视觉搜索中的目标选择

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计算机视觉中的模型选择与调优方法

计算机视觉中的模型选择与调优方法

计算机视觉中的模型选择与调优方法计算机视觉是人工智能领域中的一个重要分支,它旨在使计算机系统能够从数字图像或视频中自动获取、分析和理解视觉信息。

在计算机视觉应用领域中,选择合适的模型并对其进行调优是非常关键的,它直接影响着算法的性能和准确度。

本文将介绍计算机视觉中常用的模型选择与调优方法。

模型选择是指根据任务的需求和数据集的特点,选择适合的计算机视觉模型。

在模型选择时,首先要考虑任务的类型,例如目标检测、图像分类、图像分割等。

不同的任务需要不同的模型架构和算法。

其次,要考虑数据集的规模和特点,包括图像分辨率、类别数、图像质量以及数据集的平衡性等。

根据任务类型和数据集特点,可以选择常用的计算机视觉模型,如卷积神经网络(CNN)、循环神经网络(RNN)等。

同时,还可以根据具体的需求选择已有的预训练模型,如VGG、ResNet等,进行迁移学习或微调。

模型调优是指通过优化算法和参数设置来进一步提升模型的性能和准确度。

以下是几种常见的模型调优方法:1. 数据增强:数据增强是通过对原始图像进行一系列变换,生成更多样化的训练样本。

常用的数据增强方法有随机裁剪、水平翻转、旋转、缩放等。

数据增强可以增加数据集的多样性,帮助模型更好地泛化。

2. 学习率调整:学习率是模型训练中非常重要的超参数,它控制着模型参数的更新速度。

在训练过程中,可以采用学习率衰减策略,逐渐降低学习率,以提高模型在训练后期的稳定性和收敛性。

3. 正则化:正则化是一种用于惩罚复杂模型的方法,以防止模型过拟合。

常用的正则化方法包括L1正则化和L2正则化。

通过在损失函数中引入正则化项,可以使模型的复杂度适中,提高其泛化能力。

4. 批归一化:批归一化是一种用于加速模型训练和提高模型准确度的技术。

它通过在每一层的输入数据上进行归一化操作,可以加速收敛过程,减少梯度消失和梯度爆炸问题。

除了以上方法,还有许多其他的模型调优技术,如优化器的选择、网络结构的改进等。

眼动测试实验报告(共)2024

眼动测试实验报告(共)2024

眼动测试实验报告引言概述:眼动测试是一种通过记录观察者眼睛在特定任务中的运动来评估其注意力和视觉认知的方法。

本实验旨在使用眼动测试来研究不同任务对观察者注意力和视觉注意的影响。

通过分析眼动数据,我们希望能够揭示人类在处理不同任务时的认知和注意机制。

正文内容:一、实验设计和方法1.实验目的本实验的目的是比较观察者在执行视觉搜索任务和视觉追踪任务时的眼动模式和注意力分配。

我们研究了两种任务对观察者眼动行为的影响。

2.实验参与者我们选取了30名健康成年人作为实验参与者,其中15名女性和15名男性。

参与者没有眼部疾病和视觉障碍,并在实验前签署了知情同意书。

3.实验材料实验使用了一台高分辨率眼动追踪设备记录参与者的眼动行为和注视点,以及一台计算机用于展示实验材料和记录数据。

4.实验步骤实验分为两个任务:视觉搜索任务和视觉追踪任务。

在视觉搜索任务中,参与者需要在一系列随机排列的图像中寻找特定目标。

在视觉追踪任务中,参与者需要跟踪一个移动的目标,并在移动结束后对它的位置进行判断。

5.数据分析通过眼动追踪设备记录的眼动数据,我们使用统计方法对参与者的眼动模式和注意力分配进行分析。

具体分析方法包括注视点热度图、注视点聚类分析和注视点转移矩阵分析。

二、眼动模式和注意力分配的比较1.视觉搜索任务下的眼动模式和注意力分配在视觉搜索任务中,参与者的眼睛会以快速而不规则的方式在图像上移动,以找到目标。

通常,参与者会先进行全局扫描,然后逐渐进入局部扫描的阶段。

注意力主要集中在目标周围,特别是目标的特征上。

2.视觉追踪任务下的眼动模式和注意力分配在视觉追踪任务中,参与者会将目光集中在移动的目标上,并尽量保持目标在中央凹陷处。

眼睛的运动轨迹通常比较平滑,且注视点的稳定性较高。

注意力主要集中在目标的位置上,以便进行位置判断。

3.眼动模式和注意力分配的差异通过比较视觉搜索任务和视觉追踪任务下的眼动模式和注意力分配,我们发现两者存在显著差异。

计算机视觉中的目标定位与检测技术研究

计算机视觉中的目标定位与检测技术研究

计算机视觉中的目标定位与检测技术研究计算机视觉是人工智能领域中的重要应用之一,目标定位与检测技术是计算机视觉中的核心问题之一。

目标定位与检测技术的目标是在图像或视频中准确地定位并识别出目标物体。

本文将探讨目标定位与检测技术在计算机视觉中的研究进展和应用。

目标定位与检测技术在计算机视觉中扮演着重要的角色。

它在许多领域中都有广泛的应用,比如智能监控、自动驾驶、工业质检等。

目标定位与检测技术的目标是在图像或视频中准确地定位并识别出目标物体。

它可以分为两个主要步骤:目标定位和目标检测。

目标定位是指在一个给定的图像或视频中确定目标物体的准确位置。

目标定位技术可以通过各种方法来实现,比如基于手工设计的特征提取方法和基于深度学习的方法。

手工设计的特征提取方法通常需要先定义一些特征描述子,然后通过计算图像中的这些特征描述子来确定目标物体的位置。

然而,这种方法通常需要大量的人工工作和领域知识,并且对于复杂的目标和场景往往不够稳定和准确。

与之相比,基于深度学习的目标定位方法能够通过学习大量的图像数据和特征来自动地定位目标物体。

深度学习模型可以从数据中学习到特征提取和模式识别的能力,并且可以根据目标物体的不同特征学习到不同的目标定位模型。

深度学习的发展为目标定位任务提供了更好的性能和鲁棒性。

目标检测是在给定的图像或视频中检测出目标物体,并给出其准确的位置和类别。

目标检测技术通常可以分为两种类型:基于传统的机器学习方法和基于深度学习的方法。

基于传统的机器学习方法通常需要先定义一些手工设计的特征,并使用分类器来对这些特征进行分类。

这种方法通常需要大量的特征工程和领域知识,并且对于复杂的目标和场景往往不够稳定和准确。

与之相比,基于深度学习的目标检测方法通过学习数据中的特征和模式来自动地检测目标物体。

深度学习模型可以通过大量的图像数据和特征来学习目标物体的不同特征,并且可以根据目标物体的不同特征学习到不同的目标检测模型。

基于深度学习的目标检测方法在准确性和鲁棒性方面通常优于传统的方法。

超详细!一文讲透机器视觉常用的 3 种“目标识别”方法

超详细!一文讲透机器视觉常用的 3 种“目标识别”方法

随着机器视觉技术的快速发展,传统很多需要人工来手动操作的工作,渐渐地被机器所替代。

传统方法做目标识别大多都是靠人工实现,从形状、颜色、长度、宽度、长宽比来确定被识别的目标是否符合标准,最终定义出一系列的规则来进行目标识别。

这样的方法当然在一些简单的案例中已经应用的很好,唯一的缺点是随着被识别物体的变动,所有的规则和算法都要重新设计和开发,即使是同样的产品,不同批次的变化都会造成不能重用的现实。

而随着机器学习,深度学习的发展,很多肉眼很难去直接量化的特征,深度学习可以自动学习这些特征,这就是深度学习带给我们的优点和前所未有的吸引力。

很多特征我们通过传统算法无法量化,或者说很难去做到的,深度学习可以。

特别是在图像分类、目标识别这些问题上有显著的提升。

视觉常用的目标识别方法有三种:Blob分析法(BlobAnalysis)、模板匹配法、深度学习法。

下面就三种常用的目标识别方法进行对比。

Blob分析法BlobAnalysis在计算机视觉中的Blob是指图像中的具有相似颜色、纹理等特征所组成的一块连通区域。

Blob分析(BlobAnalysis)是对图像中相同像素的连通域进行分析(该连通域称为Blob)。

其过程就是将图像进行二值化,分割得到前景和背景,然后进行连通区域检测,从而得到Blob块的过程。

简单来说,blob分析就是在一块“光滑”区域内,将出现“灰度突变”的小区域寻找出来。

举例来说,假如现在有一块刚生产出来的玻璃,表面非常光滑,平整。

如果这块玻璃上面没有瑕疵,那么,我们是检测不到“灰度突变”的;相反,如果在玻璃生产线上,由于种种原因,造成了玻璃上面有一个凸起的小泡、有一块黑斑、有一点裂缝,那么,我们就能在这块玻璃上面检测到纹理,经二值化(BinaryThresholding)处理后的图像中色斑可认为是blob。

而这些部分,就是生产过程中造成的瑕疵,这个过程,就是Blob分析。

Blob分析工具可以从背景中分离出目标,并可以计算出目标的数量、位置、形状、方向和大小,还可以提供相关斑点间的拓扑结构。

显著性目标检测技术在视觉检索中的应用

显著性目标检测技术在视觉检索中的应用

显著性目标检测技术在视觉检索中的应用近年来,随着视觉技术的发展,显著性目标检测技术得到了广泛应用。

显著性目标检测技术指的是通过计算图像中每个像素的显著性值,来确定哪些区域是图像中的重点目标。

这项技术对于图像处理和视觉检索非常有用,因为它可以加快图像搜索和分类的速度,从而提高视觉检索的准确性和效率。

一、显著性目标检测技术的原理显著性目标检测技术的原理是通过计算每个像素的“显著性值”来确定哪些区域是该图像的重点目标。

这个“显著性值”可以理解为该像素对于整张图片的视觉重要性。

计算显著性值的方法有很多种,最常用的方法是运用计算机视觉和机器学习算法。

例如,利用神经网络来训练一个模型,将模型应用于图像中,就可以计算出每个像素的显著性值。

这个模型可以根据需要进行修改和调整,以适应不同的应用场景。

二、显著性目标检测技术的应用显著性目标检测技术可以在很多领域中应用,如图像搜索、图像重排、图像分类和视频分析等。

其主要优势在于,它可以帮助用户快速获得需要的信息。

下面我们来看一些具体的应用:1. 图像搜索:当我们需要找到一张图片中的特定物品时,显著性目标检测技术可以帮助我们快速地定位到该物品。

例如,当我们需要找到一张包含汽车的图片时,显著性目标检测技术可以帮助我们快速地找到汽车的位置,然后进一步筛选与该关键词有关的图片,以提高搜索的效率。

2. 图像重排:对于一个图库中的图片进行重排时,显著性目标检测技术可以帮助我们将重要的图片排在更靠前的位置。

这有助于提高用户检索相关图片的速度。

3. 图像分类:显著性目标检测技术可以帮助计算机快速、准确地对一张图片进行分类。

例如,在图书馆分类系统中,利用显著性目标检测技术对不同的图书进行分类,可以提高分类的准确性和效率。

4. 视频分析:对于大量视频的分析,显著性目标检测技术可以用于快速识别视频中的重要信息。

例如,在监控视频分析中,计算机可以利用显著性目标检测技术来快速地找到特定的人或车辆,从而帮助警方迅速解决问题。

如何使用计算机视觉技术进行目标识别与定位

如何使用计算机视觉技术进行目标识别与定位

如何使用计算机视觉技术进行目标识别与定位目标识别与定位是计算机视觉技术中的一个重要任务,它在许多应用领域都具有广泛的应用价值。

通过计算机视觉技术进行目标识别与定位可以帮助我们实现自动化、智能化的处理和分析。

本文将介绍如何使用计算机视觉技术进行目标识别与定位。

一、什么是目标识别与定位目标识别是指从图像或视频中自动识别并划定出感兴趣的目标物体。

目标定位是指在识别出目标物体后,进一步确定物体在图像中的位置和边界框。

二、计算机视觉技术在目标识别与定位中的应用1. 特征提取:在目标识别与定位中,首先需要从图像中提取有用的特征。

常用的特征提取方法包括颜色直方图、边缘特征、纹理特征等。

这些特征可以帮助我们描述目标物体的特点和区分不同的目标。

2. 图像分类:在目标识别过程中,需要将识别出的目标与已知的目标进行分类。

图像分类是指将输入的图像分到多个已知类别中的一种。

常用的图像分类方法包括支持向量机(SVM)、卷积神经网络(CNN)等。

3. 目标检测与定位:目标检测与定位是指在图像中定位和识别出多个目标物体。

常见的目标检测与定位方法包括滑动窗口、区域提案(region proposal)等。

这些方法通过在图像的不同位置和尺度上进行检测,得到目标的位置和边界框。

三、计算机视觉技术中常用的目标识别与定位方法1. Haar特征级联分类器:Haar特征级联分类器是一种基于机器学习的目标识别与定位方法。

它通过训练一组特征分类器,来识别出目标物体。

Haar特征级联分类器在人脸识别中得到了广泛的应用。

2. HOG特征+SVM分类器:HOG特征和SVM分类器是一对经典的目标识别与定位方法。

HOG特征是一种基于梯度的特征描述子,它能够描述图像中物体的形状和边缘信息。

SVM分类器是一种常用的机器学习分类器,能够将输入的图像进行分类。

3. R-CNN系列方法:R-CNN系列方法是一种基于区域提案的目标检测与定位方法。

它通过生成一组可能的目标区域,并对这些区域进行特征提取和分类。

视觉感知教案

视觉感知教案

视觉感知教案【视觉感知教案】导言:视觉是人类最重要的感觉之一,它占据了我们信息获取和认知世界的很大部分。

视觉感知教案旨在帮助学生理解视觉的基本原理和感知过程,以及如何利用视觉信息来解决问题和做出决策。

一、教学目标:1. 了解视觉感知的基本原理和感知过程。

2. 掌握常见的视觉错觉和其原因。

3. 学会利用视觉信息解决问题和做出决策。

二、教学内容:1. 视觉感知的基本原理- 光线的传播和折射- 光线通过眼睛进入视网膜- 视网膜对光线的转化和传递2. 视觉感知的感知过程- 视网膜感光细胞的激活- 神经信号的传导和处理- 视觉信息的整合和解释3. 常见的视觉错觉- 错觉的定义和分类- 空间错觉、颜色错觉和形状错觉的原因和例子4. 利用视觉信息解决问题和做出决策- 视觉搜索和目标定位- 视觉辨识和模式匹配- 视觉记忆和认知三、教学方法:1. 讲授法:通过课堂教学和多媒体演示,介绍视觉感知的基本原理和感知过程。

2. 实验法:利用视错觉实验和观察,帮助学生理解视觉的错觉现象和其原因。

3. 讨论法:组织学生分组讨论,探讨如何利用视觉信息解决问题和做出决策。

四、教学步骤:1. 引入:通过问题引入,让学生思考视觉在日常生活中的重要性和应用场景。

2. 知识讲解:介绍视觉感知的基本原理和感知过程,让学生了解视觉的基本原理和感知过程。

3. 视觉错觉实验:组织学生进行视错觉实验,观察和分析常见的视错觉现象。

4. 错觉原因解释:讲解视错觉的原因和机制,引导学生理解视错觉的产生和意义。

5. 讨论与总结:组织学生分组讨论,探讨如何利用视觉信息解决问题和做出决策,总结教学内容。

五、教学评估:1. 实验报告:要求学生完成一份视错觉实验报告,包括实验过程、观察结果和结论。

2. 小组讨论记录:要求学生记录小组讨论的思路和结论,以及个人的思考和总结。

六、教学资源:1. 多媒体演示:利用多媒体课件展示视觉感知的基本原理和感知过程。

2. 视错觉实验材料:准备一些常见的视错觉实验材料,用于教学和学生实验。

机器人视觉系统中的目标检测与路径规划

机器人视觉系统中的目标检测与路径规划

机器人视觉系统中的目标检测与路径规划机器人视觉系统在现代科技领域中扮演着重要的角色。

它不仅可以帮助机器人感知周围环境,还能为其提供目标检测和路径规划的功能。

本文将详细介绍机器人视觉系统中的目标检测与路径规划技术,并探讨其在不同领域的应用。

一、目标检测技术目标检测是机器人视觉系统中的关键环节之一。

通过目标检测技术,机器人能够识别和定位环境中的目标物体,从而为后续的路径规划和动作执行提供依据。

1.1 图像处理和特征提取目标检测的第一步是图像处理和特征提取。

机器人通过摄像头获取环境图像,并对图像进行处理,以提取目标物体的特征。

常见的图像处理技术包括灰度化、边缘检测、图像增强等。

在特征提取方面,主要采用的方法有颜色特征、纹理特征、形状特征等。

1.2 目标检测算法目标检测算法是实现目标检测的关键。

在机器学习和深度学习的发展下,目标检测算法得到了极大的改进和拓展。

其中,常见的目标检测算法包括传统的Haar特征级联检测算法、基于特征的卷积神经网络(CNN)算法、基于区域的卷积神经网络(R-CNN)算法等。

这些算法能够在图像中准确地检测出目标物体,并给出其位置和边界框。

1.3 实时目标检测在机器人的视觉系统中,实时性是非常重要的考虑因素。

实时目标检测能够在较短的时间内完成目标检测任务,并输出结果。

为了实现实时目标检测,需要结合高效的算法和硬件加速等技术手段。

同时,还需要优化目标检测算法的计算速度和精度,以满足机器人快速响应和决策的需求。

二、路径规划技术路径规划是机器人视觉系统中的另一个重要环节。

它决定了机器人在环境中行动的路径,并将目标检测结果与路径规划相结合,实现机器人的智能导航。

2.1 环境建模在路径规划之前,需要对环境进行建模。

机器人通过激光雷达或摄像头等传感器获取环境信息,并将其转化为机器人可识别的地图或模型。

这些模型包括栅格地图、图像地图、三维点云等,以提供给路径规划算法使用。

2.2 路径规划算法路径规划算法是决定机器人行动路径的核心。

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Faster target selection in preview visual search depends on luminance onsets:behavioral and electrophysiological evidenceMonika Kiss &Martin EimerPublished online:30June 2011#Psychonomic Society ,Inc.2011Abstract T o investigate how target detection in visual search is modulated when a subset of distractors is presented in advance (preview search),we measured search performance and the N2pc component as an electrophysi-ological marker of attentional target selection.T argets defined by a color/shape conjunction were detected faster and the N2pc emerged earlier in preview search relative to a condition in which all items were presented simultaneously .Behavioral and electrophysiological preview benefits dis-appeared when stimuli were equiluminant with their background,in spite of the fact that targets were feature singletons among the new items in preview search.The results demonstrate that previewing distractors expedites the spatial selection of targets at early sensory-perceptual stages,and that these preview benefits depend on rapid attentional capture by luminance onsets.Keywords Visual search .Visual marking .Luminance onset .ERPs .N2pcIn visual search,the processing of new objects can be prioritized over processing of objects that have been visible for some time.When a subset of distractors is presented in advance and the remaining items are then added to the search display ,search performance is more efficient than when all items are presented simultaneously .According to W atson and Humphreys (1997),this preview benefit reflectsthe active inhibition of distractor locations during the preview interval (“visual marking ”).In line with this hypothesis,such benefits only emerge when the preview interval is sufficiently long (400ms or longer);they are reduced by a secondary task during the preview interval;and they are associated with impaired probe detection performance at previewed distractor locations (W atson &Humphreys,2000)and at empty locations between pre-viewed distractors (Osugi,Kumada,&Kawahara,2009).The hypothesis that preview benefits in visual search are due to location-based inhibition has not gone unchallenged (see Donk,2006,for a review).It has been suggested that feature-based inhibition can also play a role (Olivers &Humphreys,2002;but see Theeuwes,Kramer,&Atchley,1998).Others have argued that the prioritiza-tion of new objects in visual search does not involve top-down inhibition at all.According to Jiang,Chun,and Marks (2002),temporal asynchrony between old and new items is critical,and preview benefits emerge when attention can be selectively allocated to a temporally segregated perceptual group that contains the target.Donk and Theeuwes (2001)claimed that new objects are prioritized when their appearance is associated with an abrupt luminance change,which will trigger automatic attentional capture.In support of their claim,they demonstrated that search performance is not affected by the number of old items when new stimuli are accompa-nied by a luminance onset,but is determined by the numbers of both new and old items when new stimuli are equiluminant with the background.In previous behavioral studies,preview-induced improvements of search efficiency have been inferred from search slope differences between preview and standard visual search (W atson &Humphreys,1997)or from a reduced impact of the number of old items on searchM.Kiss (*):M.EimerDepartment of Psychology ,Birkbeck College,University of London,Malet Street,London WC1E 7HX,UK e-mail:m.kiss@Atten Percept Psychophys (2011)73:1637–1642DOI 10.3758/s13414-011-0165-zperformance(Donk&Theeuwes,2001).While these observations suggest that previewing distractors facilitates target processing,it is difficult to gain precise insights into the locus and time course of preview effects on attentional selectivity from behavioral measures alone.In the present study,event-related brain potential(ERP) markers of selective attentional processing were mea-sured to find out how previewing distractors affects the onset of spatially specific target selection in extrastriate visual areas and whether such preview benefits depend on luminance changes.In contrast to a previous ERP study of visual marking(Jacobsen,Humphreys,Schröger, &Roeber,2002)that focused on ERP components that followed preview displays,we measured the N2pc component to target arrays.The N2pc is an enhanced negativity at posterior scalp sites contralateral to the location of a target with an onset latency of about 200ms;this negativity is triggered during the spatial selection of targets among distractors(Eimer,1996;Luck &Hillyard,1994)and has recently been used to study the time course of attentional capture in visual search(e.g., Eimer&Kiss,2008;Hickey,McDonald,&Theeuwes, 2006).If previewing distractors expedites the onset of spatially selective target processing,the N2pc should emerge earlier on preview trials than on trials in whichall stimuli in a search array are presented simultaneously. If such preview benefits occur only for luminance-onset stimuli(Donk&Theeuwes,2001),preview-induced modulations of N2pc onset should disappear for equilu-minant stimuli.As in W atson and Humphreys(1997),targets were defined by a conjunction of color and shape(e.g.,a blue diamond among blue circles and green diamonds).T o avoid sensory asymmetries in the visual ERPs,circular search displays were used(Fig.1).In the preview condition,four nontarget-color distractors were presented for1,000ms,before four target-color items(including the target,when present)were added to the display.Among the new items,the target was a shape singleton.Preview benefits were assessed relative to a full-element baseline condition,in which all eight items were presented simultaneously.In the half-element baseline condition, only the four target-color items were presented,so that targets were always shape singletons.These three search conditions were delivered in separate blocks.Participants had to report the presence or absence of a target,and a target was present in two thirds of all trials.In one half of the experiment,stimuli were presented against a black background.In the other half,they were equiluminant with a gray background.Preview benefits on RT and N2pc onset measures were quantified by comparing the preview and the full-element baseline conditions.T o determine whether previewed distractors can be completely ignored,the preview condition was contrasted with the half-element baseline condition.MethodParticipantsA group of31paid volunteers participated in this experiment.Four were excluded because of excessive alpha activity,and3others because of poor eye movement control,resulting in the rejection of more than50%of all trials.All of the remaining24participants(11male,13 female;mean age25.9years)had normal or corrected-to-normal vision.Stimuli and procedureStimuli were presented on a CRT monitor(refresh rate: 100Hz)at a viewing distance of80cm.On each trial,a circular search array was displayed at a radial angular distance of3.9ºfrom a central fixation cross.There were three blocked search conditions(Fig.1).In the full-element condition,targets were defined by a color/shape conjunc-tion and were presented simultaneously with seven dis-tractors that shared either the target color(blue or green)or its shape(diamond or circle).T arget color and target shape were counterbalanced independently across participants.200 msA.B.C.Fig.1Examples of search displays in full-element(A),preview(B), and half-element(C)blocks in which the target was a blue diamond. Green items are depicted as gray and blue items as whiteEach search array consisted of eight shapes,half of which were green (CIE x/y chromaticity coordinates:.296/.567),and the other half blue (CIE x/y:.185/.188).In the preview condition,four identical nontarget-color distractors were presented for 1,000ms,before four additional target-colored items were added to thedisplay.Among these new items,the target,when present,was a shape singleton.In the half-element condition,the displays contained the same four stimuli that had appeared as new items in the preview condition,but were not preceded by a preview display.In all displays,equal numbers of items in a given color (green or blue)were presented in the left and right visual fields.The blue and green search items were equiluminant (10.1cd/m 2),and their angular size was 0.72°×0.72°.Search arrays were presented for 200ms in the full-element and half-element conditions.In the preview condition,the display containing both old and new items was presented for 200ms.The intertrial interval was 1,800ms.A target was present on two thirds of all trials and appeared randomly and equiprobably at one of the eight locations in the left or the right hemifield.Participants were instructed to report the presence or absence of a target as quickly and accurately as possible,by pressing one of two vertically arranged keys with their left or right index finger.Mappings of response categories to keys,and of keys to response hands,were counterbalanced within participants.The experiment included 36blocks with 36trials per block.In 18successively presented blocks,stimuli were presented against a black background,so that the appear-ance of new items was accompanied by a luminance change (onset condition).In the other 18blocks,a light gray background (CIE x/y:.286/.312)matched the luminance of the foreground stimuli,so that their presentation was not accompanied by a luminance change (equiluminance condition).A subgroup of 12participants completed the onset condition before starting the equiluminance condi-tion,and this order was reversed for the other 12participants.The three search conditions were always presented in six successive blocks,with order of the search conditions counterbalanced across participants.EEG data recording and analysisThe Electroencephalogram (EEG)activity was recorded using a BrainAmp amplifier (Brain Products GmbH)from 23scalp electrodes mounted in an elastic cap at standard positions of the extended International 10/20system.Data were sampled at 500Hz with a bandpass filter of 0–40Hz.The horizontal electrooculogram (HEOG)was recorded from two electrodes positioned at the outer canthi of the eyes.All electrodes were referenced online against the leftearlobe,and re-referenced offline to the averaged ear-lobes.The EEG was segmented from 100ms before search array onset to 400ms poststimulus.Trials with saccades (HEOG exceeding ±25μV),blinks (FPz exceeding ±60μV),or muscle artifacts (all other electrodes exceeding ±80μV)were eliminated from analyses.Trials with RT s faster than 200ms or slower than 1,500ms were also excluded from all analyses (less than 0.1%of all trials).Average waveforms were computed for target-present trials,separately for each of the six possible combinations of search and luminance condition,and for targets in the left and right hemifields.The N2pc component was measured at lateral posterior electrode sites PO7/PO8contralateral and ipsilateral to the target location.N2pc onset latencies were determined with a jackknife-based procedure from difference waveforms obtained by subtracting ipsilateral from contralateral ERPs.This procedure estimates onset latencies from grand averages computed for subsamples of participants obtained by successively excluding 1participant from the original sample (Miller,Patterson,&Ulrich,1998).N2pc onset was defined as the time point when the voltage on the ascending flank of the N2pc difference wave exceeded 40%of the peak amplitude.Repeated measures ANOV As were con-ducted on N2pc onset latencies for the factors Luminance (onset,equiluminance)and Search Condition (full-element,preview ,half-element),followed by comparisons between paired search conditions.F and t values were corrected (denoted with F c and t c ),as described by Miller et al.(1998)and Ulrich and Miller (2001).R T (m s )400450500550600650Preview Full-element Half-element Preview Full-element Half-elementOnsetEquiluminanceFig.2Response times (RT s)for target-present and target-absent trials in the onset and equiluminance conditions,shown separately for the full-element,preview ,and half-element conditions.Error bars repre-sent standard errors of the meansResultsBehavioral performanceFigure2shows mean correct RT s for target-present trials (black circles)and target-absent trials(open circles)with luminance-onset and equiluminant stimuli,for the full-element,preview,and half-element conditions.T arget-present RT s were faster in the preview than in the full-element condition,F(1,23)=24.58,p<.001.Critically,this preview benefit was strongly reduced when search items were equiluminant with their background,as reflected by a Search Condition x Luminance interaction,F(1,23)=16.91, p<.001.Follow-up analyses revealed the presence of a reliable preview benefit in the onset condition,with faster R T s to targets in preview trials relative to the full-element baseline(461vs.524ms),t(23)=8.44,p<.001.No reliable preview benefit was found in the equiluminance condition (526vs.543ms),t(23)=1.50,p=.146.Similarly,a main effect of search condition,F(1,23)=39.18,p<.001,and a Search Condition×Luminance interaction,F(1,23)=7.47, p<.05,were observed for target-absent responses.The preview benefits observed for luminance-onset stimuli,t(23)= 10.15,p<.001,were reduced in size,albeit still reliable,for equiluminant stimuli,t(23)=2.39,p<.05.Target-present RTs were faster in the half-element condition than in the preview condition,F(1,23)=7.97, p<.05,but there was no Search Condition x Luminance interaction,F(1,23)=1.86,p=.186.Similarly,faster target-absent responses were found for the half-element relative to the preview condition,F(1,23)=7.6,p<.05, without any Search Condition x Luminance interaction, F(1,23)=1.09,p=.307.1Errors(missed targets and false alarms on target-absent trials)were rare(1.88%and2.13%,respectively,for onsetand equiluminant stimuli).Errors tended to be more frequent in the full-element condition(2.57%)than in the preview(1.68%)and half-element(1.77%)conditions.N2pc componentFigure3shows grand-average ERPs for target-present trials at lateral posterior electrodes PO7/PO8contralateral and ipsilateral to the target location,separately for luminance-onset and equiluminant stimuli,and for all three search conditions.N2pc components were triggered between200and350ms after search array onset in all conditions.N2pc differences between search conditions can be seen more easily in the difference waveforms shown in Fig.4,which were obtained by subtracting ERP activity at PO7/PO8ipsilateral to the target location from contralateral activity.N2pc preview benefits were quantified by comparing N2pc onset latencies(defined by the40%peak amplitude criterion) in the preview and full-element baseline conditions.No overall significant N2pc onset difference was found between these two search conditions,F c(1,23)= 3.11,p=.09. Critically,there was a Search Condition x Luminance interaction,F c(1,23)=4.81,p<.05,demonstrating that preview benefits on N2pc latency were modulated by the difference between luminance-onset and equiluminant stim-uli(see Fig.4).For luminance-onset stimuli,a reliable preview benefit on N2pc onset latencies was observed(216 vs.235ms,respectively,for the preview vs.full-element baseline conditions),t c(23)=2.49,p<.05.No such effect was present for equiluminant stimuli(255vs.256ms),t c<1.1As expected,target-present responses were faster than target-absent responses,and R T s to luminance-onset stimuli were faster than R T s to equiluminant stimuli(see Fig.2).Additional analyses confirmed that these differences were significant,both F s(1,23)>16.8,both p s<.001.OnsetFull-elementPreviewContralateralIpsilateralHalf-elementEquiluminanceFig.3Grand-average ERPs obtained on target-present trials at lateral posterior electrodes PO7/PO8contralateral(dashed lines)and ipsilat-eral(solid lines)to the target location in the onset(left panels)and equiluminance(right panels)conditions.ERPs are shown separately for the full-element(top panels),preview(middle panels),and half-element(bottom panels)conditionsEssentially the same pattern of preview benefits was obtained when the N2pc onset latency was defined on the basis of an absolute amplitude criterion(−0.7μV).A comparison of N2pc onset latencies between the preview and half-element conditions revealed no reliable difference with luminance-onset stimuli(214vs.216ms),t c<1.For equiluminant stimuli,the N2pc in the half-element condition started earlier relative to the preview condition(238vs. 256ms),t c(23)=2.22,p<.05.In spite of this difference,the overall Luminance×Search Condition interaction was not significant,F c(1,23)=2.90,p=.102.2Figure4shows that the N2pc was more sharply tuned,and therefore returned to baseline faster,in the half-element condition than in the preview search condition,for both luminance onsets and equiluminant stimuli.This was confirmed by N2pc offset latency analyses(with offset defined as the time point when the voltage on the descending flank of the N2pc fell below40%of peak amplitude).N2pc offset was earlier in the half-element condition relative to the preview condition for onset stimuli(274vs.300ms), t c(23)=7.37,p<.001,and for equiluminant stimuli(314 vs.329ms),t c(23)=2.63,p<.05.DiscussionWith luminance-onset stimuli,previewing distractors expedit-ed attentional target selection.T arget-present R T s were faster in the preview than in the full-element condition,and this behavioral preview benefit was accompanied by a corresponding N2pc onset difference.Because the N2pc component reflects the spatially selective processing of visual search targets in extrastriate visual cortex,preview-induced N2pc onset modulations demonstrate that preview benefits are linked to the speed of attentional target selection at early sensory–perceptual stages.There were only small R T and no N2pc latency differences between the preview and half-element conditions,indicating that previewed distractors were effectively ignored,making preview search comparable to search for a shape singleton(but see below).V ery different results were found for equiluminant stimuli, suggesting that preview effects critically depend on luminance changes.The absence of behavioral preview benefits for equiluminant stimuli confirms previous findings by Donk and Theeuwes(2001).Importantly,the lack of R T preview effectswas mirrored by the absence of N2pc onset differencesbetween the preview and full-element conditions,indicatingthat previewing distractors did not speed up attentional targetselection in the absence of luminance changes.3These findings provide no evidence for location-basedinhibition(W atson&Humphreys,1997),which shouldhave been similarly effective for luminance-onset andequiluminant stimuli.However,Braithwaite,Hulleman,W atson,and Humphreys(2006)found preview benefits forequiluminant stimuli with longer preview intervals,sug-gesting that inhibition may develop more slowly for thesestimuli.Because the present1,000-ms preview intervalmay have been too brief for preview benefits to emergewith equiluminant stimuli,we ran a behavioral follow-upexperiment in which a3,000-ms preview condition(as inBraithwaite et al.,2006)was compared to a full-elementbaseline condition for equiluminant stimuli.No reliableRT differences were found between these two conditionsfor target-present responses(584vs.563ms),t<1,ortarget-absent responses(615vs.630ms),t(7)=1.06,p=.326,demonstrating that at least with the present stimulus design,behavioral preview benefits for equiluminant stimuli remainabsent,even with long preview intervals.The present findings are not in line with a pure feature-based inhibition account(Olivers&Humphreys,2002):Previewed and new items differed in color in bothluminance conditions,but observers could restrict theirsearch to new items only in the onset condition.Becausethe temporal structures of the preview trials were identicalin both luminance conditions,the temporal segregationhypothesis(Jiang et al.,2002)also cannot account for the 2The N2pc emerged earlier in response to search arrays that wereassociated with a luminance onset than for search arrays that were equiluminant with their background.A follow-up analysis using the 40%peak amplitude criterion confirmed that this general luminance-related onset difference was reliable(222vs.249ms),F c(1,23)= 35.62,p<.001.3The absence of preview benefits in the equiluminance condition, which is consistent with previous studies in which stimuli were perceptually equiluminant(e.g.,Donk&Theeuwes,2001),also confirms that the photometrically determined equiluminance was close to perceptual equiluminance.Full-elementPreviewHalf-elementOnset EquiluminanceFig.4Grand-average difference waveforms obtained by subtracting ERPs ipsilateral to the target location from contralateral ERPs in the onset and equiluminance conditions,shown separately for the full-element(dashed black lines),preview(solid black lines),and half-element(gray lines)conditionspresent findings.They are,however,fully in line with the hypothesis that new items are prioritized in visual search only when they are associated with an abrupt luminance change,and therefore capture attention(Donk& Theeuwes,2001).Although attentional capture provides a sufficient explanation for the preview benefits observed in this study,mechanisms such as location-based inhibi-tion may still play an important role when the stimulus configuration or task demands of a visual search task are different.For example,preview benefits for equiluminant stimuli might emerge in tasks in which visual search targets are harder to find or in which larger set sizes are used(cf.Braithwaite et al.,2006).Another notable finding was that relative to the preview condition,the N2pc was more narrowly tuned both for onset and equiluminant stimuli in the half-element condition,in which search arrays contained only four stimuli,resulting in an earlier N2pc offset(Fig.4).The apparent reduced temporal variability of target selection in this condition could reflect the absence of attentional filtering costs(Kahneman,Treisman,& Burkell,1983)that are produced when previewed distractors remain present in target search displays.In line with this hypothesis,RT s were indeed faster in the half-element condition than during preview search.Overall,the present findings demonstrate that preview-ing distractors results in faster spatially selective target processing in extrastriate visual cortex,but only when target displays are accompanied by luminance onsets. When such luminance onsets are not present,distractor preview has no impact on the speed of target selection,even when the target is a feature singleton among new items. These results underline the critical role of dynamic changes in the visual environment for attentional selectivity. Author Note This work was supported by a grant from the Biotech-nology and Biological Sciences Research Council(BBSRC),U.K.ReferencesBraithwaite,J.J.,Hulleman,J.,W atson,D.G.,&Humphreys,G.W.(2006).Is it impossible to inhibit isoluminant items,or does itsimply take longer?Evidence from preview search.Perception& Psychophysics,68,290–300.Donk,M.(2006).The preview benefit:V isual marking,feature-based inhibition,temporal segregation,or onset capture?V isual Cognition, 14,736–748.Donk,M.,&Theeuwes,J.(2001).V isual marking beside the mark: Prioritizing selection by abrupt onsets.Perception&Psychophysics, 63,891–900.Eimer,M.(1996).The N2pc component as an indicator of attentional selectivity.Electroencephalography and Clinical Neurophysiology, 99,225–234.Eimer,M.,&Kiss,M.(2008).Involuntary attentional capture is determined by task set:Evidence from event-related brain potentials.Journal of Cognitive Neuroscience,20,1423–1433. Hickey,C.,McDonald,J.J.,&Theeuwes,J.(2006).Electrophysio-logical evidence of the capture of visual attention.Journal of Cognitive Neuroscience,18,604–613.Jacobsen,T.,Humphreys,G.W.,Schröger,E.,&Roeber,U.(2002).Visual marking for search:Behavioral and event-related potential analyses.Cognitive Brain Research,14,410–421.Jiang,Y.,Chun,M.M.,&Marks,L.E.(2002).Visual marking: Selective attention to asynchronous temporal groups.Journal of Experimental Psychology.Human Perception and Performance, 28,717–730.Kahneman,D.,Treisman,A.,&Burkell,J.(1983).The cost of visual filtering.Journal of Experimental Psychology.Human Perception and Performance,9,510–522.Luck,S.J.,&Hillyard,S.A.(1994).Spatial filtering during visual search:Evidence from human electrophysiology.Journal of Experimental Psychology.Human Perception and Performance, 20,1000–1014.Miller,J.,Patterson,T.,&Ulrich,R.(1998).Jackknife-based method for measuring LRP onset latency differences.Psychophysiology, 35,99–115.Olivers,C.N.L.,&Humphreys,G.W.(2002).When visual marking meets the attentional blink:More evidence for top-down,limited-capacity inhibition.Journal of Experimental Psychology.Human Perception and Performance,28,22–42.Osugi,T.,Kumada,T.,&Kawahara,J.(2009).The spatial distribution of inhibition in preview search.V ision Research,49,851–861. Theeuwes,J.,Kramer,A.F.,&Atchley,P.(1998).Visual marking of old objects.Psychonomic Bulletin&Review,5,130–134. Ulrich,R.,&Miller,J.(2001).Using the jackknife-based scoring method for measuring LRP onset effects in factorial designs.Psychophysiology,38,816–827.W atson, D.G.,&Humphreys,G.H.(1997).Visual marking: Prioritizing selection for new objects by top-down attentional inhibition of old objects.Psychological Review,104,90–122. W atson,D.G.,&Humphreys,G.H.(2000).Visual marking:Evidence for inhibition using a probe-dot detection paradigm.Perception& Psychophysics,62,471–481.。

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