| Project/Area Number |
18K12012
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 90030:Cognitive science-related
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| Research Institution | Kyoto Institute of Technology |
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Principal Investigator |
Nishizaki Yukiko 京都工芸繊維大学, 情報工学・人間科学系, 准教授 (60705945)
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| Project Period (FY) |
2018-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | マルチタスク / 自動車運転 / 認知資源 / 個人差 / 認知資源容量 / 認知工学 |
|---|
| Outline of Final Research Achievements |
Driving represents a quintessential multitasking activity. Given significant individual differences in multitasking capabilities, this study aimed to produce insights to develop personalized driving support systems. The research explored the relationships between multitasking performance, cognitive resource allocation, social information processing, and the feasibility of their deliberate control. Multiple experiments showed that young individuals' multitasking performance is not related to their cognitive resource capacity as measured by OSPAN. In contrast, elderly individuals tend to decline in performance with a secondary task during driving, though some showed improvement. The study also highlighted differences in accepting instructions from autonomous vehicles, influenced by social information processing. Additionally, while controlling attention allocation to two tasks is challenging, actual performance can vary according to the directed attention allocation.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、実験心理学の手法を用いて、自動車の運転者支援システム開発に寄与する基礎的な知見を提供した点で意義深い。特に、運転の自動化が進む中で、車内での運転者の行動が多様化し、マルチタスクが増加することが予想される。本研究で明らかになった高齢者のマルチタスク遂行の特徴、自動運転車からの指示に対する個人差、および複数の課題への注意配分の制御可能性に関する知見は、個々の運転者の特性に応じた運転支援方法の設計に有用な指針を提供する。これにより、より安全で効果的な運転支援システムの開発が期待される。
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