) 辽宁师范大学心理学院, 大连 116029
收稿日期:2020-02-23出版日期:2021-12-25发布日期:2021-10-26通讯作者:马锦飞E-mail:majinfei@lnnu.edu.cn基金资助:教育部人文社会科学研究青年基金项目: “L3自动驾驶车辆驾驶员被动疲劳的心理机制及调控研究” (项目编号)(20YJC190015)Regular schematic start training in the process of drivers’ selective attention
YUAN Luyi, CHANG Ruosong, MA Jinfei() School of Psychology, Liaoning Normal University, Dalian 116029, China
Received:2020-02-23Online:2021-12-25Published:2021-10-26Contact:MA Jinfei E-mail:majinfei@lnnu.edu.cn摘要/Abstract
摘要： 本研究将驾驶无意视盲范式与交通违规事件识别任务相结合, 通过两个实验分别探究任务性质(有提示、无提示)和规则图式训练(训练、未训练)对驾驶员识别交通信号和违规事件的影响。结果发现, 交通规则提示能够提高有经验的驾驶员识别交通信号的正确率(实验1); 对于新手驾驶员, 则需要结合规则图式训练才能起到相同效果(实验2)。研究表明驾驶员选择性注意过程中存在规则图式启动效应, 图式训练能够弥补新手驾驶员经验的不足。
图/表 13
图1驾驶视频片段截图
图1驾驶视频片段截图
图2“禁止调头”兴趣区
图2“禁止调头”兴趣区
图3提示组的集簇图
图3提示组的集簇图
图4无提示组的集簇图 注: 集簇图是一种可在背景图上呈现注视点数据密度最大的区域的可视化图型。
图4无提示组的集簇图 注: 集簇图是一种可在背景图上呈现注视点数据密度最大的区域的可视化图型。
图5“禁止掉头”标志的搜索时间
图5“禁止掉头”标志的搜索时间
图6关键试次无意视盲比率 注: ***p < 0.001, **p < 0.01
图6关键试次无意视盲比率 注: ***p < 0.001, **p < 0.01表1实验1所有被试的搜索时间(秒)和视盲人数(人)
| 驾驶经验类型 | 搜索时间(秒) (M ± SE) | 视盲人数 |
|---|---|---|
| 有经验的驾驶员 | ||
| 提示(n = 17) | 0.32 ± 0.10 | 4 |
| 无提示(n = 18) | 0.23 ± 0.10 | 16 |
| 新手驾驶员 | ||
| 提示(n = 21) | 0.19 ± 0.06 | 14 |
| 无提示(n = 20) | 0.07 ± 0.04 | 17 |
表1实验1所有被试的搜索时间(秒)和视盲人数(人)
| 驾驶经验类型 | 搜索时间(秒) (M ± SE) | 视盲人数 |
|---|---|---|
| 有经验的驾驶员 | ||
| 提示(n = 17) | 0.32 ± 0.10 | 4 |
| 无提示(n = 18) | 0.23 ± 0.10 | 16 |
| 新手驾驶员 | ||
| 提示(n = 21) | 0.19 ± 0.06 | 14 |
| 无提示(n = 20) | 0.07 ± 0.04 | 17 |
表2个人差异及眼动搜索时间对视盲结果的 逻辑回归分析
| 变量 | B | SE | Wald | df | p | Exp (B) | Exb (B) 的95%CI |
|---|---|---|---|---|---|---|---|
| 性别 | -1.43 | 0.94 | 2.30 | 1 | 0.13 | 0.24 | [0.04, 01.51] |
| 年龄 | -0.01 | 0.07 | 0.03 | 1 | 0.86 | 0.99 | [0.87, 01.13] |
| 安全评分 | -0.03 | 0.04 | 0.47 | 1 | 0.49 | 0.97 | [0.89, 01.06] |
| 受教育程度 | 0.45 | 0.46 | 0.95 | 1 | 0.33 | 1.57 | [0.63, 03.91] |
| 搜索时间 | 1.92* | 0.82 | 4.77 | 1 | 0.02 | 6.82 | [1.37, 34.06] |
| 常量 | -1.61 | 2.22 | 0.05 | 1 | 0.47 | 0.20 |
表2个人差异及眼动搜索时间对视盲结果的 逻辑回归分析
| 变量 | B | SE | Wald | df | p | Exp (B) | Exb (B) 的95%CI |
|---|---|---|---|---|---|---|---|
| 性别 | -1.43 | 0.94 | 2.30 | 1 | 0.13 | 0.24 | [0.04, 01.51] |
| 年龄 | -0.01 | 0.07 | 0.03 | 1 | 0.86 | 0.99 | [0.87, 01.13] |
| 安全评分 | -0.03 | 0.04 | 0.47 | 1 | 0.49 | 0.97 | [0.89, 01.06] |
| 受教育程度 | 0.45 | 0.46 | 0.95 | 1 | 0.33 | 1.57 | [0.63, 03.91] |
| 搜索时间 | 1.92* | 0.82 | 4.77 | 1 | 0.02 | 6.82 | [1.37, 34.06] |
| 常量 | -1.61 | 2.22 | 0.05 | 1 | 0.47 | 0.20 |
图7禁停处停车兴趣区
图7禁停处停车兴趣区
图8实线并道兴趣区
图8实线并道兴趣区
图9驾驶员识别不同事件的正确得分 注：***p < 0.001, **p < 0.01
图9驾驶员识别不同事件的正确得分 注：***p < 0.001, **p < 0.01表3识别事件得分(分)和搜索时间(秒)
| 驾驶经验类型 | 实线得分 | 禁停得分 | 实线搜索时间(秒)(M ± SE) | 禁停搜索时间(秒)(M ± SE) |
|---|---|---|---|---|
| 有经验的驾驶员 | ||||
| 训练(n = 17) | 8.47 ± 0.45 | 7.65 ± 0.44 | 13.00 ± 0.88 | 16.15 ± 0.80 |
| 未训练(n = 16) | 7.44 ± 0.61 | 3.13 ± 0.70 | 9.28 ± 1.35 | 09.27 ± 1.31 |
| 新手驾驶员 | ||||
| 训练(n = 17) | 7.64 ± 0.47 | 5.00 ± 0.54 | 12.55 ± 1.82 | 07.26 ± 1.19 |
| 未训练(n = 16) | 5.25 ± 0.70 | 1.06 ±0.44 | 12.39 ± 1.40 | 05.71 ± 0.77 |
表3识别事件得分(分)和搜索时间(秒)
| 驾驶经验类型 | 实线得分 | 禁停得分 | 实线搜索时间(秒)(M ± SE) | 禁停搜索时间(秒)(M ± SE) |
|---|---|---|---|---|
| 有经验的驾驶员 | ||||
| 训练(n = 17) | 8.47 ± 0.45 | 7.65 ± 0.44 | 13.00 ± 0.88 | 16.15 ± 0.80 |
| 未训练(n = 16) | 7.44 ± 0.61 | 3.13 ± 0.70 | 9.28 ± 1.35 | 09.27 ± 1.31 |
| 新手驾驶员 | ||||
| 训练(n = 17) | 7.64 ± 0.47 | 5.00 ± 0.54 | 12.55 ± 1.82 | 07.26 ± 1.19 |
| 未训练(n = 16) | 5.25 ± 0.70 | 1.06 ±0.44 | 12.39 ± 1.40 | 05.71 ± 0.77 |
图10中介效应模型 注：***p < 0.001, **p < 0.01
图10中介效应模型 注：***p < 0.001, **p < 0.01参考文献 35
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