Abstract:Analysis of over 1,000 innovative inventions reveals that during the innovative process at least one rarely-noticed or new (i.e., obscure) feature is unearthed and built upon to create the solution (i.e., the Obscure Features Hypothesis for innovation: OFH) [6,7]. Embedding the insights from this analysis into the structure of semantic networks creates AhaNets, which help optimize the search for the needed key obscure feature. Techniques to overcome cognitive aversions to noticing the obscure (i.e., fixation e… Show more
“…Fourth, the GPT was shown to be a powerful technique in a potential tool kit of innovation-enhancing techniques, in which each technique can be used to overcome a specific cognitive obstacle to problem solving by helping subjects notice the specific obscure feature necessary for a solution. Another such technique, discussed in McCaffrey and Spector (2011), helps subjects successfully overcome design fixation (Jansson & Smith, 1991), which is the tendency people have to fixate on known solutions when trying to create more innovative solutions.…”
A recent analysis of real-world problems that led to historic inventions and insight problems that are used in psychology experiments suggests that during innovative problem solving, individuals discover at least one infrequently noticed or new (i.e., obscure) feature of the problem that can be used to reach a solution. This observation suggests that research uncovering aspects of the human semantic, perceptual, and motor systems that inhibit the noticing of obscure features would enable researchers to identify effective techniques to overcome those obstacles. As a critical step in this research program, this study showed that the generic-parts technique can help people unearth the types of obscure features that can be used to overcome functional fixedness, which is a classic inhibitor to problem solving. Subjects trained on this technique solved on average 67% more problems than a control group did. By devising techniques that facilitate the noticing of obscure features in order to overcome impediments to problem solving (e.g., design fixation), researchers can systematically create a tool kit of innovation-enhancing techniques.
“…Fourth, the GPT was shown to be a powerful technique in a potential tool kit of innovation-enhancing techniques, in which each technique can be used to overcome a specific cognitive obstacle to problem solving by helping subjects notice the specific obscure feature necessary for a solution. Another such technique, discussed in McCaffrey and Spector (2011), helps subjects successfully overcome design fixation (Jansson & Smith, 1991), which is the tendency people have to fixate on known solutions when trying to create more innovative solutions.…”
A recent analysis of real-world problems that led to historic inventions and insight problems that are used in psychology experiments suggests that during innovative problem solving, individuals discover at least one infrequently noticed or new (i.e., obscure) feature of the problem that can be used to reach a solution. This observation suggests that research uncovering aspects of the human semantic, perceptual, and motor systems that inhibit the noticing of obscure features would enable researchers to identify effective techniques to overcome those obstacles. As a critical step in this research program, this study showed that the generic-parts technique can help people unearth the types of obscure features that can be used to overcome functional fixedness, which is a classic inhibitor to problem solving. Subjects trained on this technique solved on average 67% more problems than a control group did. By devising techniques that facilitate the noticing of obscure features in order to overcome impediments to problem solving (e.g., design fixation), researchers can systematically create a tool kit of innovation-enhancing techniques.
“…If not, then the machine intersects the set of all the hyponyms of the goal verb fasten with the set of verbs related to the uses of all the objects and their parts. If the string's representation contains the effect tie things (i.e., in the form verb nounPhrase ), then, as shown in Figure 6, the machine is able to make a suggestion that the candle's wick might be able to tie the rings together (McCaffrey & Spector, 2011a, 2011b). Fig.…”
Section: Example Involving Human Obstacles To Innovationmentioning
confidence: 99%
“…If the machine representation of candle contained the feature that a wick is a string and the representation of string contained the feature that strings tie things, then the machine can easily make the suggestion that the wick can be used to tie the rings together (McCaffrey & Spector, 2011a, 2011b).…”
Section: Example Involving Human Obstacles To Innovationmentioning
confidence: 99%
“…In sum, the two rings problem shows how the machine can help humans overcome narrow verb associations with hyponym look-ups (McCaffrey & Spector, 2012) and functional fixedness with the GPT (McCaffrey, 2012). In turn, humans can potentially help the machine fill in some of the incompleteness in its representations (McCaffrey & Spector, 2011a, 2011b).…”
Section: Example Involving Human Obstacles To Innovationmentioning
If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a humancomputer collaboration in which each partner assists the other in unearthing the obscure features of a problem.
A new visual representation for insight problems permits 22 new quantitative measures; which leads to a detailed diagnosis of a person's (or team's) creative weaknesses; which then leads to prescribing targeted, effective counter‐techniques for each weakness. Currently, only two measures are consistently used for insight problem solving: the number of problems solved and the time to solve the problems. These coarse measurements do not reveal the intricate dynamics of solving insight problems. Furthermore, four commonly used creativity measures (i.e., fluency, originality, flexibility, and elaboration) are often not applied to insight problems. This new visualization permits the easy application of all four creativity measures. I challenge creativity researchers to help determine which of the 22 proposed quantitative measures are the most diagnostic for insight problem solving in isolation and, in a weighted linear combination, which might yield an effective quotient (i.e., overall measure) of insight problem solving ability.
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