Reframing Convergent and Divergent Thought for the 21st Century

Convergent thought is defined and measured in terms of the ability to perform on tasks where there is a single correct solution, and divergent thought is defined and measured in terms of the ability to generate multiple different solutions. However, this characterization of them presents inconsistencies, and despite that they are promoted as key constructs of creativity, they do not capture the capacity to reiteratively modify an idea in light of new perspectives arising out of an overarching conceptual framework. Research on formal models of concepts and their interactions suggests that different creative outputs may be projections of the same underlying idea at different phases of this kind of 'honing' process. This leads us to redefine convergent thought as thought in which the relevant concepts are considered from conventional contexts, and divergent thought as thought in which they are considered from unconventional contexts. Implications for the assessment of creativity are discussed.Comment: 7 pages; 2 figures

Computational scientific discovery in psychology

Scientific discovery is a driving force for progress, involving creative problem-solving processes to further our understanding of the world. Historically, the process of scientific discovery has been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyse data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories, and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This review addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically

Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work

Generative artificial intelligence (GenAI) is rapidly becoming a viable tool to enhance productivity and act as a catalyst for innovation across various sectors. Its ability to perform tasks that have traditionally required human judgment and creativity is transforming knowledge and creative work. Yet it also raises concerns and implications that could reshape the very landscape of knowledge and creative work. In this editorial, we undertake an in-depth examination of both the opportunities and challenges presented by GenAI for future IS research

Innovative Entrepreneurship through Creative Outputs for Emerging Filmmakers in South Africa: A Conceptual Framework

Creativity and innovation are fundamental traits of a creative leader in the film industry. This article explores the South African film landscape and the innovative opportunities for creative film entrepreneurs. It further explores and describes emerging film creative leadership in terms of an Entrepreneurial Conceptual Framework (creative output 2) that includes a functional relationship equation (creative output 1). The conceptual framework explores the relationship between the four key dimensions (soft skills, innovation, film project management, and iterative content generation) and the four key competencies (high-quality content, influence, education, and entertainment) and their ultimate effect on entrepreneurial creative leadership in the local filmmaking landscape. The functional relationship equation identifies four key characteristics (vision, collaboration, adaptability, and emotional intelligence) that distinguish a creative leader in the local film industry from other forms of leadership. Both the conceptual framework and the functional relationship equation set innovative and structural foundations and form creative outputs for emerging filmmakers, local independent film productions, and future academic studies

The neural and cognitive mechanisms underlying creative thinking

The ability to generate creative ideas and novel solutions is a defining feature of human cognition. However, the cognitive and neural mechanisms that underlie creative cognition are poorly understood. While recent research has highlighted the roles of distinct associative and controlled processes in creative cognition, supported by the default mode and executive control networks, respectively, it remains unclear how exactly creative ideas are produced by the interactions of these processes and networks, or how creative cognition relates to more fundamental processes like executive functions and working memory (WM). The present thesis aims to examine the neurocognitive basis of creative thinking using a combination of behavioral and fMRI experiments. The need for greater computational modeling in neurocognitive creativity research (NCR) is also discussed. The first study examines how the default mode and executive control networks contribute to creative cognition over time. Results are broadly suggestive of distinct generative and evaluative phases in creative thought. A second study explores relationships between multiple forms of creative thinking and multiple forms of inhibition, finding that divergent thinking is related to cognitive inhibition. In a third study, relationships between creative cognition and control over WM are examined, using measures of executive functions. While no relationships were found between divergent thinking and executive functions, a positive relationship was found between WM updating and convergent thinking and verbal fluency. In a review chapter, the case for greater computational modeling in NCR is made. Previous models of creative cognition, and how these might be improved upon, are discussed, with some examples of the model development process. In a final study, relationships are explored between personality measures and evaluations of the novelty, usefulness, and creativity of ideas. A closing chapter summarizes all findings and discusses avenues for future research