Abstract/Details

The Obscure Features Hypothesis for innovation: One key to improving performance in insight problems


2012 2012

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Abstract (summary)

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.

Indexing (details)


Subject
Cognitive psychology
Classification
0633: Cognitive psychology
Identifier / keyword
Psychology; Creativity; Functional fixedness; Innovation; Insight problems
Title
The Obscure Features Hypothesis for innovation: One key to improving performance in insight problems
Author
McCaffrey, Anthony J.
Number of pages
180
Publication year
2012
Degree date
2012
School code
0118
Source
DAI-B 73/12(E), Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781267496669
Advisor
Cohen, Andrew
Committee member
Baker, Lynne; Clifton, Charles; Daehler, Marvin; Krishnamurty, Sundar
University/institution
University of Massachusetts Amherst
Department
Psychology
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3518388
ProQuest document ID
1034335917
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/1034335917
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