| The study of creativity has been dogged by controversy since its inception,and one of the most controversial is whether creativity is really a general skill.In fact,from the very beginning of creativity studies,scholars generally assumed that creativity was domain generic,and this view was also reflected in the tendency of early measures of creativity to be generally domain neutral.However,since the 1980 s,the support for domain specificity has become louder,resulting in a "domain general" versus "domain specific" view of creativity,and domain-specific measures of creativity have begun to be explored.As theoretical and empirical studies have emerged to support both sides,so has the convergence view,which holds that creativity has both domain-specific and domain-general factors.Different from the views of scholars who support the domain generality of creativity,the view of domain specificity holds that creativity problem solving in daily life is often accompanied by rich creative background,requiring creators to combine their own knowledge reserve(such as previous learning experience and life experience)to create.However,due to the limitation of the laboratory context and the general support of the creative field,the traditional paradigm often does not include the use of such rich experience,which is lack of ecological validity.Therefore,it is suggested to use the creativity measurement paradigm of the knowledge-rich context in the research.To test the domain of creativity,experiments 1 and 2 are designed to test whether experience and specialty make a difference in creativity,and how this effect is manifested in knowledge-rich and knowledge-poor contexts.Among them,for the test of creativity,we selected the scientific innovation problem(SIP)in the knowledge-rich context and the alternate uses task(AUT)in the knowledge-poor context.In addition,there are different opinions about the relationship between knowledge and creativity.There is the ground-based view that knowledge promotes creativity,and there is the tension view that knowledge will damage creativity when it exceeds a certain limit.Although many studies have conducted empirical studies on this subject,these studies usually measure subjects’ knowledge in more indirect ways,such as using individual experiences,artistic achievements,test scores,or extracting information from interviews,journals,and even self-evaluations.All of these methods can reveal an individual’s knowledge mastery to some extent,and establish preliminary conclusions about the relationship between knowledge and creativity,but they do not go far enough.Zhang Qinglin et al.proposed that the quantity and quality of knowledge jointly affect the quality of creativity,but there is still a lack of direct evidence.In order to solve this problem,in experiment 3,we adopt a clustering approach to construct semantic networks and ordered trees,and extract average degree,ave.weighted degree,network diameter,graph density,modularity,average clustering coefficient and average path length,tree theory indicators,including possible recall orders,tree height and number of excluded rows,are collectively referred to as graph theory indicators,so as to provide quantitative methods to represent complex knowledge systems,so as to explore the relationship between knowledge structure characteristics and creativity.In experiment 1 and experiment 2,three groups of subjects were recruited:biological science subjects,non-biological science subjects,and non-biological arts subjects.The AUT was selected as the creative task in the knowledge-poor context,and20 biological SIPs were selected from the Material Database of Scientific Invention Problems compiled by Zhu Dan as the creative task in the knowledge-rich context in the domain of biology.To explore the relationship between experience and expertise and creative problem solving in knowledge-rich and knowledge-poor contexts,a experience-rich questionnaire was used to collect experience-rich information.The results of experiment 1 show that the difference of different experiences can affect the creative performance of the subjects.Specifically,the flexibility of the object multipurpose test was related to the number and quality of practice,whether the subject filled in the open question,originality was related to the length of the experience rich hobby,and the performance of the scientific invention question was mainly related to the professional learning,and also related to the total score of the experience rich we collected.The results of experiment 1 suggest that professional learning is related to the performance of scientific invention problem solving in the biological domain in the context of knowledge enrichment,which seems to imply domain-specific characteristics of creativity.Therefore,we further analyze the performance of professional sum and creativity in experiment 2.The results of experiment 2 showed that the major could significantly affect the creativity performance of subjects in the knowledge-rich context,but could not affect the creativity performance of subjects in the knowledge-poor context.Hypothesis two is tested.Experiment 3 combines the two-stage paradigm of "learning-testing" and the experimental operation of ordered tree technology to form a three-stage experimental process of "learning-recalling-testing".The subjects need to learn the prototype first,and then conduct 24 free recall of the 20 prototypes for 3 consecutive days,8 times a day,and finally take the creativity test.The results of free recall were used to establish graph theory indicators(including network indicators and tree indicators),and the relationship between graph theory indicators and two kinds of creativity was used to test the hypothesis of experiment 3.In experiment 3,it is found that specialty and memory strategies can promote subjects to form different mental structures for the same set of specialty concepts,and different aspects of this structure affect their creativity in the knowledge-poor and knowledge-rich contexts respectively.Specifically,the three subdimensions of AUT are significantly correlated with the number of rows excluded.The fluency of AUT is significantly positively correlated with the average clustering coefficient,the flexibility is significantly positively correlated with the average clustering coefficient,and the flexibility is significantly negatively correlated with the network diameter and average path length.The results of the scientific invention problem(SIP)are negatively correlated with the PRO value in the tree index(i.e.the most possible recall order),negatively correlated with the average degree and graph density in the network index,and positively correlated with the network diameter and modularity.It is proved that subjects with tighter knowledge structure,more complete semantic network node neighborhood and less error-prone recall perform better on AUT,while those with looser,more complex and hierarchical knowledge structure and more error-prone recall perform better on SIP task.From the results of the three experiments,we can see that for the same group of subjects,the effect of using experience and specialty to predict creative performance is not as good as that of using constructed graph theory indicators.Compared with experience and specialty,the characteristics of knowledge structure have the most obvious and clear influence on creativity,which supports the view of integration in the domain debate of creativity.In addition,the correlation directions of AUT originality and SIP scores for almost every graph theory index(eight out of nine)are opposite,and the significance is basically different.This result implies that semantically rich and semantically poor creativity problem solving depends on different knowledge structure characteristics. |
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