Design exploration predicts designer creativity : a deep learning approach
© Springer Nature B.V. 2020..
This study examined brain activation in graphic designers responding to pictorial stimulation during exploration tasks and determined the predictive effects of design exploration on designer creativity through a deep learning approach. The top and bottom 25% (10 each participants) were assigned high-creativity and low-creativity groups, respectively. The results provided the following indications. (i) Shallow architectures had higher prediction accuracy than deeper architectures. (ii) The prediction accuracy of shallow long short-term memory networks was higher than that of convolution neural networks. (iii) Bandpower exhibited increased prediction accuracy, and shallow LSTM networks with differing power spectra among independent components outperformed other deep learning methods. (iv) Direct acyclic graph networks did not improve prediction accuracy. (v) Design exploration could effectively predict designer creativity.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Cognitive neurodynamics - 14(2020), 3 vom: 17. Juni, Seite 291-300 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Yu-Cheng [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Revised 02.06.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s11571-020-09569-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM309820936 |
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| 520 | |a This study examined brain activation in graphic designers responding to pictorial stimulation during exploration tasks and determined the predictive effects of design exploration on designer creativity through a deep learning approach. The top and bottom 25% (10 each participants) were assigned high-creativity and low-creativity groups, respectively. The results provided the following indications. (i) Shallow architectures had higher prediction accuracy than deeper architectures. (ii) The prediction accuracy of shallow long short-term memory networks was higher than that of convolution neural networks. (iii) Bandpower exhibited increased prediction accuracy, and shallow LSTM networks with differing power spectra among independent components outperformed other deep learning methods. (iv) Direct acyclic graph networks did not improve prediction accuracy. (v) Design exploration could effectively predict designer creativity | ||
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