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!

Integrated effects of top-down attention and statistical learning during visual search: An EEG study

提示语 N2pc 心理学 脑电图 视觉搜索 认知心理学 注意力控制 视觉空间注意 自上而下和自下而上的设计 控制(管理) 视觉注意 神经科学 计算机科学 感知 认知 人工智能 软件工程
作者
Carola Dolci,C. Nico Boehler,Elisa Santandrea,Anneleen Dewulf,Suliann Ben Hamed,Emiliano Macaluso,Leonardo Chelazzi,Einat Rashal
出处
期刊:Attention, perception & psychophysics [Springer Nature]
卷期号:85 (6): 1819-1833 被引量:7
标识
DOI:10.3758/s13414-023-02728-y
摘要

Abstract The present study aims to investigate how the competition between visual elements is solved by top-down and/or statistical learning (SL) attentional control (AC) mechanisms when active together. We hypothesized that the “winner” element that will undergo further processing is selected either by one AC mechanism that prevails over the other, or by the joint activity of both mechanisms. To test these hypotheses, we conducted a visual search experiment that combined an endogenous cueing protocol (valid vs. neutral cue) and an imbalance of target frequency distribution across locations (high- vs. low-frequency location). The unique and combined effects of top-down control and SL mechanisms were measured on behaviour and amplitudes of three evoked-response potential (ERP) components (i.e., N2pc, P1, CNV) related to attentional processing. Our behavioural results showed better performance for validly cued targets and for targets in the high-frequency location. The two factors were found to interact, so that SL effects emerged only in the absence of top-down guidance. Whereas the CNV and P1 only displayed a main effect of cueing, for the N2pc we observed an interaction between cueing and SL, revealing a cueing effect for targets in the low-frequency condition, but not in the high-frequency condition. Thus, our data support the view that top-down control and SL work in a conjoint, integrated manner during target selection. In particular, SL mechanisms are reduced or even absent when a fully reliable top-down guidance of attention is at play.

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
Affenyi发布了新的文献求助10
4秒前
6秒前
8秒前
9秒前
斯文败类应助读书的时候采纳,获得30
16秒前
17秒前
分析完成签到 ,获得积分10
22秒前
刘刘完成签到 ,获得积分10
24秒前
Suraim完成签到,获得积分10
29秒前
科研通AI2S应助读书的时候采纳,获得30
35秒前
36秒前
47秒前
热情依白应助读书的时候采纳,获得10
53秒前
54秒前
1分钟前
1分钟前
1分钟前
仁爱保温杯完成签到,获得积分10
1分钟前
热情依白应助读书的时候采纳,获得10
1分钟前
hhh完成签到 ,获得积分10
1分钟前
hhhpass应助科研通管家采纳,获得10
1分钟前
丘比特应助读书的时候采纳,获得30
1分钟前
1分钟前
wanci应助Ahan采纳,获得10
1分钟前
1分钟前
CodeCraft应助读书的时候采纳,获得10
1分钟前
1分钟前
2分钟前
2分钟前
2分钟前
2分钟前
SciGPT应助读书的时候采纳,获得10
2分钟前
2分钟前
2分钟前
2分钟前
2分钟前
2分钟前
2分钟前
2分钟前
2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
2025-2031全球及中国金刚石触媒粉行业研究及十五五规划分析报告 9000
Encyclopedia of the Human Brain Second Edition 8000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5688129
求助须知:如何正确求助?哪些是违规求助? 5063718
关于积分的说明 15193691
捐赠科研通 4846465
什么是DOI,文献DOI怎么找? 2598868
邀请新用户注册赠送积分活动 1550976
关于科研通互助平台的介绍 1509573