心理科学进展 ›› 2023, Vol. 31 ›› Issue (1): 127-144.doi: 10.3724/SP.J.1042.2023.00127
收稿日期:
2021-12-02
出版日期:
2023-01-15
发布日期:
2022-10-13
通讯作者:
迟立忠
E-mail:Chilizh3804@163.com
基金资助:
Received:
2021-12-02
Online:
2023-01-15
Published:
2022-10-13
Contact:
CHI Lizhong
E-mail:Chilizh3804@163.com
摘要:
动作视频游戏是电子竞技中受众最广、心智挑战最高的游戏项目之一。目前对动作视频游戏专业玩家的能力特征了解尚不清晰。以职业选手和排名较高的玩家为对象, 横断研究发现动作视频游戏专业玩家具有更快的注意选择、更稳定的持续注意、更好的注意瞬脱表现、更高的多目标追踪能力和工作记忆容量。专业玩家更好的注意表现主要与更高的P3波幅有关, 工作记忆表现与背外侧前额叶和右后顶叶等可塑性变化有关。此外, 专业玩家还存在中央执行网络与多个脑网络间的功能连接增强。目前游戏训练对认知的促进程度尚不足以弥补专业玩家和新手的认知差距。基础的认知能力对玩家游戏表现的预测有限。未来可扩展更高级的游戏决策, 游戏组块或游戏模式的认知研究。
中图分类号:
苗浩飞, 迟立忠. (2023). 动作视频游戏专业玩家的认知神经特征. 心理科学进展 , 31(1), 127-144.
MIAO Haofei, CHI Lizhong. (2023). Cognitive neural characteristics of professional action video game players. Advances in Psychological Science, 31(1), 127-144.
文献 | 分组(n) | 认知范式(指标) | 差异量 | 显著性 | 效应量 |
---|---|---|---|---|---|
2020 | 职业选手(14), 业余玩家(16) | 注意持续(波动率) | 20.00% | p = 0.04 | Hedges’s g = 0.75 |
注意持续(正确数) | 9.88% | p = 0.03 | Hedges’s g = 0.81 | ||
空间广度 | 17.95% | p = 0.002 | Hedges’s g = 1.27 | ||
数字广度 | 15.93% | p = 0.04 | Hedges’s g = 0.82 | ||
Stroop | - | p > 0.05 | - | ||
MOT (速度阈限) | 未报告 | p = 0.03 | 未报告 | ||
汉诺塔(正确率) | - | p = 0.08 | - | ||
拼图 | - | p > 0.05 | - | ||
2012 | 专业玩家(23), 新手(21) | 转换任务(交互作用) | 未报告 | p = 0.005 | η2 p = 0.18 |
Flanker | - | p > 0.05 | - | ||
2018 | 职业选手(10), 青训选手(10), 业余玩家(20) | Flanker | - | p > 0.05 | - |
MOT (追踪数量) | 未报告 | p = 0.04 | 未报告 | ||
2020 | 专业玩家(19), 业余玩家(19) | AB (T2正确率) | 约10% | p < 0.001 | d = 2.43 |
2020 | 精英职业选手(12), 一般职业选手(43) | 伦敦塔(反应时) | 16.90% | p < 0.001 | Hedges’s g = 1.30 |
心理旋转(正确率) | 6.50% | p = 0.001 | Hedges’s g = 1.70 | ||
2020 | 专业玩家(35), 业余玩家(35) | 任务转换(错误率) | 3.79% | p < 0.001 | 未报告 |
任务转换(转换代价) | 1.97% | p = 0.020 | d = 0.57 | ||
CPT (命中率) | 3.74% | p = 0.003 | η2 p = 0.12 | ||
CPT (误报率) | 10.26% | p = 0.005 | η2 p = 0.11 | ||
2018 | 专业玩家(15), 业余玩家(14) | UFOV (反应时) | 11.33% | p < 0.001 | Hedges’s g = 2.67 |
2013 | 职业选手(17), 新手(33) | SWM (正确率) | 5.70% | p = 0.02 | 未报告 |
2020 | 专业玩家(18), 业余玩家(19) | SWM (2个组块-正确率) | 2% | p = 0.006 | Hedges’s g = 0.87 |
SWM (4个组块-正确率) | 4% | p = 0.007 | Hedges’s g = 0.94 | ||
SWM (8个组块-正确率) | 8% | p = 0.002 | Hedges’s g = 1.12 |
表2 专业玩家和对照组的认知差异
文献 | 分组(n) | 认知范式(指标) | 差异量 | 显著性 | 效应量 |
---|---|---|---|---|---|
2020 | 职业选手(14), 业余玩家(16) | 注意持续(波动率) | 20.00% | p = 0.04 | Hedges’s g = 0.75 |
注意持续(正确数) | 9.88% | p = 0.03 | Hedges’s g = 0.81 | ||
空间广度 | 17.95% | p = 0.002 | Hedges’s g = 1.27 | ||
数字广度 | 15.93% | p = 0.04 | Hedges’s g = 0.82 | ||
Stroop | - | p > 0.05 | - | ||
MOT (速度阈限) | 未报告 | p = 0.03 | 未报告 | ||
汉诺塔(正确率) | - | p = 0.08 | - | ||
拼图 | - | p > 0.05 | - | ||
2012 | 专业玩家(23), 新手(21) | 转换任务(交互作用) | 未报告 | p = 0.005 | η2 p = 0.18 |
Flanker | - | p > 0.05 | - | ||
2018 | 职业选手(10), 青训选手(10), 业余玩家(20) | Flanker | - | p > 0.05 | - |
MOT (追踪数量) | 未报告 | p = 0.04 | 未报告 | ||
2020 | 专业玩家(19), 业余玩家(19) | AB (T2正确率) | 约10% | p < 0.001 | d = 2.43 |
2020 | 精英职业选手(12), 一般职业选手(43) | 伦敦塔(反应时) | 16.90% | p < 0.001 | Hedges’s g = 1.30 |
心理旋转(正确率) | 6.50% | p = 0.001 | Hedges’s g = 1.70 | ||
2020 | 专业玩家(35), 业余玩家(35) | 任务转换(错误率) | 3.79% | p < 0.001 | 未报告 |
任务转换(转换代价) | 1.97% | p = 0.020 | d = 0.57 | ||
CPT (命中率) | 3.74% | p = 0.003 | η2 p = 0.12 | ||
CPT (误报率) | 10.26% | p = 0.005 | η2 p = 0.11 | ||
2018 | 专业玩家(15), 业余玩家(14) | UFOV (反应时) | 11.33% | p < 0.001 | Hedges’s g = 2.67 |
2013 | 职业选手(17), 新手(33) | SWM (正确率) | 5.70% | p = 0.02 | 未报告 |
2020 | 专业玩家(18), 业余玩家(19) | SWM (2个组块-正确率) | 2% | p = 0.006 | Hedges’s g = 0.87 |
SWM (4个组块-正确率) | 4% | p = 0.007 | Hedges’s g = 0.94 | ||
SWM (8个组块-正确率) | 8% | p = 0.002 | Hedges’s g = 1.12 |
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