A new cognitive theory of innovation, the Obscure Features Hypothesis (OFH), states that many innovative solutions result from two steps: (1) noticing a rarely noticed or never-before noticed (i.e., obscure) feature of the problem's elements, and (2) then building a solution based on that obscure feature. The OFH deepens the analysis of the previous theories of innovation and opens up a systematic research program of uncovering aspects of the human semantic, perceptual, and motor systems that inhibit the noticing of obscure features and the derivation of counteracting techniques to unearth obscure features that have a high probability of being useful in problem solving. Specifically, in this study we derive a technique called the Generic Parts Technique (GPT) designed to unearth the types of obscure physical features that can counteract functional fixedness (Duncker, 1945) in insight problems involving concrete objects. Subjects trained in the GPT solved on average 33% more problems more than a control group, which has a very large standardized effect size, a Cohen's d of 1.6. Further, in a subsequent feature-listing task with concrete objects, the GPT subjects listed more obscure physical features. These results support the OFH in that obscure features seem to be one key to solving concrete object insight problems and techniques such as the GPT that are designed to unearth obscure features improve performance on these types of problems.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-6654 |
| Date | 01 January 2012 |
| Creators | McCaffrey, Anthony J |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
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