software package^37,71 was used to analyze brain activation during different phases of the four creativity tasks and the resting baseline. The haemodynamic response function (HRF) low-pass filtering and wavelet minimum description length detrending algorithms were used, as required by “NIRS_SPM”. The general linear model (GLM) was used for the estimation of neural activation. Neural activation during different phases of the four creativity tasks and the resting baseline (each phase divided into an early half and a late half) was estimated through the following steps (see Fig. 2c): First, reference waves were established for each phase of the experimental tasks and the resting baseline (with each phase divided into an early half and a late half) to reflect the theoretical changes in HbO signals triggered by the experimental stimulus. Next, regression analyses were performed using the actual brain activation of each channel as independent variables to predict the changes in the reference wave. Each channel yielded a regression coefficient beta (β). The beta increment, obtained by subtracting the beta value of the resting baseline from the beta value of each phase of the experimental tasks, was used as an indicator of brain activation levels. Beta increments were then standardized across all participants using a Z-score transformation for each channel. Finally, the beta increment from each of the 93 channels was used as the dependent variable, with participant group (design and non-design group) as the independent variable. Independent samples t-tests with the weight calculated by the propensity score were carried out to examine the difference in neural activation between the groups. The false discovery rate (FDR) correction method was used to correct the results for multiple comparisons.

Neural coupling (NC) indicates the functional connectivity between different cerebral regions during the different stage of different tasks. Neural coupling strength was quantified using correlation coefficients (see Fig. 2c). Pearson correlations were used to assess the NC between the HbO signals from each pair of channels³⁶. First, 8649 channel (CH) combinations (93 × 93 CHs) were identified. After exclusion of the redundant CH combinations [equal CH combinations (e.g., CH1-CH2 and CH2-CH1) or CH combinations of a single CH (e.g., CH1-CH1)], 4278 valid CH combinations remained. Fisher’s z-transformation was then used to convert the NC values (i.e., correlation coefficients). Similar to the analysis of the beta increment, a weighted independent samples t-test was performed for the NC group comparison, followed by correction for multiple comparisons using the FDR method.

A dynamic brain network analysis based on sliding windows and k-means clustering was conducted to characterize NC states during each creative task^37,72,73 (Fig. 2c). Sliding windows were used to segment the NC data, with different time frames analyzed for the four creativity tasks. Specifically, for the product design and book cover design tasks, data from the first two thinking stages (2 min and 3 min, respectively) were concatenated before applying the sliding window approach. For the AUT and TTCT-figural tasks, the entire 3 min duration of each task was used for the analysis. The window size was set to 2 s, with a 0.5-second step increment, spanning the entire task duration. Within each time window, NC values were averaged, resulting in a series of NC matrices. These matrices were then averaged across participants, and a k-means clustering algorithm was applied in MATLAB to assess the similarity between windowed NC matrices and identify representative NC states. The similarity was quantified using Manhattan distance⁷⁴. To determine the optimal number of states, various validity indices (e.g., the ratio of within-cluster distance to between-cluster distance) were calculated for a range of potential state numbers. These indices were plotted against the number of clusters, and the elbow criterion was applied to select the optimal number of NC states. According to this criterion, the selected state number corresponds to the “elbow” of the curve, balancing clustering precision with complexity^75,76 (Supplementary Fig. 1). In this study, k = 3 was determined to be the optimal number of clusters for each creative task. To minimize the impact of random effects, 1000 iterations were conducted, and cluster centroids representing the three NC states (State 1, State 2, and State 3) across all participants were identified. These centroids, derived from the averaged NC matrices, were then used as initial centroids for individual-level clustering, enabling the extraction of dynamic NC states for each participant.

Each state was further analyzed using graph-based network metrics computed with the GRETNA toolbox in MATLAB^77,78. In this analysis, channels were treated as nodes of the brain network, and the functional connections between them were defined as edges, forming weighted undirected networks. Then a dynamic sparsity threshold was employed to ensure that only the most significant connections in the NC matrices were retained, minimizing the influence of spurious or weak correlations. Unlike fixed thresholds, the minimum sparsity threshold S_min was dynamically calculated for each subject, ensuring that the threshold was tailored to the specific properties of the data. The sparsity range was set from S_min + 0.5 with a step size of 0.02, allowing for a comprehensive exploration of the network across varying sparsity levels. The brain network parameters analyzed included clustering coefficient (CC), average shortest path length (ASPL), global efficiency (globE), and local efficiency (locE). The CC reflects the degree of local interconnectivity or “cliquishness” in the network, with higher CC values suggesting more efficient information transfer among neighboring brain regions⁷⁹. ASPL measures the harmonic mean of the shortest paths between all node pairs, with shorter ASPL values indicating more direct pathways for information transfer across distant brain regions⁸⁰. GlobE assesses the overall efficiency and capacity of information transmission across the entire network, reflecting global integration, while locE measures the efficiency of information transfer within localized clusters or sub-networks of the brain. Higher values of globE and locE indicate that the brain network can process information more rapidly and in parallel, leading to more efficient cognitive processing⁷⁷. Repeated measures ANOVAs were followed by post hoc pairwise comparisons using Bonferroni correction to explore differences in network parameters between the three states (State 1, State 2, and State 3) for each creative task.

Following the identification of NC states for each participant, various metrics were used to characterize these states^36,76. These metrics included the ratio of each state, which indicates the proportion of total windows attributed to each state; the number of transitions between states, which reflects the frequency of state changes; and the dwell time of each state, representing the average duration spent in each state, calculated from the starting point of each occurrence. Several weighted independent samples t-tests were performed to examine the difference in the abovementioned metrics between design and non-design groups. All p-values were corrected using the FDR method.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Author contributions

J.T., X.Q., X.W., and H.N. designed the research. J.T., and X.Q. performed the research. J.T., X.Q., and K.L., T.L., X.W., and Z.G. analyzed the data. J.T., X.Q., and N.H. interpreted the data. J.T. and X.Q. wrote the paper. N.H., X.Q., and T.Y. critically reviewed the manuscript. All authors edited and approved the manuscript.

Data availability

Due to the requirements and regulations of the University Committee on Human Research Protection of East China Normal University, data related to this paper will be made available upon request to the corresponding author with a formal data-sharing agreement. There are no limits on data sharing.

Code availability

Custom-written code is available upon request by contacting the corresponding author.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at 10.1038/s41539-025-00388-1.