Electronic brainstorming systems have been developed and utilized as a means to understand and to improve brainstorming sessions. However, little attention has been paid to the relatedness between individual ideas in those systems. Here, we investigated how the mechanisms of idea suggestion, manipulated using idea relatedness, impacts the success of brainstorming. For our first experiment, we created an algorithm that ranked ideas in real-time using participants' self-reports of relatedness between ideas. Newly sampled sets of ideas were repeatedly presented back to participants as stimuli. Unfortunately, results showed that our experimental manipulation had no impact on brainstorming. We therefore designed a second experiment with numerous improvements, including replacement of participants' self-reports by an automatic semantic similarity calculation algorithm. We found that, in the new results, experimental variables had a quantifiable impact on brainstorming, detected by multiple statistical measures. Our brainstorming system has potentials for applications in scientific, industrial and creative use.
|Advisers||Hiroki Sayama; Shelley Dionne|
|School||STATE UNIVERSITY OF NEW YORK AT BINGHAMTON|
|Subjects||Experimental psychology; Organizational behavior; System science|
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