Decreasing the effect of verbal noise in analyzing cognitive activity of a design process

Mina Tahsiri, Jonathan Hale, Chantelle Niblock

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

In studying cognitive activity in design it is common practice to use designers' verbalizations during a design process to elicit the reasoning behind design actions. These verbalizations are segmented in order to enable a quantifiable analysis of the cognitive processes. Researchers have shown how Shannon's entropy can be applied to coded verbal data to provide a measure of creativity of those processes. We applied this method to a pilot study, investigating the effects of different design tools on creativity in the context of architectural design. Participants had to design three tasks of isomorphic nature, each with a different tool, in one design session. As shown a significant number of verbal comments were repetitions of already established ideas. Such comments brought nothing new to the sequence of activities but affected the value of information carried within that process which biased the measure of creativity. The paper regards these utterance as verbal noise. It proposes the use of corpus linguistic tools together with a coding scheme that can depict the hierarchical relationship of cognitive patterns used in the process to eliminate verbal noise from analysis. The method was applied to one participant's data, which shows a promising step in increasing the veracity of using verbal data in analyzing cognitive activity.
Original languageEnglish
Title of host publicationProceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition
Place of PublicationNew York
Pages169-172
Number of pages4
DOIs
Publication statusPublished - 22 Jun 2015

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    Tahsiri, M., Hale, J., & Niblock, C. (2015). Decreasing the effect of verbal noise in analyzing cognitive activity of a design process. In Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition (pp. 169-172). New York. https://doi.org/10.1145/2757226.2764545