發(fā)布時間：2018-01-28 08:17

  本文關(guān)鍵詞： 工業(yè)設(shè)計 產(chǎn)品意象 事件相關(guān)電位 產(chǎn)品造型感知　出處：《機械設(shè)計》2017年06期 　論文類型：期刊論文

【摘要】：為更客觀地獲取用戶的產(chǎn)品意象數(shù)據(jù),應(yīng)用事件相關(guān)電位技術(shù)探討了用戶感知意象的思維表征。對154個國際知名品牌中的2 500款汽車造型進行篩選,得到經(jīng)典車型720款。以16名大學生用戶作為被試者,分別從中選擇美觀、中等和不美觀圖片各60幅,應(yīng)用事件相關(guān)電位技術(shù)測試這些用戶在欣賞各類圖片時的腦電信號,并應(yīng)用口語分析法獲取其心理活動。結(jié)果顯示,在第100 ms和第200 ms時,在大腦額葉Fz附近產(chǎn)生了腦電成分N1和P2;在第440 ms和第650~920 ms時,在大腦頂葉Cz,Cp1,Cp2和Pz位置產(chǎn)生了中期成分和晚成分。結(jié)果表明:漂亮、獨特、協(xié)調(diào)、流暢、硬朗和簡潔是大學生用戶感知汽車造型的主要判斷標準。在該感知過程中,用戶在Cz,Cp1,Cp2及Pz位置的中期成分和晚成分可表征大腦思維,且兩種成分的波幅可作為評價產(chǎn)品意象值的重要指標。該研究結(jié)果有助于提升產(chǎn)品意象獲取手段的客觀性,并有望應(yīng)用于產(chǎn)品造型設(shè)計。
[Abstract]:In order to obtain the user's product image data more objectively, the thinking representation of the user's perceived image was discussed by using event-related potential technology. The 2,500 models of the 154 international famous brands were screened. We got 720 classic models and took 16 college students as the subjects. We chose 60 beautiful, medium and unattractive pictures from each of them. The electroencephalogram (EEG) signals of these users were measured by event-related potentials and their psychological activities were obtained by oral analysis. The results showed that at 100ms and 200ms respectively. The brain electrical components N1 and P2were produced near FZ in the frontal lobe of the brain. At the 440ms and 650s 920ms, the metaphase and late components were produced in the Cp1Cp2 and Pz position of the apical lobe Cz. the results showed that they were beautiful, unique and coordinated. Fluency, hardness and simplicity are the main criteria for college students to perceive car modeling. In the process of perception, the user is in CzCp1. The intermediate and late components of Cp2 and PZ positions can be used to characterize brain thinking. The amplitude of the two components can be used as an important index to evaluate the value of product image. The results are helpful to enhance the objectivity of product image acquisition and are expected to be used in product modeling design.
【作者單位】： 華南農(nóng)業(yè)大學藝術(shù)學院;廣東省服裝創(chuàng)新設(shè)計工程技術(shù)研究中心;
【基金】：廣東省科技計劃資助項目(2014B090904076) 廣東省哲學社會科學規(guī)劃資助項目(GD16CYS10)
【分類號】：TB47
【正文快照】： 產(chǎn)品意象研究通過應(yīng)用神經(jīng)網(wǎng)絡(luò)[1]、遺傳算法[2]、主成分分析[3]、Mars[4]等方法構(gòu)建產(chǎn)品意象與用戶感知之間的內(nèi)在機制,有效提升了產(chǎn)品設(shè)計質(zhì)量。這些研究主要采用語義差異法等方法獲取用戶感知意象,以解決其模糊性和不確定性[5]。近年來,事件相關(guān)電位(Event-Related Potenti，

本文編號：1470239