Dual-process models of cognition suggest that there are two types of thought: autonomous Type 1 processes and working memory dependent Type 2 processes that support hypothetical thinking. Models of creative thinking also distinguish between two sets of thinking processes: those involved in the generation of ideas and those involved with their refinement, evaluation, and/or selection. Here we review dual-process models in both these literatures and delineate the similarities and differences. Both generative creative processing and evaluative creative processing involve elements that (...) have been attributed to each of the dual processes of cognition. We explore the notion that creative thinking may rest upon the nature of a shifting process between Type 1 and Type 2 dual processes. We suggest that a synthesis of the evidence bases on dual-process models of cognition and of creative thinking, together with developing time-based approaches to explore the shifting process, could better inform the development of i.. (shrink)

This exploratory study aims at integrating the psychometric approach to studying creativity with an eye-tracking methodology and thinking-aloud protocols to potentially untangle the nuances of the creative process. Wearing eye-tracking glasses, one hundred adults solved a drawing creativity test – The Test of Creative Thinking-Drawing Production (TCT-DP) – and provided spontaneous comments during this process. Indices of visual activity collected during the eye-tracking phase explained a substantial amount of variance in psychometric scores obtained in the test. More importantly, however, clear (...) signs of methodological synergy were observed when all three sources (psychometrics, eye-tracking, and coded thinking-aloud statements) were integrated. The findings illustrate benefits of using a blended methodology for a more insightful analysis of creative processes, including creative learning and creative problem-solving. (shrink)

According to Mednick’s (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to each (...) other by their association correlations - overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick’s (1962) theory and the feasibility of using network science paradigms to investigate high level cognition. (shrink)