Problem solving in design is frequently susceptible to fixation, restrictions and mistakes introduced in the design process due to previous practice, that often impede the generation of effective design solutions. Research has shown that the inclusion of examples in the problem’s instructions is associated with a tendency to conform to those examples during creative generation. Individual differences in learning tendencies during concept building might underlie one’s susceptibility to design fixation. In this exploratory study, we investigated the impact of learning preferences and domain differences in design fixation. We hypothesized that an exemplar-based approach to learning—reflected in brain activity patterns—would reinforce the impact of examples in design tasks, by increasing the salience of the example design features relative to the abstract relationships that unite them. In contrast, an abstraction-based approach to learning—reflected in different patterns of neural activity—may emphasize the abstract design rules governing the example designs, thus offering protection from design fixation to their features. Based on prior literature, we further hypothesized that differences in domain expertise between mechanical engineering and product design would mitigate these effects. Mechanical engineering or product design students participated in two experimental sessions. In the first session, they completed multiple learning and individual differences behavioral assessments; in the second session, they underwent a functional magnetic resonance imaging scan (fMRI) while completing learning and design tasks using a tablet compatible for the brain imaging environment. Participants’ thought processes were also captured through concurrent verbal protocols during the scan. We discuss preliminary findings that reflect neural correlates of individual differences in design fixation. We further highlight the importance for engineering education of using a multimethod approach regarding the quantification of design fixation, learning tendencies, and individual differences through various neurocognitive assessments.
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