Analyzing Perceptual Cognitive Characteristics of Product Forms with Eye-tracking and Electroencephalogram Techniques
-
摘要: 为产品感性形态设计的认知特征提供客观生理层面的深入剖析。运用眼动、脑电技术,以家用充电桩形态为刺激对象,基于语义差异量表设计并实施眼-脑感性认知实验,并记录被试“极‘不符合’、有点‘不符合’、‘不确定是否符合’、有点‘符合’、极‘符合’用户感性期望”5种感性认知结果下的眼-脑生理信息,剖析认知特性。研究结果表明:在产品形态极“符合”用户感性期望时,被试行为反应时最短,首次注视时间最少,且能产生中央-顶区联合皮层和顶区较强烈的P3、LPP波幅。在产品形态极“不符合”用户感性期望时,被试行为反应时、首次注视时间增加,后颞N2成分幅值最大;在产品形态“不确定”是否符合用户感性期望时,被试中央区产生的N4波幅增大。从感性意象认知的神经加工机制出发,行为、眼-脑生理信息均能客观有效区分用户不同感性意象的认知状态,视觉、脑认知特性可为设计提供认知本源性的底层支持。Abstract: The cognitive characteristics of the perceptual product form were analyzed and designed on the objective physiological level. The experiments on the cognitive characteristics, combined with eye-tracking (ET) and electroencephalogram (EEG), were designed and carried out with the household charging piles′ forms as the stimulation. The results were collected wirelessly with the experiments on cognitive characteristics. The E-prime programming was used to measure the eye fixation time under the five perceptual cognitive characteristics: extremely "non-conforming", a little "non-conforming", "uncertain", a little "conforming" and extremely "conforming". The results show that when the product form is extremely "conforming" to the subject′s cognitive characteristics, the response time is shorter and the first fixation time is reduced. The product form can produce stronger P3 and LPP amplitudes in the central-parietal area associated with cortex and the parietal area. For the "non-conforming" cognitive characteristic, the subject′s behavioral response time and first fixation time increase, and the amplitude of the N2 component in the posterior temporal area increases. When the product form is "uncertain" whether it meets the subject′s perceptual cognition, the subject′s N4 wave amplitude generated in the central brain area increases. The analysis of the neural processing mechanism of perceptual image cognition, the behavior, ET and EEG data can objectively and effectively reflect the cognitive process of the user′s perceptual image and provide cognitive support for the design.
-
表 1 被试样本量信度检验结果
Table 1. Sample size reliability test results
检验功效 σ 1.000 00 100.0 1.000 00 200.0 1.000 00 300.0 0.999 50 400.0 0.996 59 500.0 0.973 07 600.0 0.915 08 700.0 表 2 家用充电桩形态代表性感性词组选取结果
Table 2. Selection results of representative perceptual word phrases for household charging station forms
意象词组(选取次数达1/3以上) 立体的-扁平的创新的-守旧的轻薄的-厚重的变化的-单一的智能的-机械的安全的-危险的圆润的-硬朗的小巧的-巨大的豪华的-廉价的简单的-复杂的 4组感性词组 守旧的-创新的复杂的-简单的厚重的-轻薄的机械的-智能的 表 3 用户感性认知结果和相关描述
Table 3. User perceptual-cognitive results and corresponding descriptions
感性意象评价值 感性认知结果 相关描述 1 极“不符合”用户感性期望 产品特征极具有“守旧的、复杂的、厚重的、机械的”的感性意象 2 有点“不符合”用户感性期望 产品特征有点具有“守旧的、复杂的、厚重的、机械的”的感性意象 3 “不确定是否符合”用户感性期望 中性, 用户难以根据目标刺激判断是否符合心理期望 4 有点“符合”用户感性期望 产品特征有点具有“创新的、简单的、轻薄的、智能的”的感性意象 5 极“符合”用户感性期望 产品特征极具有“创新的、简单的、轻薄的、智能的”的感性意象 表 4 感性意象评价值对应的平均反应时统计表
Table 4. Statistical table of average reaction time corresponding to perceptual image evaluation values
感性意象评价值 平均反应时(标准差)/ms 1 1 208.55(±483.10) 2 1 710.68(±838.45) 3 1 604.83(±1 081.50) 4 1 466.54(±632.88) 5 1 063.95(±361.48) 表 5 感性认知结果下样本热点图
Table 5. Heatmap of samples under perceptual cognitive results
感性维度 “不符合”用户感性期望 “不确定”是否符合用户感性期望 “符合”用户感性期望 守旧的-创新的 复杂的-简单的 厚重的-轻薄的 机械的-智能的 表 6 感性意象评价值与眼动指标相关系数和双尾检验结果
Table 6. Correlation analysis results between perceptual image evaluation values and eye movement indicators
名称 进入时间 凝视时间 眼跳时间 转换时间 首次注视时间 眼跳次数 注视次数 感性意象评价值相关系数 -0.070** 0.094** 0.092** 0.088** 0.068** -0.002 0.044 感性意象评价值Sig.(双尾) 0.003 0.000 0.000 0.000 0.004 0.943 0.070 注:“**”表示强相关。 表 7 感性认知结果对应的眼动指标统计结果
Table 7. Statistical results of eye movement indicators corresponding to perceptual-cognitive results
感性认知结果 进入时间/ms 凝视时间/ms 眼跳时间/ms 转换时间/ms 首次注视时间/ms 眼跳次数/次 注视次数/次 极“不符合” 1 371.680 477.617 5 505.132 5 532.797 5 145.937 5 1.115 0 1.520 0 “不确定” 1 217.445 434.480 0 457.272 5 477.2150 122.3650 0.897 5 1.282 5 极“符合” 1 114.540 426.090 0 447.220 0 465.582 5 128.222 5 0.795 0 1.152 5 表 8 感性意象评价值与各脑区相关系数和双尾检验结果
Table 8. Correlation analysis results between perceptual image evaluation values and various brain regions
名称 前额 中央区 前额-中央区 中央-顶区 顶区 枕区 前颞 中颞 后颞 感性意象评价值相关系数 0.388** 0.255* 0.581** -0.360** 0.119 -0.287* 0.119 0.571** -0.666** 感性意象评价值Sig.(双尾) 0.001 0.033 0.000 0.002 0.327 0.016 0.328 0.000 0.000 注:“*”表示弱相关,“**”表示强相关。 表 9 意象(明确/模糊)状态下各电极平均波幅独立样本t检验结果统计
Table 9. Independent sample t test results for the average waveform amplitude of electrodes under clear/ambiguous imagery states
脑电成分 时间窗/ms 电极 意象明确/μV 意象模糊/μV t值 p值 均值 标准差 均值 标准差 CP5 -0.472 0.111 -0.183 0.017 -4.446 0.011 CP1 0.458 0.158 -0.015 0.140 3.877 0.018 N2 200~220 O2 -2.982 0.150 -2.442 0.146 -4.458 0.011 P7 -1.612 0.143 -0.861 0.134 -6.645 0.003 P8 -2.691 0.028 -2.409 0.123 -3.871 0.018 CP5 1.375 0.050 0.738 0.037 17.850 0.000 P3 CP1 1.284 0.073 0.452 0.072 14.059 0.000 310~330 O1 1.275 0.128 0.409 0.023 11.559 0.000 N3 F7 0.234 0.046 -0.258 0.013 17.626 0.000 T8 -1.098 0.033 -0.309 0.068 -17.966 0.000 Cz -1.183 0.056 -2.858 0.090 27.404 0.000 FC1 -1.323 0.027 -1.917 0.011 35.739 0.000 FC5 -0.195 0.047 -1.122 0.100 14.527 0.000 N4 440~460 CP5 -0.105 0.063 -0.915 0.043 18.348 0.000 CP1 -0.204 0.068 -1.435 0.093 18.533 0.000 CP6 0.767 0.045 1.870 0.178 -10.397 0.000 P3 0.264 0.027 -0.237 0.055 14.114 0.000 Pz 1.070 0.046 0.403 0.030 20.917 0.000 Cz -0.678 0.030 -1.424 0.032 29.532 0.000 C4 -0.018 0.060 0.583 0.005 -17.163 0.000 LPP 590~610 FC5 0.112 0.043 -0.994 0.051 28.639 0.000 CP6 1.131 0.038 2.284 0.066 -26.096 0.000 P7 -1.347 0.030 2.654 0.223 -30.779 0.000 P8 2.138 0.205 3.411 0.048 -10.460 0.000 -
[1] 吕中意. 基于意图认知的复杂电气产品外观设计策略[J]. 图学学报, 2020, 41(5): 779-787. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX202005012.htmLYU Z Y. Exterior design strategies of complex electrical products based on intention cognition[J]. Journal of Graphics, 2020, 41(5): 779-787. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX202005012.htm [2] 王美超, 林丽, 万露, 等. 基于感性语意模糊因子评价的图案设计源码特征集筛选[J]. 图学学报, 2019, 40(6): 1048-1055. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201906009.htmWANG M C, LIN L, WAN L, et al. Feature set selection of pattern design source code based on perceptual semantic fuzzy factor evaluation[J]. Journal of Graphics, 2019, 40(6): 1048-1055. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201906009.htm [3] 陈香, 卫华. 基于结构熵权TOPSIS法的产品设计方案评估研究[J]. 图学学报, 2020, 41(3): 446-452. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX202003016.htmCHEN X, WEI H. Research on product design scheme evaluation based on TOPSIS method of structure entropy weight[J]. Journal of Graphics, 2020, 41(3): 446-452. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX202003016.htm [4] 李洁, 郭士杰. 人形机器人造型意象与用户情感认知研究[J]. 机械设计, 2019, 36(5): 134-138. doi: 10.13841/j.cnki.jxsj.2019.05.025LI J, GUO S J. Research on image modeling design and user emotional cognition of humanoid robot[J]. Journal of Machine Design, 2019, 36(5): 134-138. (in Chinese) doi: 10.13841/j.cnki.jxsj.2019.05.025 [5] TELPAZ A, WEBB R, LEVY D J. Using EEG to predict Consumers′ future choices[J]. Journal of Marketing Research, 2015, 52(4): 511-529. doi: 10.1509/jmr.13.0564 [6] 陈默, 王海燕, 薛澄岐, 等. 基于事件相关电位的产品意象-语义匹配评估[J]. 东南大学学报(自然科学版), 2014, 44(1): 58-62. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201401012.htmCHEN M, WANG H Y, XUE C Q, et al. Match judgments of semantic word-product image based on event-related potential[J]. Journal of Southeast University (Natural Science Edition), 2014, 44(1): 58-62. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201401012.htm [7] 沙春发, 潘欣云, 杨桦, 等. 基于眼动实验的产品造型与意象空间匹配度评估指标选取[J]. 科学技术与工程, 2019, 19(32): 105-111. doi: 10.3969/j.issn.1671-1815.2019.32.015SHA C F, PAN X Y, YANG H, et al. Cognition index selection for evaluating matching degree between product shapes and image space based on eye-tracking experiment[J]. Science Technology and Engineering, 2019, 19(32): 105-111. (in Chinese) doi: 10.3969/j.issn.1671-1815.2019.32.015 [8] 侯冠华, 卢国英. 标识设计中语义认知事件相关电位[J]. 同济大学学报(自然科学版), 2018, 46(11): 1582-1588. doi: 10.11908/j.issn.0253-374x.2018.11.017HOU G H, LU G Y. Event related potential of semantic cognition in sign design[J]. Journal of Tongji University (Natural Science), 2018, 46(11): 1582-1588. (in Chinese) doi: 10.11908/j.issn.0253-374x.2018.11.017 [9] SLANZI G, BALAZS J A, VELÁSQUEZ J D. Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention[J]. Information Fusion, 2017, 35: 51-57. doi: 10.1016/j.inffus.2016.09.003 [10] 杨程, 陈辰, 唐智川. 基于脑电的产品意象推理模型研究[J]. 机械工程学报, 2018, 54(23): 126-136. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201823015.htmYANG C, CHEN C, TANG Z C. Study of electroence-phalography cognitive model of product image[J]. Journal of Mechanical Engineering, 2018, 54(23): 126-136. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201823015.htm [11] 钟奇, 裴学胜, 郭钢, 等. 基于多项眼动数据的拖拉机造型设计评选模型[J]. 包装工程, 2018, 39(8): 166-169. https://www.cnki.com.cn/Article/CJFDTOTAL-BZGC201808036.htmZHONG Q, PEI X S, GUO G, et al. The selection model of tractor appearance design based on multiple eye movement data[J]. Packaging Engineering, 2018, 39(8): 166-169. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BZGC201808036.htm [12] 王苗辉, 李艳, 宋武, 等. 基于科学实验的人机交互界面设计研究——以格力空调为例[J]. 图学学报, 2019, 40(1): 181-185. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201901026.htmWANG M H, LI Y, SONG W, et al. Research on man-machine interface design based on scientific experiment——a case study of the improved design of Gree air conditioner interface[J]. Journal of Graphics, 2019, 40(1): 181-185. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201901026.htm [13] 朱月, 邓成连. 基于心理与生理测量的辽瓷文化意象基因研究[J]. 计算机辅助设计与图形学学报, 2020, 32(7): 1183-1191. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF202007021.htmZHU Y, DENG C L. Study on culture image meme of Liao Dynasty ceramics by psychological and physiological measurement[J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(7): 1183-1191. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF202007021.htm [14] 王雪霜, 郭伏, 刘玮琳, 等. 基于事件相关电位的产品外观情感测量研究[J]. 人类工效学, 2018, 24(1): 20-26. https://www.cnki.com.cn/Article/CJFDTOTAL-XIAO201801004.htmWANG X S, GUO F, LIU W L, et al. Affective measurement of product appearance by ERPs[J]. Chinese Journal of Ergonomics, 2018, 24(1): 20-26. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XIAO201801004.htm [15] 汪海彬, 卢家楣, 姚本先, 等. 职前教师情绪复杂性对情绪面孔加工的影响——来自行为、ERP和眼动的证据[J]. 心理学报, 2015, 47(1): 50-65. https://www.cnki.com.cn/Article/CJFDTOTAL-XLXB201501006.htmWANG H B, LU J M, YAO B X, et al. The impact of pre-service teachers′ emotional complexity on facial expression processing: evidences from behavioral, ERP and eye-movement study[J]. Acta Psychologica Sinica, 2015, 47(1): 50-65. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XLXB201501006.htm [16] 韩玉昌, 张健, 杨文兵. 认知风格影响框架效应的ERP研究[J]. 心理科学, 2014, 37(3): 549-554. https://www.cnki.com.cn/Article/CJFDTOTAL-XLKX201403006.htmHAN Y C, ZHANG J, YANG W B. The influence of cognitive style on the framing effect: an event-related potential study[J]. Journal of Psychological Science, 2014, 37(3): 549-554. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XLKX201403006.htm