How to Guard Against Fixation? Demonstrating Individual Vulnerability is More Effective Than Warning of General Risk
Open accessAbstract
Creative behavior can be inhibited by fixation and so reducing fixation is a focus of much creativity research. One of the most common methods of tackling fixation is to warn people of fixation risks and instruct them to avoid constrained problem framing and solution search. However, such treatments are often ineffective. One possible explanation for this is that people typically believe that they (as individuals) are less vulnerable to a specified risk than other people are (in general). If we really want to motivate people to guard against a risk, we need to demonstrate that they, as individuals, are vulnerable to those risks. To study the effect of demonstrating individual vulnerability to fixation, we conducted an online experimental study using number and word tasks that both included a fixation "trap." The first task was used to provide a "demonstrated vulnerability" treatment (revealing participants' own fixated behavior) to the experimental group. This group outperformed those who received a comparable "asserted vulnerability" treatment (a warning about general fixation effects) and also those in a control group. Researchers and practitioners developing creativity training and tools aimed at reducing fixation effects should consider the benefits of demonstrating individual vulnerability to fixation rather than, or in combination with, issuing warnings that people in general are vulnerable to fixation.
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Uncovering participants' fixation tendencies ('demonstrated vulnerability') is an effective approach that can strengthen participants' ability to overcome fixation.
“Further, Neroni and Crilly [46] state that uncovering participants' fixation tendencies, which they call”
From The Effects of Generative AI on Design Fixation and Divergent Thinking (2024) · cites this paper as [46]
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The Effects of Generative AI on Design Fixation and Divergent Thinking
Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q., & Velloso, E. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642919 · Cited by 187 · see citing works
“Finally, our study only evaluated the potential of generative AI tools for ideation support through the specific example of image generators in a visual ideation task.”
- Authors
- Neroni, M. A., & Crilly, N.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Crilly, Nathan
- Venue
- The Journal of Creative Behavior
- Publisher
- Wiley
- Published
- 2020-07-23
- Volume
- 55
- Issue
- 2
- Pages
- 447-463
- ISSN
- 0022-0175 (ISSN-L)
- 2162-6057
- OpenAlex
- W3044682446
- Cited by
- 8
- References
- 83
- Citations by year
- 2024: 3 · 2023: 2 · 2022: 2 · 2021: 1
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Creative Behavior (publishedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Funders
- UK Engineering and Physical Sciences Research Council (EP/K008196/1)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
- Keywords
- fixation, creativity, individual susceptibility, risk warning, problem framing, solution search, creativity training
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