Can Rewards Enhance Creativity? Exploring the Effects of Real and Hypothetical Rewards on Creative Problem Solving and Neural Mechanisms

doi:10.21203/rs.3.rs-4610324/v1

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Can Rewards Enhance Creativity? Exploring the Effects of Real and Hypothetical Rewards on Creative Problem Solving and Neural Mechanisms

https://doi.org/10.21203/rs.3.rs-4610324/v1

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Reward cues have long been considered to enhance creative performance; however, little is known about whether rewards can affect creative problem solving by manipulating states of flexibility and persistence. This study sought to elucidate the differential impacts of real versus hypothetical rewards on the creative process utilizing the Chinese compound remote association task. Behavioral analysis revealed a significantly enhanced solution rate and response times in scenarios involving real rewards, in contrast to those observed with hypothetical rewards. Furthermore, participants exhibited a greater ability to solve CRA items under low rewards than under high rewards. Electrophysiological findings indicated that hypothetical rewards led to more positive P200-600 amplitudes, in stark contrast to the amplitudes observed in the context of real rewards. These findings indicate a positive impact of real rewards on creative remote associations and contribute new insights into the relationship between rewards and creative problem solving, highlighting the crucial role of the level of control in the formation of creativity.

creative problem solving

P200-600

insight

metacontrol state

reward

Creativity, a profound and intricate phenomenon of the human mind (Zhang et al., 2020), serves as the wellspring of human civilization by fostering the creation of knowledge and artifacts that are integral to human culture. Investigating the neural mechanisms underlying creativity thus offers insights into the very essence of our humanity (Dietrich, 2019). Consequently, enhancing creative performance emerges as a critical objective within creativity research. Historically, creativity has been predominantly assessed through divergent thinking tasks, which involve generating multiple novel solutions to open-ended problems (Benedek & Fink, 2019). However, divergent thinking is not entirely synonymous with creativity (Runco, 2008), and creative problem solving—also referred to as insight problem solving—constitutes a vital component of creativity. Thus, the present study focused on how to improve creative problem solving.

Humans inherently exhibit reward-seeking behavior (Mohr et al., 2010), and prior research has demonstrated that monetary incentives can enhance behavioral performance (Pessoa, 2010). In traditional cognitive studies, rewards have been shown to facilitate learning and influence attention and other cognitive control processes (D’Ardenne et al., 2008; Krebs et al., 2012). Nevertheless, the impact of rewards on creativity remains a highly contentious issue within creativity research (Friedman, 2009). Cognitive-oriented researchers argue that external rewards diminish intrinsic motivation and are detrimental to creativity (Amabile & Pillemer, 2012). Conversely, behaviorally oriented researchers have presented evidence suggesting that rewards can enhance creativity when they are explicitly tied to creative performance (Eisenberger & Rhoades, 2001; Eisenberger & Shanock, 2003).

In creativity research, numerous studies have identified two distinct cognitive pathways that contribute to the emergence of creativity (Akbari Chermahini & Hommel, 2012; Cui et al., 2023; De Dreu et al., 2008; Sagiv et al., 2010; Toyama et al., 2023; Zhang et al., 2020). The first pathway, flexible thinking, involves exploring a broad range of categories and perspectives, whereas the second pathway, persistent thinking, entails a focused and effortful examination of a limited number of cognitive categories and perspectives. Both pathways have been shown to generate creative outcomes, a concept encapsulated in the dual pathway to creativity model (Baas et al., 2013). Flexible thinking facilitates access to distant information links and enables the discovery of novel connections between categories and concepts. In contrast, persistent thinking supports systematic, effortful, and incremental search processes (De Dreu et al., 2012; Dreu et al., 2011; Nijstad et al., 2010; Toyama et al., 2023). Previous research has suggested that elevated dopamine levels are associated with reduced inhibition of alternative thoughts and increased cognitive flexibility. Consequently, this raises the following question: can rewards influence creative problem solving by affecting cognitive flexibility?

This study seeks to examine whether varying the type of reward can influence cognitive flexibility and thereby enhance creative problem solving. Unlike real rewards, which provide tangible benefits, hypothetical rewards lack such concrete advantages. It is therefore hypothesized that real rewards will enhance cognitive control to a greater extent, promoting increased persistence relative to hypothetical rewards. Additionally, participants generally exert more mental effort on a cognitive control task when they are offered greater rewards for performing well (Frömer et al., 2021). Consequently, this study investigated the effects of different rewards on creative problem-solving outcomes.

To gain a more comprehensive understanding of the impact of rewards on creative problem solving, it is essential to incorporate neurophysiological measures or neuroimaging techniques (Cristofori et al., 2018). Behavioral measures alone are insufficient to provide conclusive evidence regarding the cognitive control and attentional processes involved in creative problem solving. This study addresses this issue by employing electrophysiological techniques with high temporal resolution. Creative problem solving, as evaluated by the compound remote association (CRA) test, can be approached through both noninsight (i.e., analytic) and insight solutions (Cui et al., 2021, 2023; Kizilirmak et al., 2016). Previous research has indicated that spontaneous insight is associated with the P200–600 component (Cui et al., 2023; Qiu et al., 2008). Therefore, this study focused on examining the effects of rewards on these electrophysiological indices.

In summary, the objective of this study was to explore the differential effects of real and hypothetical reward cues on creative problem solving over time. To achieve this goal, we employed the Chinese CRA task within a spontaneous insight paradigm wherein participants independently discovered solutions.

Participants

We conducted total sample size estimation by G*Power to determine the number of samples sufficient to detect a reliable effect. According to partial eta square values of previous reward-CPS studies (Cui et al., 2021; Cristofori et al., 2018), we calculate the effect size f are 0.47 and 0.52. Consequently, we adopted an effect size of f = 0.4, as suggested by Cohen (2013), 20 participants were needed to detect a significant effect (α = 0.05, power (1-β) = 0.9, ANOVA: repeated measures, 2 × 2 within factors, G-Power 3.1.9.2) (Faul et al., 2009).

Twenty-five participants aged 18 to 23 years (M = 20.74, SD =1.51; women: 22) participated in this study. All participants had normal or corrected-to-normal vision, were unaware of the study's aims, and were right-handed native Chinese speakers with no reported neurological disorders. The study received approval from the local ethics committee. Upon completion, participants were thoroughly informed about the study's objectives and procedures. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Participants received 50￥as payment for their participation, with additional monetary rewards paid (up to 70￥) depending on their performance.

Design and Procedure

The stimuli were presented on a CRT monitor with a resolution of 1920×1080 and a refresh rate of 85 Hz. The experiment was programmed using E-Prime 3.0 (Psychology Software Tools, Inc., Pittsburgh, PA, USA) for both stimulus presentation and response recording. The study employed a within-participants design with a 2 (reward type: real or hypothetical) × 2 (reward level: high or low) factorial structure. Participants were provided with detailed instructions regarding the reward conditions, the Chinese CRA task, and the concept of insight. They then completed five practice trials to familiarize themselves with the task.

As illustrated in Figure 1, each trial began with a central fixation cross displayed for 0.5 seconds, followed by the presentation of a reward value (1￥/0.1￥, presented in half of the trials) for 1 second. Participants were subsequently required to solve Chinese CRA problems (Cui et al., 2021; Du et al., 2017). Each problem in the Chinese CRA task consisted of three stimulus words (e.g., “xing/liu/li”, 行, 流, 里) presented together. Participants had to generate a solution word (e.g., “cheng”, 程) that could combine with each of the three given words to form a familiar two-word phrase (i.e., “xing cheng”, 行程, “liu cheng”, 流程, “li cheng”, 里程) within a 10-second time limit. They then indicated whether their solution was reached through insight or analysis (Jung-Beeman et al., 2004; Salvi et al., 2015) within 3 seconds. Successful problem solving was followed by reward feedback. In the hypothetical reward condition, participants were informed that the gains were virtual and were instructed to imagine them as real money, striving to maximize their virtual profits. Conversely, in the real reward condition, participants were informed that the rewards earned during the experiment were tangible, with the assurance that their compensation would be directly proportional to the actual monetary gains they accumulated throughout the study. The experiment consisted of two blocks: real and hypothetical rewards. Within each block, all items were pseudorandomly assigned to two reward levels. The entire experiment comprised 240 trials. All trials were presented randomly to the participants, and after the participants completed 60 trials, they could rest. The procedures of the experiment are shown in Fig. 1.

EEG recording and analysis

EEG data were collected during the Chinese CD task using the Neuroscan Synamps2 EEG recording and analysis system. EEGs were recorded using 64 Ag/AgCl electrodes in an elastic cap using the International Standard 10–20 system. Vertical and horizontal EEGs were recorded during data acquisition using the Neuroscan electrode cap and with its own reference electrode as the online reference electrode. EEG data were sampled at 1000 Hz/channel, electrode impedances were kept lower than 10 kΩ, and the recording bandwidth ranged from 0.05 to 100 Hz.

Off-line analyses were performed in MATLAB using the EEGLAB toolbox (Delorme & Makeig, 2004) and ERPLAB toolbox (Lopez-Calderon & Luck, 2014). The EEG signals were referenced to the average of bilateral mastoid electrodes and filtered using IIR-Butterworth filters with half-power cutoffs at 0.1 Hz (roll-off = 12 dB/oct) with a high-pass filter and at 30 Hz (roll-off = 12 dB/oct) with a lowpass filter (Luck, 2014). Independent component analysis (ICA) was performed to correct the components associated with eve movement and eye-blink artifacts. Then, the artifact correction process was supplemented with artifact rejection to eliminate the trials with clearly artifactual voltage deflections. Specifically, trials were excluded if the peak-to-peak voltage within the EEG epoch was greater than 300 μV in any 200 ms window in any channel (Bacigalupo & Luck, 2018). Four participants were excluded for whom > 35% of trials were rejected because of EEG/EOG artifacts; therefore, 21 participants were included in the ERP/EEG analysis. EEG data were segmented into epochs. The problem-solving phase was segmented into epochs using a time window of 1400 ms, ranging from 200 ms before the stimulus to 1200 ms after the stimulus. For each subject, epochs belonging to the same reward condition were averaged, yielding four average waveforms time locked to the stimulus onset. Single-subject average waveforms of each reward condition were averaged across subjects to obtain group-level waveforms. Based on previous creative EEG studies (Cui et al., 2023; Qiu et al., 2008) and grand average waveforms, we focused on the P200-600 component and late positive component (LPC). To increase statistical strength and reduce false effects (Luck & Gaspelin, 2017), the F3, Fz, and F4 electrodes were collapsed by averaging their values as an indication of frontal activity; the FC3, FCz, FC4, C3, Cz, C4, CP3, CPz, CP4, P3, Pz, and P4 electrodes were collapsed by averaging their values as an indication of frontocentral, central, centroparietal and parietal activity, respectively. Three-factor repeated measures ANOVAs with 2 (reward: real, hypothetical) × 2 (level: high, low) × 5 (region: frontal, frontocentral, central, centroparietal, parietal) factors were used.

For behavioral measures and ERP components, we utilized JASP 16.1 software (Wagenmakers et al., 2018). In all analyses, we employed the Greenhouse–Geisser method to correct the p values of the F tests for deviations. The effects of ANOVAs were measured using partial eta squared, referred to as η_p². For effect sizes in paired t tests, we employed Cohen's d, which calculates the mean difference score as the numerator and the pooled standard deviation from both repeated measures as the denominator (Cohen, 2013). To address multiple comparisons, we applied the Holm correction (Holm, 1979) in the present research.

Behavioral Performance

The participants correctly solved 47.4% (SD = 6.6%) of the problems. When considering real reward conditions, 50.8% (SD = 10.1%) of the problems were solved under high reward conditions, while 51.8% (SD = 8.8%) were solved under low reward conditions. The average response times were 4.54 s (SD = 0.67 s) for the high-reward condition and 4.36 s (SD = 0.67 s) for the low-reward condition. In the hypothetical conditions, 41.6% (SD = 7.7%) of the problems were solved under the high reward condition, and 45.4% (SD = 8.4%) were solved under the low reward condition. The average response times were 4.78 s (SD = 0.69 s) for the high-reward group and 4.71 s (SD = 0.54 s) for the low-reward group (Table 1).

Table 1

*Means and SDs of the solution rate, average response times and insight rate*
	Real Reward		Hypothetical Reward
Measure	High	Low	High	Low
Solution Rate	0.51 ± 0.10	0.52 ± 0.09	0.42 ± 0.08	0.45 ± 0.08
RT(s)	4.54 ± 0.67	4.36 ± 0.67	4.78 ± 0.69	4.71 ± 0.54
Insight Rate	0.51 ± 0.29	0.49 ± 0.28	0.45 ± 0.32	0.47 ± 0.34

The solution rate, response times, and insight rate were analyzed using a repeated-measures ANOVA with a 2 (reward: real vs. hypothetical) × 2 (level: high vs. low) design. As shown in Fig. 2, we observed a significant main effect for the reward condition, F (1, 20) = 20.71, p < 0.001, η_p² = 0.51. Post hoc comparisons indicated that participants solved more CRA items under real rewards, t (20) = 4.55, p < 0.001, Cohen’s d = 0.89. Furthermore, participants exhibited a greater ability to solve CRA items under low rewards than under high rewards, t (20) = 2.04, p = 0.055, Cohen’s d = 0.28, although the effect of level did not reach significance, F (1, 20) = 4.16, p = 0.055, η_p² = 0.17. No significant interaction effect was observed.

For average response times, the ANOVA showed a significant main effect for the reward condition, F (1, 20) = 8.93, p = 0.007, η_p² = 0.31, but no significant main effect was observed for the reward level and interaction effect, F (1, 20) = 2.13, p = 0.16, η_p² = 0.10; F (1, 20) = 0.44, p = 0.51, η_p² = 0.02. Post hoc comparisons indicated that participants responded significantly faster to real rewards than to hypothetical rewards (Fig. 2), t (20) = -2.99, p = 0.007, Cohen’s d = 0.45. For the insight rate, no significant main effect or interaction effect was observed, F (1, 20) = 0.91, p = 0.35, η_p² = 0.04; F (1, 20) = 0.04, p = 0.85, η_p² = 0.002; F (1, 20) = 1.19, p = 0.29, η_p² = 0.06.

ERP analysis

N1 (120–180 ms)

To examine whether there were differences in N1 (120–180 ms) between different reward conditions for CRA processing, a 2 (reward: real vs. hypothetical) × 2 (level: high vs. low) × 5 (region: frontal vs. frontocentral vs. central vs. centroparietal vs. parietal) repeated ANOVA was conducted on the N1 amplitude. There was no significant main effect of reward, F (1, 20) = 2.38, p = 0.14, η_p² = 0.11. The main effects of level were also not significant, F (1,20) = 0.4, p = 0.53, η_p² = 0.02, while the effect of region was significant, F (4, 80) = 7.98, p = 0.0005, η_p² = 0.29. No other significant interaction effect was observed (Table 2). Post hoc comparisons revealed more negative waveforms in the central-parietal and parietal regions than in the frontal region (t (80) = 4.24, p < 0.001; Cohen’s d = 0.70; t (80) = 5.13, p < 0.001; Cohen’s d = 0.84) (Fig. 3).

Table 2

*Mean amplitudes of different reward conditions in 120–180 ms time windows*
	Real Reward		Hypothetical Reward
Measure	High	Low	High	Low
Frontal	1.74 ± 3.11	1.97 ± 2.89	1.40 ± 3.77	1.85 ± 2.60
Fronto-central	0.34 ± 3.12	1.02 ± 2.44	0.76 ± 3.22	1.21 ± 2.16
Central	-0.20 ± 2.32	0.70 ± 1.87	0.73 ± 2.83	0.92 ± 2.01
Centro-parietal	-1.03 ± 2.85	0.49 ± 2.36	0.22 ± 2.72	0.61 ± 2.32
Parietal	-0.75 ± 2.79	0.08 ± 2.96	0.08 ± 2.96	-0.56 ± 3.05

P200-600

To examine whether different rewards induce different P200-600 amplitudes in Chinese CRA processing, an ANOVA of 2 (reward: real vs. hypothetical) × 2 (level: high vs. low) × 5 (region: frontal vs. frontocentral vs. central vs. centroparietal vs. parietal) repeated measures was conducted on P200-600 amplitudes. According to the grand average map (Fig. 3), two bins (220 to 260 ms, 400 to 500 ms) were measured in the CRA solution. The mean amplitudes in the time windows from 220 to 260 ms were measured, and there was no significant main effect of reward, F (1, 20) = 0.55, p = 0.47, η_p² = 0.03, or level, F (1, 20) = 0.41, p = 0.53, η_p² = 0.02. There was a main effect of region, F (4, 80) = 9.31, p = 0.002, η_p² = 0.32. A post hoc test indicated that in the fronto-central brain region, the waveform was significantly greater than that in the centro-parietal and parietal regions, t (80) = 3.33, p = 0.009, Cohen’s d = 0.36; t (80) = 3.70, p < 0.001, Cohen’s d = 0.60. In the central region, the waveform was significantly greater than that in the parietal region, t (80) = 4.44, p < 0.001, Cohen’s d = 0.48. As shown in Fig. 3, the mean amplitudes in the time windows from 400 to 500 ms were measured (Table 3). The results showed that there was a significant main effect of reward, F (1, 20) = 9.66, p = 0.006, η_p² = 0.33. The main effect of reward level was not significant, F (1, 20) = 0.46, p = 0.50, η_p² = 0.02. The main effect of region was marginally significant, F (4, 80) = 3.69, p = 0.058, η_p² = 0.16.

No other interaction effect was observed. Post hoc comparisons showed that hypothetical rewards induced greater amplitude than real rewards, t (20) = 3.11 p = 0.006, Cohen’s d = 0.35. The amplitudes in the parietal region were greater than those in the frontal region, t (80) = 2.99 p = 0.037, Cohen’s d = 0.38.

Table 3

*Mean amplitudes of different reward conditions in 400–500 ms time windows*
	Real Reward		Hypothetical Reward
Measure	High	Low	High	Low
Frontal	-0.95 ± 2.55	-1.70 ± 2.88	-0.67 ± 3.28	-0.55 ± 3.04
Fronto-central	-1.11 ± 2.36	-1.72 ± 2.20	-0.58 ± 2.64	-0.53 ± 2.25
Central	-0.90 ± 2.07	-1.32 ± 1.71	-0.27 ± 2.22	-0.28 ± 1.84
Centro-parietal	-0.55 ± 1.76	-0.86 ± 1.39	0.05 ± 2.02	0.14 ± 1.69
Parietal	-0.40 ± 1.81	-0.58 ± 1.53	0.12 ± 2.08	0.31 ± 1.70

In this study, we utilized a Chinese CRA task to examine the neurobiological and behavioral mechanisms underlying the impact of real and hypothetical rewards on creative problem solving. To the best of our knowledge, this is the first electrophysiological investigation aimed at elucidating the neural mechanisms involved in the effects of real and hypothetical rewards on CRA problem solving. We obtained two principal findings from this study. Behaviorally, participants solved a greater number of Chinese CRA problems when motivated by real monetary rewards than when motivated by hypothetical monetary rewards. Additionally, the behavioral results indicated that participants solved Chinese CRA problems more quickly in the real reward condition than in the hypothetical reward condition (Fig. 2). Neurophysiologically, hypothetical rewards elicited a more positive P200-600 amplitude than real rewards during the problem-solving phase. Specifically, within the 400–500 ms time interval, hypothetical rewards generated a more positive waveform than real rewards.

Behavioral findings

Overall, the findings of this study demonstrated that real reward cues, in comparison to hypothetical reward cues, significantly facilitated the solution of CRA problems. According to the metacontrol state model, the solution to the CRA problem may benefit from cognitive persistence (Hommel, 2015; Zhang et al., 2020). Anderson and colleagues indicated that the important characteristic of CRA problems is that it takes a long time to retrieve a solution if one is retrieved at all. This produces a sustained demand on the retrieval module, while the subgoal module remains unchanged (Anderson et al., 2009). Anderson's research suggested that cognitive persistence in solving CRA problems may sustain continuous activity within the retrieval module, thereby improving problem-solving performance.

Our findings indicated that participants answered significantly more items at the low-reward level than at the high-reward level. The reason for this result may be that moderate levels of reward are more conducive to fostering creativity. Numerous studies have suggested that moderate levels of prefrontal and striatal dopamine can facilitate cognitive persistence and flexibility, respectively—implying an inverted U-shaped relationship between dopamine levels and performance (Akbari Chermahini & Hommel, 2012; Boot et al., 2017; Chermahini & Hommel, 2010). This function provides a plausible explanation for the observed advantage of the low reward level in our study.

Temporal mechanisms of the effect of rewards on creative problem solving.

In this study, the ERP technique was employed to elucidate the electrophysiological differences between the types of reward cues. The scalp ERP data revealed that real reward cues and hypothetical reward cues elicited distinct ERP components during the problem-solving phases.

As illustrated in Fig. 3, both real and hypothetical rewards induced a P200–600 component in the spontaneous insight paradigm (Cui et al., 2023; Qiu et al., 2008). However, hypothetical rewards elicited a significantly more positive P200–600 component than real rewards. The potential reasons for these effects are as follows: (1) The P200-600 component might represent a pronounced P300 component (Cui et al., 2023; Qiu et al., 2008), where the amplitude of the P300 reflects the amount of attentional resources allocated to a given task (Donchin & Coles, 1988). Similar to previous studies, hypothetical rewards, akin to subliminal rewards (Cui et al., 2023), may enhance cognitive flexibility more effectively than real rewards, which are more likely to trigger inhibition processes of potentially interfering stimuli or thoughts. This could explain why hypothetical rewards elicited a more positive P200–600 component than real rewards. According to MSM theory, solving CRA problems may benefit from cognitive persistence (Zhang et al., 2020). The important characteristic of the RAT is that it takes a long time to retrieve a solution, if one is retrieved at all. This produces a sustained demand on the retrieval module, while the subgoal module remains unchanged (Anderson et al., 2009). These findings suggest that persistence bias may benefit RAT questions. The RAT provides increasingly tight top-down constraints, and there is only one possible answer per item, suggesting that the task calls for a control state with a strong impact on the goal—a bias toward persistence (Hommel, 2015). Therefore, despite the enhanced cognitive flexibility associated with hypothetical rewards, real rewards still led to a greater number of solutions due to their promotion of cognitive persistence. (2) Previous studies have suggested that the P200-600 component may reflect the formation of novel and rich associations, with higher P200-600 amplitudes observed in spontaneous insight solutions than in noninsight solutions (Qiu et al., 2008). Furthermore, research by Cui et al. (2023) revealed that subliminal low rewards induced a greater number of insight solutions. MSM theory posits that insight solutions benefit from cognitive flexibility (Zhang et al., 2020). However, in the present study, no significant difference was found between the insight rates of real rewards and hypothetical rewards. The cause may be a bias in self-reports, although self-reports differentiating between insight and analytic solutions are reliable, and behavioral and neuroimaging markers have consistently provided evidence (Cristofori et al., 2018; Jung-Beeman et al., 2004; Kounios et al., 2008). However, the choice of insight can be biased, as participants tend to misremember their analytic solutions as insights when they are informed that the problems they have solved are highly uncommon (Du et al., 2017). According to the theory of event coding, event files allow the selection of actions according to the effects they are likely to produce (Hommel, 2004; Hommel et al., 2001). Selecting a response can be considered a dynamic process of uncertainty reduction; it involves the intentional weighting of feature dimensions that are expected to be relevant for the task or that are suggested by the context (Hommel & Wiers, 2017). We infer that the higher weighting given to real rewards may influence the choice of insight judgments, thereby facilitating a higher insight rate for real rewards. This could explain why no significant difference was observed between the insight rates of real rewards and hypothetical rewards.

Limitations and Directions for Future Studies

There are several limitations to this study. First, creativity is generally considered an active process involving both generation and evaluation. The CRA task primarily reflects a generation phase, combining remote association (Bowden & Jung-Beeman, 2003) based on the search for semantic memory and autobiographical memory (Madore et al., 2019; Milivojevic et al., 2015; Ren et al., 2020). Future experiments should focus on the evaluation process of creativity construction. Additionally, a larger sample size would enhance the robustness and generalizability of the findings; thus, it is recommended that future studies include larger EEG samples. Furthermore, this study solely examined the distinct impact of varying reward levels without incorporating a control condition devoid of rewards. Future research should aim to address this gap by including a control condition to provide a more comprehensive understanding of the effects of rewards on creative problem solving.

In conclusion, this study offers both behavioral and neural evidence addressing the contentious effects of rewards on creative cognition. Our findings indicate a positive impact of real rewards on creative remote associations. Notably, compared with real rewards, hypothetical rewards elicited more P200-600 components. These results contribute new insights into the relationship between rewards and creative problem solving, highlighting the crucial role of the level of control in the formation of creativity.

Acknowledgements

This research was supported by Xuzhou Medical University initiated funding project (RC20552306), the Education Scientific Planning Project of Jiangsu Province(C/2023/01/74), the National Natural Science Foundation of China (32271095) and the Natural Science Foundation of Jilin Province (20230101149JC).

Funding

Xuzhou Medical University initiated funding project (RC20552306), the Education Scientific Planning Project of Jiangsu Province (C/2023/01/74), the National Natural Science Foundation of China (32271095) and the Natural Science Foundation of Jilin Province (20230101149JC).

Availability of data and materials

The data and code used in the study are available upon direct request and can be shared or re-used with permission from the authors and a formal data sharing agreement. The dataset, along with the relevant code, will be made publicly accessible via the Open Science Framework (OSF) following the publication of our paper.

Ethics approval and consent to participate

This research was approved by the Ethics Committee of the School of Psychology, Northeast Normal University. Before starting the experimental task, participants received information about the purpose of the study, the task, and its duration and gave their written informed consent.

Competing interests

The authors declare that they have no competing interests.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the author(s) used chatgpt 3.5 to improve the language and readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

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Can Rewards Enhance Creativity? Exploring the Effects of Real and Hypothetical Rewards on Creative Problem Solving and Neural Mechanisms

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