EEG Searchlight Decoding Reveals Person- and Place-specific Responses for Semantic Category and Familiarity

心理学 认知心理学 名词 语义记忆 人工智能 计算机科学 认知 神经科学
作者
Andrea Bruera,Massimo Poesio
出处
期刊:Journal of Cognitive Neuroscience [MIT Press]
卷期号:: 1-20 被引量:2
标识
DOI:10.1162/jocn_a_02125
摘要

Abstract Proper names are linguistic expressions referring to unique entities, such as individual people or places. This sets them apart from other words like common nouns, which refer to generic concepts. And yet, despite both being individual entities, one's closest friend and one's favorite city are intuitively associated with very different pieces of knowledge—face, voice, social relationship, autobiographical experiences for the former, and mostly visual and spatial information for the latter. Neuroimaging research has revealed the existence of both domain-general and domain-specific brain correlates of semantic processing of individual entities; however, it remains unclear how such commonalities and similarities operate over a fine-grained temporal scale. In this work, we tackle this question using EEG and multivariate (time-resolved and searchlight) decoding analyses. We look at when and where we can accurately decode the semantic category of a proper name and whether we can find person- or place-specific effects of familiarity, which is a modality-independent dimension and therefore avoids sensorimotor differences inherent among the two categories. Semantic category can be decoded in a time window and with spatial localization typically associated with lexical semantic processing. Regarding familiarity, our results reveal that it is easier to distinguish patterns of familiarity-related evoked activity for people, as opposed to places, in both early and late time windows. Second, we discover that within the early responses, both domain-general (left posterior-lateral) and domain-specific (right fronto-temporal, only for people) neural patterns can be individuated, suggesting the existence of person-specific processes.
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
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
刚刚
小柚子发布了新的文献求助10
刚刚
称心不尤完成签到 ,获得积分10
2秒前
yyy完成签到,获得积分10
3秒前
妩媚的书易完成签到 ,获得积分10
6秒前
6秒前
SciGPT应助陶治采纳,获得10
6秒前
所所应助虚心的夏青采纳,获得10
6秒前
奎奎发布了新的文献求助10
7秒前
烟花应助粗暴的君浩采纳,获得30
7秒前
8秒前
淡写完成签到,获得积分10
8秒前
8秒前
二萌完成签到,获得积分10
9秒前
小二郎应助Arthur采纳,获得10
9秒前
10秒前
小倪完成签到 ,获得积分10
11秒前
DDy10001发布了新的文献求助10
11秒前
11秒前
搜集达人应助Melody采纳,获得10
11秒前
xzy998发布了新的文献求助10
12秒前
wangweiwei完成签到,获得积分10
13秒前
14秒前
如意千万发布了新的文献求助10
14秒前
14秒前
14秒前
打工人发布了新的文献求助10
15秒前
16秒前
16秒前
一只虎斑猫完成签到,获得积分10
17秒前
18秒前
负责怡发布了新的文献求助10
18秒前
18秒前
18秒前
18秒前
liu123发布了新的文献求助10
19秒前
19秒前
小泌完成签到,获得积分10
19秒前
桐桐应助科研兄采纳,获得10
20秒前
21秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Foreign Policy of the French Second Empire: A Bibliography 500
Chen Hansheng: China’s Last Romantic Revolutionary 500
Classics in Total Synthesis IV 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3145621
求助须知:如何正确求助?哪些是违规求助? 2797097
关于积分的说明 7822848
捐赠科研通 2453435
什么是DOI,文献DOI怎么找? 1305652
科研通“疑难数据库(出版商)”最低求助积分说明 627514
版权声明 601469