This body of work is composed of three individual papers that each seek to explain individual differences in creative cognition. Paper 1 used structural equation modeling to examine the ways in which creative problem solving and creative idea generation relate or differ. Results from Paper 1 demonstrate that divergent thinking and creative problem solving are best represented as a bifactor model, bearing distinct domain-specific factors, as well as a shared domain-general factor. Though working memory and fluency of memory retrieval explained significant portions of the domain-specific constructs, they only explained ~2% of variance in the domain-general factor. Paper 2 explores the idea that domain-general creativity can be attributed to the structure of one’s knowledge. Semantic networks were developed and compared across high and low general creativity, divergent thinking, creative problem solving, and working memory. Results indicated that, in general, a looser network structure is more amenable for flexible thought processes across multiple classifications of creative ability. Paper 3 explores the idea that domain-general creativity can be attributed to one’s ability to overcome salient, prepotent responses. More specifically, Paper 3 argues that the presence of ambiguities enhances the likelihood that someone will develop a faulty mental representation that requires restructuring in order to reach the desired solution or response. Results demonstrate that overcoming ambiguities in language comprehension draws on similar processes as creative problem solving: ambiguous language comprehension predicted creative problem solving above and beyond that of working memory, fluid intelligence, or normal sentence comprehension. Importantly, this relationship was unique to creative problem solving, as the effect disappeared when predicting analytic problem solving. Together, these studies suggest that the ability to overcome ambiguities and the organization of one’s semantic knowledge are both critical components underlying creativity. More generally, this work has highlighted the ways in which domain-specific and domain-general processes are unique or shared across different measures of creativity, and researchers should be aware of these relationships as they work to advance the creativity literature.

In the field of musical expression, the complex relationship between improvisation and the cognitive processes that underlie creativity presents a fascinating yet challenging puzzle, prompting this thesis to explore the connection between musical improvisation and creative cognition among musicians. Focusing on the development of robust methods for feature extraction and representation, it utilizes unsupervised Machine Learning (ML) techniques to project improvisations from a prime melody into a high-level latent space. The methodology involves iterative analysis employing Variational Autoencoder (VAE) models, initially pre-trained with a larger dataset and fine-tuned with a musical improvisation dataset provided by the Max Plank Institute. Evaluation encompasses Evidence Lower Bound (ELBO) loss metric and dimensionality reduction techniques like Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Multidimensional Scaling (MDS), and Uniform Manifold Approximation and Projection (UMAP) to explore latent space representations. The results reveal that experienced musicians exhibit a greater divergence from the prime melody compared to amateurs. Moreover, professionals’ samples demonstrate more refined clustering and nuanced adjustments between improvisations projected in the latent space. / Inom musikaliska uttryck är det komplexa förhållandet mellan improvisation och de kognitiva processer som ligger till grund för kreativitet ett fascinerande men utmanande pussel, vilket föranleder denna avhandling att utforska sambandet mellan musikalisk improvisation och kreativ kognition bland musiker. Avhandlingen fokuserar på utvecklingen av robusta metoder för extraktion och representation av funktioner och använder oövervakade maskininlärningstekniker (ML) för att projicera improvisationer från en huvudmelodi till ett latent utrymme på hög nivå. Metoden innebär iterativ analys med hjälp av VAE-modeller (Variational Autoencoder), som ursprungligen förutbildades med ett större dataset och finjusterades med ett dataset för musikalisk improvisation från Max Plank Institute. Utvärderingen omfattar förlustmåttet Evidence Lower Bound (ELBO) och dimensionalitetsreducerande tekniker som Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Multidimensional Scaling (MDS) och Uniform Manifold Approximation and Projection (UMAP) för att utforska latenta rymdrepresentationer. Resultaten visar att erfarna musiker uppvisar en större avvikelse från huvudmelodin jämfört med amatörer. Dessutom visar professionella musiker mer raffinerade kluster och nyanserade justeringar mellan improvisationer som projiceras i den latenta rymden.

Design fixation is a phenomenon experienced by professional designers and engineering design students that stifles creativity and innovation through discouraging ideational productivity, fluency and diversity. During the design idea and concept generation phase of the design process, a reliance on perceptual surface feature similarities between design artefacts increases the likelihood of design fixation leading to design duplication. Psychologists, educators and designers have become increasingly interested in creative idea generation processes that encourage innovation and entrepreneurial outcomes. However, there is a notable lack of collaborative research between psychology, education and engineering design particularly on inductive reasoning of undergraduate engineering students in higher education. The data gathered and analysed for this study provides an insight into property inference decision-making preferences and decision switching (SWITCH) patterns of engineering undergraduates under similarity-based inductive judgements [SIM] and category-based inductive judgements [CAT]. For this psychology experiment, property induction tasks were devised using abstract shapes in a triad configuration. Participants (N = 180), on an undergraduate engineering programme in London, observed a triad of shapes with a target shape more similar-looking to one of two given shapes. Factors manipulated for this experiment included category alignment, category group, property type and target shape. Despite the cognitive development and maturation stage of undergraduate engineers (adults) in higher education, this study identified similarity-based inductive judgements [SIM] to play a significant role during inductive reasoning relative to the strength of category-based inductive judgements [CAT]. In addition to revealing the property inference decision-making preferences of a sample of undergraduate engineers (N = 180), two types of switch classification and two types of non-switch classification (SWITCH) were found and named SIM_NCC, SIM-Salient, Reverse_CAT and CAT_Switching. These different classifications for property inference switching and non-switching presented a more complex pattern of decision-making driven by the relative strength between similarity-based inductive judgements [SIM] and category-based inductive judgements [CAT]. The conditions that encouraged CAT_Switching is of particular interest to design because it corresponds to inference decision switching that affirms the sharing of properties between dissimilar-looking shapes designated as category members, i.e., in a conflicting category alignment condition (CoC). For CAT_Switching, this study found a significant interaction between a particular set of conditions that significantly increased the likelihood of property inference decisions switching to affirm the sharing of properties between dissimilar-looking shapes. Stimuli conditions that combined a conflicting category alignment condition (where dissimilar-looking shapes belong to the same category) with category specificity, a causal property and a target shape with merged (or blended) perceptual surface features significantly increased the likelihood of a property inference decision switching. CAT_Switching has important implications for greater ideational productivity, fluency and diversity to discourage design fixation within the conceptual design space. CAT_Switching conditions could encourage more creative design transformations with alternative design functions through inductive inferences that generalise between dissimilar artefact designs. The findings from this study led to proposing a Cartesian view of the concept design space to represent the possibilities for greater movements through flexible and expanding category boundaries to encourage conceptual combinations, greater ideational fluency and greater ideational diversity within a configuration design space. This study has also created a platform for further research into property inference decision-making, ideational diversity and category boundary flexibility under stimuli conditions that encourage designers and design students to make inductive generalisations between dissimilar domains of knowledge through a greater emphasis on causal relations and semantic networks.