Visual prediction of statistical information across physical movements
| Project/Area Number |
16K16073
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Cognitive science
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| Research Institution | Tokyo Metropolitan University |
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Principal Investigator |
Banno Hayaki 首都大学東京, 人間健康科学研究科, 日本学術振興会特別研究員(PD) (00707440)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 認知科学 / 実験心理学 / 統計的要約 / 予測的知覚 / 身体運動 / 実験系心理学 |
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| Outline of Final Research Achievements |
Main findings in this research are human sensitivity to the perception of statistical covariation between visual features. Although the initial purpose was to investigate how statistical information is visually predicted across physical movements, I decided to know the candidate for the statistical information to be predicted. I investigated whether human correctly perceive statistical covariation in a visual scene by briefly presenting a display of several circles filled with sinusoidal gratings differing in their sizes, presentation locations and sinusoidal orientations. Results demonstrated a significantly lower performance in judging the covariation of the size-orientation pair compared with that of the location-related pairs, which suggests that during a brief viewing, human perception of statistical covariation is specific to the location dimension. They also suggested that the feature-specific performance was not related to a scarcity of attentional allocation on each circle.
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| Academic Significance and Societal Importance of the Research Achievements |
成果は，本研究の当初の目指すところであった統計構造の予測研究にどのような刺激を用いればいいかを決めることに有用なものである。別の観点からも，本研究成果は意義深いものである。第一に，統計構造知覚のメカニズムを考える良いヒントとなる。第二に，データの視覚表現という応用的見地からも有用である。相関関係を視覚的にどのように表現するか，そのデザインに制約を与えることに貢献しうる。
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Report
(4 results)
Research Products
(7 results)
