When attempting to solve a problem, people often fall back on prior experiences that worked, sometimes without considering other solutions.
In other words, they stay in their comfort zone, which psychologists call ‘fixation.’
Researchers at the University of Michigan, University of Limerick and Iowa State University investigated what happens when new engineers attempt to design a solution on their own with no examples: They tend to stick to their original idea and not try other options.
“Scientists assumed that people who don’t see a provided example are free to pursue a wide variety of solutions; however, we wondered whether these people may also become fixated on their own first idea, limiting their creativity in the same way as with a provided example,” said Colleen Seifert, U-M professor of psychology and faculty associate at the Research Center for Group Dynamics at the Institute for Social Research.
The study involved engineering education, but the findings are applicable across areas of creative problem solving, the researchers say.
About 120 college students participated in experiments to create a nonspill coffee cup or a car-mounted bicycle rack – half saw an example solution and half were not given an example, but generated their own initial design.
Researchers analyzed both groups’ concepts for similarity to the first solution they saw – either the example provided or their own initial concept.
Surprisingly, students who did not see a provided example showed greater fixation to their own first ideas. Those who were given an initial example solution showed some fixation, but not as much.
Shanna Daly, U-M associate professor of mechanical engineering, said students working without a provided example created more design concepts; however, they were also more similar to their own initial concept.
Perhaps the introduction of an example design from an outside source – not one an individual created – motivates a search for new ideas, she said.
To consider whether fixation on initial examples might be mitigated, both groups of students continued to create more concepts in a second (30-minute) phase using creativity strategies, called “Design Heuristics,” to inspire more varied ideas. With these prompts pushing the engineers in new directions, both groups experienced less fixation during this phase and more willingness to consider other ideas.
When designers become aware of their fixations, it may improve how they solve future problems.
“These findings suggest learning to recognize your own fixation may be an important metacognitive skill in managing the search for creative outcomes in design,” said Keelin Leahy, a lecturer at the University of Limerick and the study’s lead author.
The study – which was also co-authored by Seda McKilligan, professor of industrial design at Iowa State University – appears in the October issue of Journal of Mechanical Design.
Much of the engineering design research can be divided into two related but distinct categories. There are projects that focus on the tools designers use in an effort to make the design process easier, more efficient, more innovative, or generally more effective. Alternatively, there is research that aims to understand designers themselves.
In doing so, researchers attempt to map out how designers solve problems and develop strategies to improve these processes. The work presented here falls in the latter category. Specifically, this work examines a phenomenon that has been consistently observed and studied in behavioral research, but much less so in neuroscientific work: design fixation.
Design fixation has been defined as the “blind adherence to a set of ideas or concepts limiting the output of conceptual design” (Jansson & Smith 1991) and is an effect that many designers face during the design process. Since neuroimaging has been proven to be a useful method for gaining greater insight into the cognitive processes associated with specific behaviors, this research employed neuroimaging to investigate design fixation in the context of solving engineering design problems.
Design fixation often occurs in the conceptual design phase, during which an individual is engaging their creativity to generate ideas to solve a given design problem. As such, this work is informed by the prior work in the neuroscience of creativity. Studying creativity using functional magnetic resonance imaging (fMRI) comes with its own set of unique challenges, as outlined by Abraham (2012, 2013) and Benedek, Christensen & Beaty (2019).
Behavioral study of creativity involves the generation of ideas in open-ended settings. The time limitation of being inside the scanner, in addition to the physical constraint of not being able to move while in the scanner in order to generate usable data, significantly challenges the study of creativity using fMRI. In addition, larger sample sizes are harder to achieve in fMRI research in comparison to behavioral research because the data collection is much more time-consuming and physically involved.
Creative cognition is seen, by some, as distinct from normative cognition because it calls for “more open-ended, unstructured or non-linear information processing strategies to be adopted” (Abraham 2013). Much of the prior work on creativity in neuroscience has focused on individual differences in creative ability, and how these manifest in the brain (Abraham 2019).
Abraham discusses how the premise of “creative ability” is challenging to the research findings because it is unclear if this ability is the result of other brain-related differences, and because it relies on a presumption that creative ability is partially an innate attribute.
On the contrary, Fink et al. found evidence of increased creative ability as the result of training in divergent thinking, with increased activity patterns in the left inferior parietal cortex and the left middle temporal gyrus (Fink et al. 2015). Others have studied individual or small groups of neurological patients, such as those with Parkinson’s or epilepsy, and how their condition affects their creative abilities.
The study presented in this paper is distinct from the prior studies in that it recognizes that participants may have differing levels of creative ability and that the impact of inducing design fixation will not necessarily be modulated by innate/learned individual differences in creative ability.
This study is exploratory in nature, with the goal of ascertaining whether design fixation can be detected in brain activation; it is not yet known which regions of the brain may be associated with design fixation.
In Section 2, prior research on design fixation in behavioral studies is presented, along with a review of the current understanding of the neuroscience of creativity. Subsequently, in Section 3, more specific hypotheses are presented based on the prior research. Section 4 briefly discusses the significance of our work.
Section 5 presents the methods of the study conducted here, and Sections 6–9 present the results, discussion, limitations, and conclusions, respectively, of this study.
Fixation has different meanings across the fields of psychology, neuroscience, and design cognition.
In psychology, Freud pioneered the term “fixation” as the “persistence of anachronistic sexual traits” (First 1970), which later evolved into a broader psychological definition of “object relationships with attachments to people or things persisting from childhood into adult life” (Akhtar 2018).
In neuroscience, fixation refers to “the maintaining of the visual gaze on a single location” (Krauzlis & Goffart 2017). In design cognition research, fixation refers to the “blind adherence to a set of ideas or concepts limiting the output of conceptual design” (Jansson & Smith 1991). In this study, the design cognition definition is employed.
Design fixation can have negative impacts on the design process. Consequently, this issue is relevant to design practitioners, as well as design educators (Purcell & Gero 1996).
For instance, the presence of an example solution (good or bad) may result in the transfer of attributes from the example to the new solution, thus making it more difficult to develop a novel solution (Chrysikou & Weisberg 2005). While this sort of fixation can be readily observed, there are several factors, such as the commonness of the example (Purcell et al. 1993) and the domain of the problem (Purcell & Gero 1996; Goldschmidt 2011), that affect whether or not fixation will be present.
Due to the potentially significant impact fixation can have on the output of the design process, developing more effective ways of mitigating and potentially preventing design fixation is an important area of research.
Researchers have investigated a variety of different factors that contribute to design fixation. Linsey et al. observed significant fixation manifesting in both students and experts when provided an example solution (Linsey, Viswanathan & Gadwal 2010).
Although novice and expert designers may experience comparable levels of fixation, the effectiveness of defixation strategies may differ based on the designer’s level of expertise (Viswanathan & Linsey 2013a ). In the work by Agogué et al., the authors demonstrate that fixation can differ from subject to subject based on age as well as education level (Agogué et al. 2014). Designing as a team versus as an individual is another factor that can affect the impact of design fixation (Fu, Cagan & Kotovsky 2010).
Youmans observed physical models having a positive impact on design fixation (Youmans 2011). Results from that study showed less fixation with the example solution when a physical prototype was used. Viswanathan and Linsey argue that physical models can supplement a designer’s mental model, leading to positive impacts on fixation and idea quality (Viswanathan & Linsey 2013b ).
Design-by-analogy is related to fixation because analogy requires examining, abstracting, and mapping concepts from an outside source to the design problem at hand. Exposure to outside sources, such as example solutions, can be beneficial for the purposes of design-by-analogy, or it can be detrimental by causing fixation.
The analogical distance of the example solutions from the design problem can also have an impact on design fixation. While there is evidence that example solutions that are conceptually far from the problem lead to less fixation (Purcell & Gero 1992), this is not always the case (Fu et al. 2013). Chan et al. showed that example solutions that are conceptually near can be more helpful for design-by-analogy than far ones (Chan, Dow & Schunn 2015).
Koh and De Lessio demonstrated that exposure to patent documents (a form of example solution/analogical stimuli) prior to problem solving in an effort to avoid infringement could still lead to fixation (Koh & De Lessio 2018). Moss et al. showed that helpful cues (examples/hints) are more effective when presented after an initial work period than at the start of problem solving (Moss, Kotovsky & Cagan 2011).
Further, a meta-analysis of 43 design studies presented by Sio et al. reinforced evidence of the significant effect that presentation and composition of examples can have on design fixation (Sio, Kotovsky & Cagan 2015). In order to induce fixation intentionally, this study employs near-field example solutions at the very beginning of problem solving.
As discussed, there are numerous factors that contribute to the level of fixation experienced during problem solving. While there have been advancements in addressing these factors so that fixation can be broken or avoided all together (Smith & Linsey 2011; Vasconcelos et al. 2017; Crilly 2018), such strategies would benefit from a greater understanding of the cognitive mechanisms that underlie this phenomenon.
The work presented here used fMRI to observe brain activity as designers solve problems. The ultimate goal is to gain insight into the areas of the brain that are active during fixation in order to develop better methods that counteract design fixation.
As design fixation is related to cognitive processing, it is of interest to study whether design fixation can be detected in brain activation, and it is not yet known which regions of the brain may be associated with design fixation. Based on previous research, the following three hypotheses were formulated:
Hypothesis 1. Fixated thought relies on distinct cognitive processes. Therefore, fixation, and its mitigation, can be detected in a person’s brain as they are working on a design problem. Particular regions of the brain will be identified as more active.
This is a general exploratory hypothesis, derived from the consistent evidence of design fixation found in behavioral studies in design cognition (Jansson & Smith 1991; Purcell & Gero 1996; Linsey et al. 2010).
Hypothesis 2. Design fixation is associated with the anterior and dorsal prefrontal cortex (which is involved in high-level cognition processes), the secondary visual cortex, and areas associated with language and memory.
This hypothesis is derived from studies that have shown the dorsolateral prefrontal cortex to be associated with executive processes, such as attention and working memory (Curtis & D’Esposito 2003). As one of the indicators of design fixation is feature transfer, it is plausible that information is passed through working memory during this process. Additionally, viewing example solution images may lead to activation in the secondary visual cortex, an area associated with visual processing (Arslan 2016).
Hypothesis 3. Motor and premotor areas of the brain (areas that are activate while people imagine movement) are active during the process of generating solutions for mechanical design problems.
It is expected that part of the ideation process will involve imagining the use of any potential solution. Hypothesis 3 is derived from previous work where mental object manipulation has led to activation patterns similar to those observed when subjects physically manipulate objects (Vingerhoets, De Lange & Vandemaele 2002).
Hypothesis 1 pertains to the detection and observation of fixation in a designer’s brain as they work on generating design solutions. This hypothesis was supported by the results of this study. Results from the survey show that participants had difficulty generating solutions that were distinct from the example solutions, providing evidence that fixation occurred on some level.
It is likely that the presence of the example, as well as the accompanying fixation, is a contributing factor to the unique pattern of activation that is observed when comparing the Example condition to the No Example condition.
While no activation was found in the prefrontal cortex as theorized in Hypothesis 2, activation was found in the occipital lobe, where the visual cortex is located. Activation in the middle occipital gyrus (MOG) is consistent with the participants expending mental effort to process the information in the image of the example solution.
As previously noted, nearly half of the participants reported difficulty coming up with solutions that were completely different from the examples. The activation in the precuneus region could have resulted from participants trying to recall prior experience with aspects of the design problems as well as attempts to contextualize the example solution.
Hypothesis 3 was also supported by the results of this study. The areas where activation was found are often associated with spatial processing and are very close to the region associated with goal-directed movement.
The parietal lobule, associated with the dorsal stream, and the IFG, associated with the ventral stream, contribute to image processing by facilitating the recognition of an object’s location and identity, respectively. Recent findings have caused some researchers to conclude that the parietal and occipital brain areas play a significant role in mental imagery (Fink et al. 2014).
This activation pattern may be the result of the subjects imagining their potential solutions. As a major part of the visual cortex, the MOG also plays an important role in the semantic processing of visual images (Vandenberghe et al. 1996). The activation observed in these areas during the example condition is consistent with the subjects expending effort to process the picture provided as an example solution.
Additionally, the MOG is linked to the extrastriate body region described by Astafiev et al. (Astafiev et al. 2004). This region, located in the lateral occipital cortex, is associated with perception of body movement and goal-directed movements. This pattern may be the result of the subjects imagining how they would interact with their solutions.
Our brains are able to predict and compensate for changes in the mechanical behavior of a system by altering our internal models. This strategy may be accompanied by increased activation in the MOG (Shadmehr & Holcomb 1997).
Qiu et al. used fMRI to observe activation during “Aha and No-aha” conditions as subjects solved visually based word puzzles. Among other areas, increased activation was observed in the precuneus and inferior occipital gyrus and linked to the “Aha” effects indicating that the inferior occipital gyrus may have a role in the re-arrangement of visual stimulus (Qiu et al. 2010).
Moreover, a variety of creative tasks have been associated with some increased activation in the MOG (Howard-Jones et al. 2005; Ellamil et al. 2012; Aziz-Zadeh, Liew & Dandekar 2013; Boccia et al. 2015; Chen et al. 2015). In a work by Chrysikou and Thompson-Schill, subjects were tasked with thinking up uncommon uses for familiar objects.
Solutions that were judged to be perceptually based (i.e., containing properties visible or available without prior knowledge of the object’s identity (e.g., tennis racket: to use as a snow shoe) or properties visible or available without prior knowledge of the object’s identity (e.g., chair: to use as firewood)) were accompanied by greater activation in the middle occipital cortex (Chrysikou & Thompson-Schill 2011). The MOG activation present in this study may indicate a focus on incorporating attributes from the example solutions.
When compared to the No Example condition, deactivation was observed in the LG, an area associated with processing letter images and visual memories. This pattern of activity suggests that the presence of the example image may have shifted the focus of the subjects’ mental effort from the design prompt to the example image and potentially limited their thinking.
Deactivation was also observed in the left and right SFG. These areas are close in proximity to the prefrontal cortex, which has been argued to be a major contributing area to creativity (Dietrich 2004) and divergent thinking (Beaty et al. 2017).
Based on the ROI analysis, a significant negative correlation between the average beta values in the SFG ROI and novelty scores was found. This indicates that, within this study, as novelty scores increase, activity in the SFG decreases across conditions and design problems.
A meta-analysis of 45 fMRI studies yielded insights into some of the areas commonly associated with musical, verbal, and visuospatial creative activities (Boccia et al. 2015). In this context, verbal activities included tasks such as finding uncommon uses for everyday objects.
The analysis found that verbal creative activities were more likely to be associated with activation in several areas, including the prefrontal cortex, the middle and superior temporal gyri, the MOG, the right inferior frontal gyrus, and the LG. The pattern of deactivation observed in this work may indicate a decrease in creative processes when an example is provided.
Design problem solving results did not indicate statistically significant differences in novelty or quality between the Example and No Example conditions. However, significant differences in feature transfer indicated that fixation was successfully induced, as has been consistently done in prior studies that use paper-based design ideation.
The results indicate statistically significant differences between the fixation conditions in the fMRI data, as well. With this information, we will be able to design subsequent studies with imaging techniques that target the areas of interest found here without restricting motion to such a high degree. One promising technology is functional near-infrared spectroscopy (fNIRS).
This technology, which uses infrared light to acquire BOLD signals in more natural environments, has already been used successfully in some investigations of design-related activities (Shealy, Hu & Gero 2018).
The design problem solved in experiments like this one can significantly impact the results in design data outcomes. The level of difficulty or commonness of, or subjects’ familiarity with, the problem are likely to impact the feature transfer, quality, and novelty of design solutions, as shown in prior work (Chan et al. 2011).
One way to mitigate this variability is to test multiple design problems within the same experimental setup to determine whether effects are robust to changes in the design problem. Generalizability of experimental results in design science is always limited by the context in which the data was collected; this is an ongoing challenge to human subject-based research in design.
A study by Kumar and Mocko used latent semantic analysis to compare the design problems used in design cognition research, finding high correlation with the goal of a problem, functional requirements, non-functional requirements, and reference to an existing product, and low correlation with information about the end user.
This indicates the importance of using similar or benchmarked design problems in experimental design to increase the ability to compare results across experiments (Kumar & Mocko 2016).
When reflecting upon how these results might influence design practice, it is important to remember that the literature indicates that designers are often not even aware of their own design fixation (Linsey et al. 2010). When choosing the process, tools, steps, and order of the design activities, the results from this study indicate that it is important for designers to consider that their mental effort may be diverted away from their desired task of ideation if fixation has occurred.
For example, if a designer is examining pre-existing solutions for benchmarking and market research analysis, exposing themselves to these “example solutions” prior to or during concept generation may cause them to experience fixation. Diminished focus or mental energy used on a task can lead to fatigue, lower productivity, and frustration. If fixation may have been induced, designers might proactively choose to use mitigation techniques to invigorate their ideation process and outcomes.
reference link : https://www.cambridge.org/core/journals/design-science/article/using-fmri-to-deepen-our-understanding-of-design-fixation/2DD81FEE8ED682F6DFF415BF2948EFA6/core-reader
More information: Keelin Leahy et al. Design Fixation From Initial Examples: Provided Versus Self-Generated Ideas, Journal of Mechanical Design (2020). DOI: 10.1115/1.4046446