The representational similarity between visual perception and recent perceptual history

感知 刺激(心理学) 失配负性 心理学 视觉感受 英语 可视对象 认知心理学 沟通 脑电图 虚假关系 填写 感觉记忆 跟踪(心理语言学) 神经科学 计算机科学 人工智能 机器学习 哲学 语言学
作者
Junlian Luo,Thérèse Collins
出处
期刊:The Journal of Neuroscience [Society for Neuroscience]
卷期号:: JN-22 被引量:10
标识
DOI:10.1523/jneurosci.2068-22.2023
摘要

From moment to moment, the visual properties of objects in the world fluctuate due to external factors like ambient lighting, occlusion and eye movements, and internal (proximal) noise. Despite this variability in the incoming information, our perception is stable. Serial dependence, the behavioral attraction of current perceptual responses towards previously seen stimuli, may reveal a mechanism underlying stability: a spatio-temporally tuned operator that smoothes over spurious fluctuations. The current study examined the neural underpinnings of serial dependence by recording the electroencephalographic (EEG) brain response of female and male human observers to prototypical objects (faces, cars and houses) and morphs that mixed properties of two prototypes. Behavior was biased towards previously seen objects. Representational similarity analysis revealed that responses evoked by visual objects contained information about the previous stimulus. The trace of previous representations in the response to the current object occurred immediately upon object appearance, suggesting that serial dependence arises from a brain state or set that precedes processing of new input. However, the brain response to current visual objects was not representationally similar to the trace they leave on subsequent object representations. These results reveal that while past stimulus history influences current representations, this influence does not imply a shared neural code between the previous trial (memory) and the current trial (perception). Significance statement The perception of visual objects is pulled towards instances of that object seen in the recent past. The neural underpinnings of this serial dependence remain to be fully investigated. The present study examined EEG responses to faces, cars and houses, and ambiguous between-category morphs. With representational similarity analysis, we showed (1) object-specific neural patterns that differentiate the three categories; (2) that the response to the current object contains information about the previous object, mirroring behavioral serial dependence; (3) that the object-specific neural pattern about the past was different from that in the current response, revealing that while past stimulus history influences current representations, this does not imply a shared neural code between the previous trial (memory) and the current trial (perception).

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
艾查恩发布了新的文献求助10
1秒前
2秒前
2秒前
淡然如风完成签到 ,获得积分10
4秒前
liaoyoujiao完成签到,获得积分10
4秒前
曦柚完成签到 ,获得积分10
5秒前
orixero应助tutoutou采纳,获得10
5秒前
机灵柚子发布了新的文献求助30
5秒前
7秒前
西高所完成签到,获得积分10
7秒前
9527应助聪慧的以菱采纳,获得10
8秒前
9秒前
mouxq发布了新的文献求助10
10秒前
10秒前
Hyp完成签到 ,获得积分10
10秒前
10秒前
11秒前
11秒前
13秒前
13秒前
14秒前
lxy发布了新的文献求助10
14秒前
17秒前
17秒前
ipomoea97发布了新的文献求助10
18秒前
今后应助沄霄之上采纳,获得10
18秒前
18秒前
可不可以完成签到 ,获得积分10
19秒前
活在当下发布了新的文献求助10
19秒前
22秒前
22秒前
七七发布了新的文献求助10
22秒前
23秒前
ddli发布了新的文献求助10
23秒前
林枫发布了新的文献求助10
25秒前
害羞的不尤完成签到,获得积分10
25秒前
樱木花雪发布了新的文献求助10
26秒前
26秒前
黎明前完成签到,获得积分10
27秒前
老老熊完成签到,获得积分10
27秒前
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
APA handbook of humanistic and existential psychology: Clinical and social applications (Vol. 2) 2000
Cronologia da história de Macau 1600
Handbook on Climate Mobility 1111
Current concept for improving treatment of prostate cancer based on combination of LH-RH agonists with other agents 1000
Research Handbook on the Law of the Sea 1000
Contemporary Debates in Epistemology (3rd Edition) 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 计算机科学 化学工程 生物化学 物理 复合材料 内科学 催化作用 物理化学 光电子学 细胞生物学 基因 电极 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6174358
求助须知:如何正确求助?哪些是违规求助? 8001718
关于积分的说明 16642624
捐赠科研通 5277447
什么是DOI,文献DOI怎么找? 2814679
邀请新用户注册赠送积分活动 1794348
关于科研通互助平台的介绍 1660085