Scoring Divergent Thinking Tests by Computer With a Semantics-Based Algorithm

Divergent thinking (DT) tests are useful for the assessment of creative potentials. This article reports the semantics-based algorithmic (SBA) method for assessing DT. This algorithm is fully automated: Examinees receive DT questions on a computer or mobile device and their ideas are immediately compared with norms and semantic networks. This investigation compared the scores generated by the SBA method with the traditional methods of scoring DT (i.e., fluency, originality, and flexibility). Data were collected from 250 examinees using the “Many Uses Test” of DT. The most important finding involved the flexibility scores from both scoring methods. This was critical because semantic networks are based on conceptual structures, and thus a high SBA score should be highly correlated with the traditional flexibility score from DT tests. Results confirmed this correlation (r = .74). This supports the use of algorithmic scoring of DT. The nearly-immediate computation time required by SBA method may make it the method of choice, especially when it comes to moderate- and large-scale DT assessment investigations. Correlations between SBA scores and GPA were insignificant, providing evidence of the discriminant and construct validity of SBA scores. Limitations of the present study and directions for future research are offered

Reanalysis of Genetic Data and Rethinking Dopamine\u27s Relationship With Creativity

Several genetic analyses of creativity have recently been reported. A key finding is that dopamine might be related to ideational fluency (Runco, Noble, Reiter-Palmon, Acar, Ritchie, & Yurkovich, 2011) or even to creativity per se (Reuter, Roth, Holve, & Hennig, 2006). Previous analyses have ignored an important part of genetic theory, however, namely the likelihood of polygenetic contributions. Many human characteristics are polygenetic

Problem Construction and Creativity: The Role of Ability, Cue Consistency, and Active Processing

Problem construction has been suggested as the first step in creative problem solving, but our understanding of the underlying process is limited. According to a model of problem construction (Mumford, Reiter-Palmon, & Redmond, 1994), problem construction ability, active engagement in problem construction, and the presence of diverse and inconsistent cues influence creative problem solving. To test these hypotheses, 195 undergraduates were asked to solve 6 real-life problems and complete a measure of problem construction ability. Active engagement in problem construction was manipulated by instructions to the participants. Cue consistency was manipulated by the information presented in the problem situation. The quality, originality, and creativity of the solutions were evaluated. Results indicated that problem construction ability was related to higher quality solutions as well as solutions rated as more original. Problem construction ability also interacted with cue consistency such that individuals with high problem construction ability produced solutions of higher quality and originality when faced with inconsistent cues. The implication of these findings to our understanding of creative problem solving and the problem construction process are discussed

The Genetic Basis of Creativity and Ideational Fluency The Genetic Basis of Creativity and Ideational Fluency

Reuter, Roth, Holve, & Hennig (2006) described what they called the first candidate gene for creativity. This study replicated and extended their work for a more careful analysis of five candidate genes: Dopamine Transporter (DAT), Catechol-O-Methyltransferase (COMT), Dopamine Receptor D4 (DRD4), D2 Dopamine Receptor (DRD2), and Tryptophane Hydroxylase (TPH1). Participants were 147 college students who received a battery of tests of creative potential. Multivariate analyses of variance indicated that ideational fluency scores were significantly associated with several genes (DAT, COMT, DRD4, and TPH1). This was apparent in both verbal and figural fluency ideation scores, before and after controlling general intelligence. Yet fluency, alone, is not an adequate measure of creativity, and the index that is by far the most important part of creativity (i.e., originality) had a negligible relationship with the genes under investigation. Hence, in contrast to earlier research, the conclusion offered here is that there is a clear genetic basis for ideational fluency, but that fluency, alone, is not sufficient to predict or guarantee creative performance. Hence, at present, the genetic basis of creativity remains uncertain

Entrepreneurship and Creative Professions – A Micro-Level Analysis

It has widely been recognized that creativity plays an immense role not only for arts, sciences, and technology, but also for entrepreneurship, innovation, and thus, economic growth. We analyze the level and the determinants of self-employment in creative professions at the level of individuals. The analysis is based on the representative micro data of the German Socio-Economic Panel (SOEP). The findings suggest that people in creative professions appear more likely to be self-employed and that a high regional share of people in the creative class increases an individual's likelihood of being an entrepreneur. Investigating the determinants of entrepreneurship within the creative class as compared to non-creative professions reveals only some few differences

Positive Creativity and the Intentions, Discretion, Problem Finding, and Divergent Thinking That Support It Can Be Encouraged in the Classroom

This article begins by presenting a definition of positive creativity. This definition is based in part on the standard view of creativity, which points to originality and effectiveness. A brief discussion of the distinction between benevolent creativity and malevolent creativity indicates that intentions should also be required of positive creativity. Intentions may seem like difficult things to monitor in the classroom, but several useful methods are described herein. The suggestions that are offered here to support positive creativity involve divergent thinking and decision making. The most novel claim in this article is that positive creativity may involve not just problem solving but also problem finding. A second important claim is that educators must be prepared to take the good with the bad. More specifically, when creativity is encouraged, students are likely to think in truly divergent directions, which means they may offer negative as well as positive ideas. Educators should be prepared for ideas that they themselves do not understand. Practical suggestions are offered, including the recommendation that educators should encourage careful decision-making about what constitutes a worthwhile problem (as well as how to solve such problems in a creative fashion). Quite a few instances of malevolence take the form of pseudo-problems. These must be recognized as such and attention must be directed instead to the significant problems that do plague society, such as the climate crisis, the protection of voting rights, and racial discrimination. Positive creativity is needed now more than ever before

AI can only produce artificial creativity

This article (a) draws from various theories of creativity (e.g., 4P and 6P theories) and (b) uses several concepts from the creativity literature (e.g., self-actualization, emergence) to evaluate the claim that AI can be creative. This approach suggests that, at most, the output of AI represents products which, although lacking, may be attributed with creativity. Such attributions are often mistaken, and, significantly, products say little about the underlying process. Indeed, criticisms previously leveled at the view that the social recognition of products is required of creativity also apply to AI output. Several examples of products and overt actions that have been mistakenly attributed with creativity are discussed. The most telling of these is the ostensible emergence by a machine. The conclusion is that it makes no sense to refer to “creative AI.” One alternative is to extend the concept of “artificial intelligence” to creativity, which gives us “artificial creativity” as the label for what computers can do. Artificial creativity may be original and effective but it lacks several things that characterize human creativity. Thus it may be the most accurate to recognize that the output of AI as a kind of pseudo-creativity

An Empirical Test of the Inter-Relationships between Various Bibliometric Creative Scholarship Indicators

Quantifying the creative quality of scholarly work is a difficult challenge, and, unsurprisingly, empirical research in this area is scarce. This investigation builds on the theoretical distinction between impact (e.g., citation counts) and creative quality (e.g., originality) and extends recent work on using objective measures to assess the originality of scientific publications. Following extensive evidence from creativity research and theoretical deliberations, we operationalized multiple indicators of openness and idea density for bibliometric research. Results showed that in two large bibliometric datasets (creativity research: N = 1643; bibliometrics dataset: N = 2986) correlations between impact and the various indicators for openness, idea density, and originality were negligible to small; this finding supports the discriminant validity of the new creative scholarship indicators. The convergent validity of these indicators was not as clear, but correlations were comparable to previous research on bibliometric originality. Next, we explored the nomological net of various operationalizations of openness and idea density by means of exploratory graph analysis. The openness indicators of variety (based on cited journals and cited first authors) were found to be made up of strongly connected nodes in a separate cluster; the idea density indicators (those based on abstracts or titles of scientific work) also formed a separate cluster. Based on these findings, we discuss the problems arising from the potential methodological overlap among indicators and we offer future directions for bibliometric explorations of the creative quality of scientific publications

General and Domain-Specific Contributions to Creative Ideation and Creative Performance

The general objective of this study was to reexamine two views of creativity, one positing that there is a general creative capacity or talent and the other that creativity is domain-specific. These two views were compared by (a) testing correlations among measures of domain-general and domain-specific creativity and (b) examining how the general and the specific measures was each related to indices of knowledge, motivation, and personality. Participants were 147 college students enrolled in a foreign language course. Data were collected on participants’ domain knowledge, motivation, and creative personality, as well as four measures representing “General or Domain-Specific Creative Ideation” or “Creative Performance and Activity”. Results indicated that the four measures of creativity were correlated with one another, except for “General Performance and Activity” and “Domain-Specific Ideation.” A canonical correlation indicated that knowledge, motivation, and personality were significantly correlated with the four creativity measures (Rc = .49, p < .01). Multiple regressions uncovered particular relationships consistent with the view that creativity has both general and domain-specific contributions. Limitations, such as the focus on one domain, and future directions are discussed