Collaborative fixation: Effects of others' ideas on brainstorming

A system capable of suggesting multi-word phrases while someone is writing could supply ideas about content and phrasing and allow those ideas to be inserted efficiently. Meanwhile, statistical language modeling has provided various approaches to predicting phrases that users type. We introduce a simple extension to the familiar mobile keyboard suggestion interface that presents phrase suggestions that can be accepted by a repeated-tap gesture. In an extended composition task, we found that phrases were interpreted as suggestions that affected the content of what participants wrote more than conventional single-word suggestions, which were interpreted as predictions. We highlight a design challenge: how can a phrase suggestion system make valuable suggestions rather than just accurate predictions?

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“…But the phrases shown were the most likely, thus not novel or creative-and poor examples have been shown to hinder creativity [20,24,32].…”

Section: Discussionmentioning

confidence: 99%

On Suggesting Phrases vs. Predicting Words for Mobile Text Composition

Arnold

Gajos

Kalai

2016

Proceedings of the 29th Annual Symposium on User Interface Software and Technology

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“…Similar lines of thinking led to a number of learning strategies in educational practices, such as small group teaching and cooperative learning. Subsequently, a number of studies about brainstorming showed that it did not really work; sometimes, brainstorming led to opposite effects, such as idea fixation [136]. However, Seelig, in her popular book inGenius: A Crash Course on Creativity [137], insisted that brainstorming was effective, and she blamed the detractors for not "understanding how different brainstorming is from normal conversation".…”

Section: Discussionmentioning

confidence: 99%

Deciphering the Enigma of Human Creativity: Can a Digital Computer Think?

Hong¹

2013

J Comput Sci Syst Biol

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The objective of the present article is a) to explain humans' high creativity in non-mystic and unambiguous terms, b) to evaluate the performance of problem-solving computer programs and c) to make suggestions about future designs of heuristics. Unlike many previous attempts in the past century, we sought inspiration from two sources that had been neglected or excluded from considerations by experts: artificial intelligence and introspections of a number of highly creative individuals, who confessed that they had a penchant for visual thinking. Simonton's chance-configuration model was refurbished accordingly. It is now possible for the refurbished model to explain a number of outstanding puzzles that had eluded our predecessors: a) what intuition is, b) why creators had no idea about their source of inspiration even after the fact, c) a peculiar event happening at the discovery time, known as the "aha" phenomenon, d) a type of accidental discoveries known as serendipity. Moreover, the elusive concept of abduction advanced by philosopher Charles Peirce is actually visual thinking in disguise. Blessed with this new understanding, we could evaluate the performance of a number of problem-solving computer programs from a cognitive point of view. It turned out that the common thread that links human creativity and computerbased creative problem solving is heuristic searching. Recognizing that a digital computer must perform heuristic searching in a digital environment, which is not the most user-friendly environment to do so, we made suggestions about how to circumvent the restrictions without sacrificing the principles in future designs of heuristics.

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“…For example, while exposure to mundane examples may hinder creativity [19,23], individuals can come up with more diverse and/or creative ideas if they have access to diverse and high quality inspirational examples [6,31,36,44,45,46]. The net effect of increasing individual creativity is that the community can converge on novel, high quality solutions more quickly than if all participants simply saw their own ideas [4].…”

Section: Creativity Enhancing Interventionsmentioning

confidence: 99%

IdeaHound

Siangliulue

Chan

Dow

et al. 2016

Proceedings of the 29th Annual Symposium on User Interface Software and Technology

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Prior work on creativity support tools demonstrates how a computational semantic model of a solution space can enable interventions that substantially improve the number, quality and diversity of ideas. However, automated semantic modeling often falls short when people contribute short text snippets or sketches. Innovation platforms can employ humans to provide semantic judgments to construct a semantic model, but this relies on external workers completing a large number of tedious micro tasks. This requirement threatens both accuracy (external workers may lack expertise and context to make accurate semantic judgments) and scalability (external workers are costly). In this paper, we introduce IDEAHOUND, an ideation system that seamlessly integrates the task of defining semantic relationships among ideas into the primary task of idea generation. The system combines implicit human actions with machine learning to create a computational semantic model of the emerging solution space. The integrated nature of these judgments allows IDEAHOUND to leverage the expertise and efforts of participants who are already motivated to contribute to idea generation, overcoming the issues of scalability inherent to existing approaches. Our results show that participants were equally willing to use (and just as productive using) IDEAHOUND compared to a conventional platform that did not require organizing ideas. Our integrated crowdsourcing approach also creates a more accurate semantic model than an existing crowdsourced approach (performed by external crowds). We demonstrate how this model enables helpful creative interventions: providing diverse inspirational examples, providing similar ideas for a given idea and providing a visual overview of the solution space.

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Collaborative fixation: Effects of others' ideas on brainstorming

Cited by 198 publications

References 55 publications

On Suggesting Phrases vs. Predicting Words for Mobile Text Composition

On Suggesting Phrases vs. Predicting Words for Mobile Text Composition

Deciphering the Enigma of Human Creativity: Can a Digital Computer Think?

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