清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Effects of attention on the asymmetric serial dependences between form and motion patterns and their computational processes

心理学 运动(物理) 认知心理学 沟通 人工智能 计算机科学
作者
Qian Sun,Qian Sun,Siyu Wang,Meng-Ying Sun,Fan-Huan You,Ping Ran,Qi Sun,Qi Sun
出处
期刊:Frontiers in Psychology [Frontiers Media]
卷期号:16: 1505031-1505031
标识
DOI:10.3389/fpsyg.2025.1505031
摘要

Recent studies have revealed that serial dependences are asymmetric in the estimation of the focus of expansion (FoE) in the global static form and dynamic optic flow displays. In the current study, we conducted two experiments to examine whether and how attention affected the serial dependences between the two displays. The results showed that when all attentional resources are allocated to the FoE estimation task, the serial dependence of the form FoE estimation on the previous flow FoE ( SDE flow − form ) still existed even as the flow FoE was 40°, while the serial dependence of the flow FoE estimation on the previous form FoE ( SDE form − flow ) disappeared as the form FoE was beyond 30°. When attentional resources are distributed by other tasks, the SDE flow − form tended to be stronger than the SDE form − flow . Therefore, the SDE flow − form and SDE form − flow are asymmetric regardless of observers' attentional states. Finally, we developed two Bayesian models to address the computational mechanism underlying the attentional effects. Both models proposed that attention modulated the certainty of sensory representations of currently presented features. In addition, the effects of working memory on previously presented features were considered in one model. The results showed that the Bayesian inference model that included working memory predicted participants' performances better than the model without considering working memory. In summary, the current study demonstrated that attention and working memory affected the serial dependences between form and flow displays, and the effects could be quantitatively predicted by Bayesian inference models.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
翟庆春完成签到,获得积分10
2秒前
23秒前
羞涩的小白菜完成签到,获得积分10
23秒前
慧子完成签到 ,获得积分10
36秒前
39秒前
成就云朵完成签到,获得积分10
1分钟前
1分钟前
飞龙在天完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
zhuzhen007完成签到 ,获得积分10
1分钟前
帅气寄风完成签到,获得积分10
1分钟前
1分钟前
2分钟前
xue完成签到 ,获得积分10
2分钟前
精明寒松完成签到 ,获得积分10
2分钟前
5555完成签到,获得积分10
2分钟前
清爽笙完成签到,获得积分10
2分钟前
2分钟前
铃铛完成签到 ,获得积分10
3分钟前
3分钟前
仁爱的鞋子完成签到,获得积分10
3分钟前
3分钟前
房天川完成签到 ,获得积分10
3分钟前
3分钟前
3分钟前
3分钟前
3分钟前
4分钟前
joycelin发布了新的文献求助10
4分钟前
靓丽的初丹完成签到,获得积分10
4分钟前
chy完成签到 ,获得积分10
4分钟前
joycelin完成签到,获得积分10
4分钟前
4分钟前
5分钟前
所所应助哈哈我采纳,获得10
5分钟前
5分钟前
5分钟前
5分钟前
从容的绿蝶完成签到,获得积分10
5分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Römisch-Germanische Forschungen 1000
Social Psychology (第二版) 700
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7612715
求助须知:如何正确求助?哪些是违规求助? 9188098
关于积分的说明 19683628
捐赠科研通 7186104
什么是DOI,文献DOI怎么找? 3270731
关于科研通互助平台的介绍 2434302
邀请新用户注册赠送积分活动 2265643